Chemosensors doi: 10.3390/chemosensors14110250

Authors: Sérgio Williams Ferreira de Sousa Luis Felipe Lima Guimarães Gessica Michely da Conceicao Silva Tatiana Sainara Maia Fernandes Joaquim Rodrigues de Vasconcelos Neto Deboha Viegas A. de A. dos Santos Ronaldo Ferreira do Nascimento Daniel Barbosa Alcântara

Organochlorine pesticide (OCP) residues act as critical drivers of environmental impacts and pose significant risks to human and animal health. This scenario is further exacerbated by their high environmental persistence, marked bioaccumulative potential, and chronic toxicity. The International Agency for Research on Cancer (IARC) classifies dieldrin, as well as aldrin when metabolized into this substance, as probably carcinogenic to humans (Group 2A). OCPs can accumulate in the fatty tissue of fish. Therefore, when consumed, fish can become a route of human exposure to OCPs, such as dieldrin. A novel method for determining dieldrin in Fillets of Nile tilapia (Oreochromis niloticus) using GC/MS and the original QuEChERS was developed and validated. The optimization of chromatographic parameters resulted in an appropriate retention time (Rt) for dieldrin. Matrix-matched calibration was established to correct the matrix effect (ME) in the quantitative analysis of the pesticide. Dieldrin showed matrix-induced signal suppression of -21.17%. The pesticide studied showed good linearity with a coefficient of determination (R2) of 0.9918. According to the analytical validation standards established by ANVISA, concentrations above the LOQ (50 μg L−1) can be used as a linear working range with high reliability.

Chemosensors doi: 10.3390/chemosensors14110249

Authors: Omobolanle A. Omoteso Handsome Ndlovu Sandile M. Khamanga Roderick B. Walker

Palatability has an impact on pediatric medication adherence, yet the influence of water dilution, PBS volume fraction, and salivary pH on the sensory performance of formulations is poorly characterized. This study provides the first systematic, biorelevant evaluation of how water dilution factor, PBS volume fraction, associated ionic strength change, and physiologically relevant pH affect the palatability of three commercially available hyoscine butylbromide (HBB) syrup brands (X, Y, and Z), extending earlier characterization of undiluted formulations. Samples were evaluated using an Alpha Astree II electronic tongue (ChemFET array. Water dilution (Batch B) and PBS volume fractions of 2 mL or 5 mL at pH 6.2, 6.8, and 7.4 within a fixed total measurement volume of 25 mL simulated salivary pH and ionic composition across the physiological range relevant to pediatric and adult populations. Pure HBB API solutions were tested in parallel. Principal component analysis (>85% cumulative variance) confirmed robust discrimination across all conditions. The water dilution factor increased bitterness for all brands by disrupting excipient matrix integrity, with cellulosic polymer-containing Brands X (HEC) and Y (HPMC) retaining superior taste masking over polymer-free Brand Z. The PBS volume fraction revealed pH-dependent, brand-specific palatability responses, at pH 6.2, 6.8, and 7.4, and the hierarchy was consistently Y > X > Z, confirming the superiority of the Brand Y formulation, combining HPMC with sodium cyclamate and saccharin sodium, across the full physiological salivary pH range, noting that this superiority reflects the broader excipient composition of Brand Y, rather than polymer choice alone. Notably, Brand X exhibited substantially higher ANS sweetness scores than Brand Y at 2 mL pH 7.4 PBS, reflecting differential sweetener system sensitivity to near-neutral PBS ionic conditions. Increasing the PBS volume fraction from 2 mL to 5 mL within a fixed total measurement volume of 25 mL, increased the ionic strength and pH contribution of the simulated salivary component, amplified bitterness and attenuated sourness across all brands, reflecting changes in the ionic environment at the ChemFET sensor membrane, rather than any increase in the intrinsic bitterness of HBB itself. Taste-masking strategies that rely solely on sweeteners, without viscosity-building polymers, are inadequate under biorelevant oral conditions. pH-stratified assessment is essential for comprehensive characterization of palatability in pediatric oral liquid formulations.

Chemosensors doi: 10.3390/chemosensors14110248

Authors: Mustafa Demirtaş

Nanopipette-based single-molecule sensing offers a label-free route to protein analysis, but distinguishing structurally different proteins from ionic current traces remains difficult. Here, we classified three clinically important proteins (CCL5, MMP1 and TIMP1) from their nanopipette translocation signals. Twelve biophysical features were extracted from a balanced subset of 846 events (282 per protein) drawn from 1317 recorded events (one experiment per protein). Random Forest, SVM and gradient boosting were compared by five-fold cross-validation; gradient boosting performed best (accuracy 92.1 ± 2.0%, macro ROC-AUC 0.982). Absolute current drop and relative blockage (ΔI/I0) were the most important features (about 37% of importance), and PCA and clustering separated TIMP1 but not CCL5 and MMP1. However, with a histogram-based gradient boosting classifier on a pool of 28 experiments (7095 events), balanced accuracy fell from 90.0% under event-level cross-validation to 73.5% when whole experiments recorded with other nanopipettes were held out (mean CCL5 recall 30%), and to 47–63% without baseline-related features, whereas open-pore current alone gave 92.0% and 82.6%, indicating pipette-specific contributions. These proof-of-concept findings indicate that, within the present dataset, nanopipette signals can distinguish the three proteins, whereas generalization to independent nanopipettes, particularly for CCL5, remains to be demonstrated.

Chemosensors doi: 10.3390/chemosensors14110247

Authors: Ke Tang Fanfan Li Zong Xu Huojiao Sun Dandan Liu Xing Zhao

Optical acquisition geometry determines the tissue regions represented in visible–near-infrared (Vis–NIR) spectra, yet its interactions with spectral preprocessing and multimodal fusion remain insufficiently understood for intact citrus fruit. This study systematically compared diffuse reflectance (DR), diffuse transmittance (DT), and transmittance (T) for the nondestructive prediction of soluble solids content (SSC) in Shatangju mandarins. For each optical mode, 180 modeling pipelines combining six preprocessing methods, five wavelength-selection strategies, and six regression algorithms were evaluated using repeated nested cross-validation. Three multimodal fusion strategies were subsequently investigated: direct feature concatenation, sequential orthogonalized partial least squares (SO-PLS), and stacked generalization (Stacking). Marked differences in predictive performance were observed among the three optical modes. T achieved the strongest single-mode performance, followed by DT, whereas DR performed substantially worse, with mean coefficient of determination (R2) values of 0.832, 0.815, and 0.448 and corresponding root mean square errors (RMSEs) of 0.808, 0.849, and 1.463 °Brix, respectively. Preprocessing effectiveness was strongly mode-dependent: Savitzky–Golay smoothing provided the greatest improvement for DT, whereas preprocessing yielded only limited gains for DR. Wavelength selection did not consistently improve prediction over the full valid wavelength range, although competitive adaptive reweighted sampling substantially reduced spectral dimensionality in some pipelines. Neither direct feature concatenation nor SO-PLS surpassed the best single-mode model. In contrast, prediction-level Stacking consistently improved predictive performance. Stacking-6, combining the two highest-ranked base models from each optical mode, achieved the best overall results, with a mean R2 of 0.861, RMSE of 0.735 °Brix, mean absolute error of 0.576 °Brix, concordance correlation coefficient of 0.924, residual predictive deviation of 2.700, and ratio of performance to interquartile range of 3.931. Relative to the best T-mode model, Stacking-6 reduced mean RMSE by approximately 9.0% and achieved lower RMSE in all 10 repetitions. These findings demonstrate that acquisition geometry governs not only single-mode predictive capability but also the effectiveness of preprocessing and multimodal integration. Under the conditions evaluated, transmittance provided the most informative single-mode spectra, whereas prediction-level stacking more effectively exploited complementary information across optical modes than feature-level concatenation or SO-PLS fusion.

Chemosensors doi: 10.3390/chemosensors14110246

Authors: Shimaa A. Atty Shereen M. Azab Asmaa H. Ibrahim Fatma A. Fouad Mahmoud A. Tantawy Suliman A. Almahmoud Mona A. Abdel Rahman

This work reports a novel, ultra-sensitive electroanalytical sensor based on a carbon paste electrode modified with multi-walled carbon nanotubes, cesium chloride, and sodium dodecyl sulfate (CPE/MWCNT-CsCl/SDS) for the determination of the antihistamine levocabastine (LC). The structural, morphological, and surface properties of the nanocomposite and modified electrode were thoroughly characterized. Cyclic voltammetry (CV) and square wave voltammetry (SWV) showed that combining MWCNTs and CsCl markedly enhanced the peak current by providing a porous, high-surface-area network, while the addition of SDS enabled remarkable preconcentration via electrostatic interactions and conferred antifouling properties. The oxidation of LC was an irreversible, predominantly diffusion-controlled process with a one-electron, one-proton rate-determining step. Under optimized conditions, the sensor exhibited a wide linear dynamic range (4.0 nM–22 µM in buffer) with a limit of detection of 0.9 nM in artificial tear fluid, and was successfully applied to a commercial nasal spray, with recoveries of 99.15–100.80% (RSD ≤ 1.12%). As a result, we were able to detect LC either alone or in the presence of azelastine HCl (AZE) or emedastine difumarate (EMD) using square wave voltammetry. The AGREE metric gave a score of 0.79, indicating the greenness of the overall analytical procedure. The sensor is suitable for routine quality control and ocular pharmacokinetic studies.

Chemosensors doi: 10.3390/chemosensors14110245

Authors: Leilei Lan Ziheng Ni Yongcai Li Yixue Zhang Kexin Wang Baichun Yao Caiye Zhao

Semiconductors are regarded as promising alternatives to traditional noble metals for constructing surface-enhanced Raman scattering (SERS) substrates. Nevertheless, their sensitivity is insufficient for widespread practical applications. In this work, urchin-like tungsten oxide (WO3) nanostructures with different oxygen-vacancy contents are fabricated by tuning the solvothermal reaction temperature. Oxygen-vacancy-rich WO3 nanostructures display a broad localized surface plasmon resonance band, whose resonance maximum is centered at ~585 nm. Owing to the synergistic coupling of electromagnetic enhancement and interfacial charge transfer enhancement, the as-synthesized urchin-like WO3 nanostructures exhibit outstanding SERS sensing capability toward Rhodamine 6G molecules. The lowest detectable concentration of 1 × 10−7 M and a remarkable enhancement factor of 5.1 × 105 are achieved. The relative standard deviations below 6.5% are obtained, indicating outstanding signal uniformity of the WO3 SERS substrates. In addition, these urchin-like WO3 nanostructures can serve as SERS substrates for the detection of prohibited fishery drugs. This work offers a feasible strategy for fabricating high-performance semiconductor-based SERS platforms, and the plasmonic urchin-like WO3 nanostructures show great prospects for practical chemical sensing applications.

Chemosensors doi: 10.3390/chemosensors14110244

Authors: Sayeh Dowlatshahi Ehsan Shabani Mohammad J. Abdekhodaie Mohammad Ali Shafiee

Prostate cancer is a neoplastic disease that generally remains asymptomatic until its late-stage emergence. Among the techniques approved to date for prostate cancer diagnosis, prostate-specific antigen (PSA) screening is the most established method for its early detection. Currently, centralized clinical laboratories measure serum PSA using costly, time-consuming, multi-step immunoassays that rely heavily on lab technicians and highly sophisticated equipment. To address this issue, we developed a proof-of-concept, disposable, label-free electrochemical biosensor for rapid detection of PSA-α1-antichymotrypsin complex (PSA-ACT) and free PSA (fPSA) using a single antigen incubation step. The immunosensors were based on HNO3-pretreated pencil graphite electrodes (PGEs) modified with graphene oxide (GO). Antigen concentrations were determined by measuring signals from the [Fe(CN)6]3−/4− redox probe using cyclic voltammetry (CV); the presence of antigen molecules decreased anodic current response at the PGE surface. A log-linear calibration response over 1–1000 ng/mL and an estimated limit of detection (LOD) of 2.48 ng/mL were observed for PSA-ACT, indicating a detection range of 2.48–1000 ng/mL for the tPSA biosensor. The fPSA immunosensor showed a log-linear calibration response over 0.1–1000 ng/mL; however, an analytical LOD below the lowest experimentally evaluated calibrator (0.1 ng/mL) was not determined. Analyses of the biosensors’ performance, selectivity, repeatability, and short-term storage stability, along with a preliminary comparison with commercial enzyme-linked immunosorbent assay (ELISA) kits using real human samples, provided early evidence supporting further development and optimization of these platforms for point-of-care (POC) PSA testing.

Chemosensors doi: 10.3390/chemosensors14110243

Authors: Jiahua Su Wei Qu Chengjun Qiu Yuan Zhuang Yi Qu Muzhi Li Huili Hao Zizi Zhao

1-Aminopyrene, as a transformation product of the priority pollutant 1-nitropyrene, has raised increasing concerns due to its carcinogenic, mutagenic, and bioaccumulative properties in marine environments. A rapid and sensitive method for trace 1-aminopyrene detection in seawater is essential for marine environmental monitoring and public health protection. In this paper, a novel electrochemical immunosensor was fabricated based on an NH2-functionalized multi-walled carbon nanotubes-chitosan (NH2-MWCNTs/CS) nanocomposite film loaded with anti-1-aminopyrene antibody on a glassy carbon electrode. The NH2-MWCNTs/CS composite served as a conductive and biocompatible matrix that facilitated interfacial electron transfer and provided a suitable surface for adsorption-based antibody immobilization. Potassium ferricyanide was employed as an electrochemical probe to generate voltammetric signals, and the immune response was investigated by differential pulse voltammetry. Under optimized experimental conditions, the peak current change was inversely proportional to the 1-aminopyrene concentration in the range of 0.5 ng·mL−1 to 110 ng·mL−1, with a detection limit of 0.36 ng·mL−1. The immunosensor demonstrated good selectivity, stability, and anti-interference capability in complex sample matrices. It was successfully applied to the determination of 1-aminopyrene in real seawater samples, with recovery rates ranging from 97.53% to 99.30% and relative standard deviations below 0.92%. The results indicate that the proposed immunosensor offers a simple, cost-effective, and environmentally friendly alternative to conventional chromatographic methods, and holds great potential for on-site monitoring of trace PAH metabolites in marine environments.

Chemosensors doi: 10.3390/chemosensors14110242

Authors: Taiwo Musa Adeniji Palak Sondhi Keith J. Stine

In this paper, we report a flow injection electrochemical sensor for bisphenol F based on a hierarchical multimodal nanoporous gold (hm-NPG)/multi-walled carbon nanotube (MWCNT) nanocomposite deposited onto a screen-printed carbon electrode (SPCE). The hierarchical pore structure of hm-NPG provides an enhanced electroactive surface area arising from the smaller mesopores and efficient mass transport pathways due to larger macropores, while MWCNTs enhance electrical conductivity and electron transfer. Integration of the sensor with flow injection analysis (FIA) substantially reduced the loss of electrode response associated with electrode fouling arising from the electropolymerization of BPF during oxidation. Cyclic voltammetry and electrochemical impedance spectroscopy (EIS) were employed to characterize the electrochemical behavior of the modified electrode, and chronoamperometry was used for quantitative detection. Under optimized conditions, the sensor exhibited a calculated limit of detection of 6.00 nM, with two linear concentration ranges of 0.5–10 μM and 10–100 μM. The sensor also demonstrated good repeatability (RSD = 5.64%), reproducibility (RSD = 5.78%), and limited response to some common environmental and commercial interferents. Application to spiked tap and bottled water samples demonstrated the potential of the hm-NPG/MWCNT-modified SPCE for BPF detection. The combination of hierarchical porosity of hm-NPG, MWCNTs, and FIA provides an effective strategy to improve the electrochemical detection of BPF while minimizing signal loss due to electrode fouling.

Chemosensors doi: 10.3390/chemosensors14100241

Authors: Abdul Shakoor Syed Adil Sardar Almas Woo Young Kim Shahzad Iqbal

Intelligent gas sensors (IGSs) have garnered significant attention owing to their highly sensitive, selective, and reliable gas detection in industrial safety, environmental monitoring, food quality assessment, healthcare diagnostics, and smart Internet of Things (IoT) frameworks. While traditional metal oxide semiconductor (MOS) gas sensors demonstrate exceptional sensitivity and cost-effective fabrication, their practical implementation is typically hindered by slow recovery, poor selectivity, long-term instability, and susceptibility to environmental interference. In recent times, single-atom engineered materials, specifically single-atom catalysts (SACs), have attracted considerable attention because isolated metal atoms maximize atomic utilization efficiency while providing highly uniform and well-defined catalytic active sites. These exceptional atomic-scale properties substantially enhance charge transfer, gas adsorption, surface reaction kinetics, and catalytic activity, resulting in marked improvements in sensing performance. At the same time, the rapid advancement of artificial intelligence (AI) and machine learning (ML) has transformed traditional gas sensors into smart sensing technologies with optimized pattern recognition, feature extraction, gas classification, concentration prediction, drift compensation, and instantaneous decision-making. In this review, we have comprehensively summarized the recent advancements in single-atom engineered materials for smart gas sensing. The fundamental sensing mechanisms of single-atom materials, such as electronic structure engineering, adsorption modulation, and catalytic activation, are first discussed. Afterward, recent progress in SA-supported MOs gas sensors (SAC/MO-GSs) is thoroughly addressed, with special focus on SA classification, anchoring and stabilization strategies, metal–support interactions, and their impact on catalytic reactions, gas adsorption, charge transfer, and sensing performance. The incorporation of ML algorithms-such as random forests (RF), support vector machines (SVMs), k-nearest neighbors (k-NNs), principal component analysis (PCA), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and deep learning architectures-for smart gas sensing and sensor-array design is critically examined. Additionally, current challenges relating to atomic stability, scalable synthesis, data availability, computational cost, model generalization, practical device integration, and long-term durability are reviewed alongside emerging prospects in explainable AI, edge computing, digital twins, and self-powered wearable sensing devices. In conclusion, we have proposed future research directions toward the design of next-generation smart gas sensors that integrate atomic-scale sensing material engineering along with cutting-edge artificial intelligence, offering a route toward low-power, accurate, and autonomous gas-sensing applications.

Chemosensors doi: 10.3390/chemosensors14100240

Authors: Dain Kang Jongho Lee Yeongjun Seo Taeha Lee Gyudo Lee

Paper-based colorimetric glucose sensing has evolved from simple visual assays toward digitally quantified systems increasingly supported by artificial intelligence (AI). However, advanced computational analysis cannot fully compensate for limitations in sensing hardware or for insufficient and poorly standardized analytical data. In this Perspective, we argue that truly AI-ready paper-based glucose sensing requires coordinated development of three capabilities: selective molecular input, sensitive and reproducible signal generation, and standardized high-throughput data generation. Selective input can be engineered through filtering and permselective interfaces; signal generation can be strengthened through catalytic amplification, enzyme stabilization, reagent organization, and reaction-microenvironment engineering; and standardized paper platforms can provide the large, comparable datasets required for robust machine-learning models. We discuss these developments in the context of paper-based glucose assays and propose an integrated framework in which sensing chemistry, device architecture, dataset design, and computational analysis are co-designed. Such integration shifts AI from compensating for limitations of individual assays toward extracting robust quantitative information from intrinsically selective, informative, and reproducible analytical data.

Chemosensors doi: 10.3390/chemosensors14100238

Authors: Mona A. Abdel Rahman Aya A. Mouhamed Ola G. Hussein Mahmoud A. Tantawy Suliman A. Almahmoud Amr M. Mahmoud Eman M. Moaaz

Accurate monitoring of antidepressant drugs is essential for ensuring therapeutic efficacy and minimizing adverse effects, creating a demand for sensitive and reliable analytical methodologies. In the present study, a novel electrochemical sensor was developed for the determination of dothiepin hydrochloride (DOTH) using an iron-based metal–organic framework supported on nitrogen-doped graphite (Fe-MOF@N-dG). The composite material was synthesized through a rapid sonication-assisted approach at ambient temperature, avoiding hydrothermal/solvothermal heating and reducing the consumption of hazardous organic solvents. The physicochemical and electrochemical characteristics of the prepared Fe-MOF@N-dG were investigated using scanning electron microscopy, energy-dispersive X-ray spectroscopy, Fourier-transform infrared spectroscopy, X-ray diffraction, and electrochemical impedance spectroscopy. Characterization results confirmed the successful formation of the composite and demonstrated a synergistic interaction between the Fe-MOF and nitrogen-doped graphite. The conductive N-dG network facilitated electron transfer, while the Fe-MOF provided abundant electrocatalytic active sites, which enhanced sensing performance. Electrochemical measurements were performed in Britton–Robinson buffer solution at pH 3.0 under optimized conditions. The fabricated sensor exhibited a linear response over the concentration range of 0.6–100 μM, with a detection limit of 0.073 μM, indicating good sensitivity toward DOTH determination. The proposed method was validated with respect to accuracy and precision and successfully applied to the analysis of spiked human plasma samples. The environmental sustainability of the developed methodology was also assessed using the recently introduced Environmental, Performance, and Practicality Index (EPPI), together with other established green analytical evaluation tools. The findings demonstrate that the Fe-MOF@N-dG platform offers a potentially sensitive, reliable, and environmentally sustainable strategy for electrochemical determination of DOTH in human plasma.

Chemosensors doi: 10.3390/chemosensors14100239

Authors: Kaiqiang Zeng Siqi Liu Shuyuan Jia Deyu Liu Qing Kang Hong Zhao Jinjing Li Wensheng Liu Jian-Hua Zhang Yang Teng

Carbon dots (CDs) have emerged as widely used functional nanomaterials in biomedical sensing due to their tunable fluorescence emission wavelength, excellent biocompatibility, and ease of functional modification. Fluorescent CDs operating in the near-infrared region II (NIR-II, 1000–1700 nm) offer advantages such as deep tissue penetration, low tissue autofluorescence background, and a high imaging signal-to-noise ratio, making them widely used in the detection of disease biomarkers. This article systematically reviews the latest research progress of NIR-II CDs in the detection of disease-related biomarkers. Firstly, we summarize the classification of fluorescent CDs and two types of chemical synthesis strategies. Subsequently, the research progress of NIR-II CDs in the detection of disease-related physiological markers is reviewed, and the fluorescence biosensing mechanism of NIR-II CDs is analyzed in depth. Finally, we explore the advantages of NIR-II fluorescent CDs in disease diagnosis and molecular imaging, analyze their current key challenges, and provide insights into their future development directions. This review aims to garner increased attention for the development of a new generation of high-performance NIR-II fluorescent CD biosensors.

Chemosensors doi: 10.3390/chemosensors14100237

Authors: Minghan Li Yinuo Chen Yihang Du Yicong Li Jinpeng Shi Jie Tong Yinan Li Jiashuang Xu Qiuyue Wu Shengyuan Deng Xingrui Li

The tumor microenvironment (TME) is spatially and temporally heterogeneous, limiting the specificity of single-biomarker strategies for resolving complex tumor states. The sequence programmability and predictable base pairing of DNA allow multiple biological inputs to be recognized, logically processed, and translated into defined analytical outputs. Here, we review recent advances in DNA logic-gated biosensors and associated nanodevices for multicomponent TME profiling, spanning nucleic acids, proteins, extracellular vesicles, and physicochemical cues. Particular attention is directed to how molecular recognition, Boolean computation, signal amplification, and transduction collectively govern analytical specificity, signal-to-noise ratio, reaction kinetics, and operational reliability in complex biological matrices. We further compare chemical stabilization, smart delivery, and spatial confinement strategies, together with applications in tumor phenotyping, immune-response monitoring, molecular imaging, and conditional therapeutic activation. Emerging directions include continuous and wearable monitoring, spatially organized molecular circuits, and advanced DNA molecular computing. Translation, however, will require improved nuclease resistance, delivery efficiency, threshold robustness, fabrication reproducibility, clinical-matrix validation, and system-level integration. By converting multicomponent TME signals into programmable molecular decisions, DNA logic-gated biosensing may support more reliable tumor characterization, precision diagnosis, and context-dependent therapy.

Chemosensors doi: 10.3390/chemosensors14100235

Authors: Shengsheng Wang Xulong Peng Tian Yao Qingzhao Wan Tulin Lu Jing Zhu Yaqi Wang

Wine-processed Scutellaria baicalensis is a processed Chinese herbal product obtained by wine-processing of raw S. baicalensis. Flavonoids are its core pharmacodynamic constituents with heat-clearing, anti-inflammatory, hepatoprotective and other bioactivities. High-performance liquid chromatography (HPLC) remains the reference for their quantification but is slow and solvent-intensive. This study proposes a chromatography-free alternative that fuses near-infrared (NIR) and ultraviolet (UV) spectra through low- and mid-level data fusion, coupled with interval partial least squares (iPLS) variable screening, to quantify the four flavonoids simultaneously across five processing degrees (raw plus four stir-frying durations, n = 90). HPLC values served as reference and the set was split by the Kennard–Stone algorithm. Against single-spectroscopy models, data fusion improved prediction for all four analytes: low-level fusion benefited the aglycones baicalein and wogonin (e.g., wogonin: Rc2 = 0.973, Rp2 = 0.979), whereas mid-level fusion raised the glycosides baicalin and wogonoside to satisfactory accuracy. Across all analytes, calibration R2 > 0.93, prediction R2 > 0.90, and RPD > 2.5. The method supports rapid, non-destructive evaluation of wine-processed S. baicalensis and is extensible to other processed herbal medicines.

Chemosensors doi: 10.3390/chemosensors14100236

Authors: Zhi Zhang Dian Rong Xiaoping Wang Chengwei Sun

Porphyrins are typical macrocyclic conjugated compounds widely utilized for designing high-sensitivity and high-selectivity chemical sensors. Porphyrin macrocycles can self-assemble into uniform two-dimensional (2D) nanostructures on gold films via π-π stacking, which extend horizontally along the X-Y plane and possess nanoscale thickness in the vertical Z-direction. The inherent electron-rich conjugated skeleton, 2D nanostructure-induced signal enhancement, and specific molecular recognition ability of porphyrins jointly satisfy the two core requirements of surface plasmon resonance (SPR) trace detection, namely target-specific recognition and signal amplification. This study leverages the integrated superiorities of porphyrin architectures to develop a high-sensitivity SPR strategy for heavy metal ion detection. Three key research contents are systematically explored herein: (1) sensitive layer fabrication, focusing on the optimization of preparation protocols for 2D porphyrin nanostructures on gold films, including the screening of material concentrations and solvents as well as the precise regulation of porphyrin aggregation; (2) detection performance evaluation, covering critical analytical parameters such as limit of detection (LOD), linear concentration range, and linear response property; and (3) practical performance assessment, involving the fabrication success rate, the detection repeatability and the storage stability of the sensitive layer, as well as the definition of application constraints including storage conditions, service life and standard operating procedures. The proposed porphyrin self-assembled structure achieves an ultra-low LOD of 1 pM for target detection, which is about 250-fold lower than the Class I limit for mercury in surface water (50 ng/L, GB 3838-2002).

Chemosensors doi: 10.3390/chemosensors14100234

Authors: Xueqin Yang Shiyang Dong Xingyu Liu Chaobo Chen

Near-infrared spectroscopy supports rapid quantification of moisture, oil, protein, and starch in corn. Their prediction models may require different spectral inputs, complicating shared-variable selection. We developed the multicomponent shared wavelength selection (MCSWS) algorithm to select one fixed-size subset for all four constituents. It combines a scale-normalized objective with five adaptively fused descriptors. Evaluation used 80 corn samples originally measured at Cargill and distributed by Eigenvector Research, with 700 wavelengths from the m5 instrument. Eight repeated four-way splits assessed held-out prediction (outer cross-validation). Selection and calibration tuning used training-only five-way splits (inner cross-validation). MCSWS retained 38 variables, a 94.6% reduction, and achieved a joint normalized root-mean-square error of prediction (JNRMSEP) of 0.35 ± 0.03. Rounded results were 0.34 ± 0.02 for full-spectrum partial least squares (PLS) regression and competitive adaptive reweighted sampling coupled with PLS (CARS-PLS). Values are means ± sample standard deviations across eight repeat means. After exploratory corn calibration development, the frozen workflow yielded JNRMSEP = 0.049 using six variables on the official casein–glucose–lactate (CGL) test split versus 0.047 for CARS-PLS and 0.016 for full-spectrum PLS. These results characterize an accuracy–dimensionality trade-off. Variable reduction concerns regression inputs after full-spectrum preprocessing, not demonstrated reductions in physical measurements.

Chemosensors doi: 10.3390/chemosensors14100233

Authors: Zhecong Yuan Xindong Wang Xinxin Wu Xinai Zhang

The demand for non-destructive evaluation of agricultural products has stimulated the emergence of multimodal sensing technologies capable of integrating diverse quality- and safety-related information. Recent advances in multimodal sensing and machine learning (ML) have enabled intelligent platforms capable of integrating diverse physicochemical, structural, and biochemical information for comprehensive product characterization. This review summarizes the principles, data-processing strategies, and applications of multimodal sensing systems combining machine vision, visible-near-infrared spectroscopy, hyperspectral imaging, Raman and fluorescence techniques, electronic noses, and auxiliary information with ML algorithms. The complementary capabilities of different sensing modalities for evaluating external appearance, internal quality, chemical composition, freshness, spoilage, and safety hazards are discussed. Meanwhile, the roles of conventional ML and deep learning in feature extraction, pattern recognition, and multimodal data fusion are highlighted. Although advanced algorithms and increased data dimensions improve analytical performance, reliable deployment requires robust datasets, effective calibration transfer, model interpretability, uncertainty assessment, and independent validation. Future advances should prioritize self-supervised and few-shot learning, domain adaptation, physics-informed models, lightweight edge intelligence, and standardized benchmarking to improve model reliability, transferability, and scalability. These innovations will facilitate the deployment of intelligent sensing systems from laboratory environments to real-world agricultural quality and safety management.

Chemosensors doi: 10.3390/chemosensors14100232

Authors: Run Gao Li Zhao Lin Ma Yinan Chen Jialuo Mao Xiuxiu Wu Jinsong Yu Hui Cao

Thiamethoxam is a widely used neonicotinoid insecticide in agriculture, and its excessive use has raised growing concerns about food safety and environmental pollution. In this study, a portable fluorescence sensing platform was developed for the rapid detection of thiamethoxam by integrating a molecularly imprinted polymer-coated lanthanide metal–organic framework (Eu/Tb-MOF@MIP) with a smartphone-based imaging system. The synthesized Eu/Tb-MOF@MIP exhibited excellent stability in aqueous media and high thermal stability up to 270 °C, attributed to the protective imprinted polymer layer. Owing to the dual recognition mechanism arising from the ligand groups of the Ln-MOF and the specific imprinted cavities, the platform also displayed excellent selectivity toward thiamethoxam, with an imprinting factor of 3.06. Under optimized conditions, the platform enabled sensitive detection of thiamethoxam over a linear range of 0.01–9 mg·L−1, with a detection limit of 9.3 μg·L−1. When applied to spiked vegetable samples, the method achieved recoveries of 76.0–95.3% with relative standard deviations (RSDs) of 1.24–5.12%. Mechanistic investigation revealed that the fluorescence quenching was primarily attributable to the inner filter effect (IFE) and analyte-induced excited-state quenching, with the latter likely enhanced by MIP-mediated enrichment.

Chemosensors doi: 10.3390/chemosensors14100231

Authors: Huicong Cao Xinhao Zhuang Guixian Zhu Taotao Mu Yuan Ma

Wearable health monitoring is expanding from physical signals, such as heart rate and body temperature, to molecular information at body interfaces and in biofluids. Raman spectroscopy provides chemically specific vibrational fingerprints and supports label-free molecular analysis. Surface-enhanced Raman scattering (SERS) further improves sensitivity through plasmonic enhancement, enabling analysis of low-abundance biomarkers. For wearable sensing, SERS-active structures must provide reliable hotspots, integrate with flexible or three-dimensional supports, and remain compatible with biofluid sampling. Self-assembly offers a bottom-up route to generate nanoscale interparticle gaps and integrate plasmonic structures with diverse supports. Existing reviews have mainly focused on biomedical SERS, flexible SERS substrates, or nanoparticle assembly, while links between self-assembly routes, biofluid sampling, and wearable integration have received less systematic attention. This review summarizes recent progress in self-assembled plasmonic structures for wearable SERS. Reported strategies are classified as fluid-interface, evaporation-induced, and template-assisted self-assembly according to the dominant assembly process and spatial constraints. Representative studies involving sweat, interstitial fluid, tears, and wound exudate are compared across biofluid-sensing scenarios. Finally, we discuss the stability of the effective sensing area under on-body conditions and quantitative validation in real biofluids.

Chemosensors doi: 10.3390/chemosensors14100230

Authors: HyeRee Han

Background: Astronauts operating in extravehicular activity (EVA) and intravehicular activity (IVA) suits are exposed to a closed, thermally demanding microenvironment in which sweat accumulation, hydration status, skin-barrier disturbance, optical-signal interference, and changes in oxygen-carrying capacity can affect health, comfort, and task performance. This review therefore evaluates five sensing modalities—humidity, pH, sebum, melanin, and hemoglobin—not as general cosmetic sensors, but according to their potential roles in AI-enabled astronaut-suit monitoring. Methods: A systematic literature review was conducted using multidisciplinary scholarly databases and publisher platforms. From 1036 initially identified records, 187 full texts were screened, 135 reports were assessed for eligibility, and 93 studies were included. Studies were organized into humidity (n = 22), pH (n = 20), sebum (n = 16), melanin (n = 20), and hemoglobin (n = 15) categories and comparatively evaluated for sensing principle, quantitative performance, calibration requirements, wearability, environmental robustness, and relevance to astronaut-suit integration. Results: Humidity and pH sensing are currently the most directly compatible with textile or skin-interfaced suit architectures because they can provide continuous information on the local microclimate and sweat/skin chemistry. Hemoglobin sensing is promising for trend monitoring of oxygen-carrying status but requires careful optical calibration for pigmentation, motion, perfusion, and pressure effects. Melanin sensing is primarily valuable as a calibration and radiation/optical-context variable rather than as an acute physiological alarm, whereas continuous sebum sensing remains comparatively immature for operational spacesuit use. Edge artificial intelligence (AI) can support multimodal sensor fusion, artifact rejection, personalized baselines, anomaly detection, and tiered alerts without implying stand-alone disease diagnosis. Conclusions: The five modalities have different levels of technological readiness for astronaut suits. Near-term research should prioritize humidity, pH, and optical hemoglobin trend monitoring, while treating melanin as a correction/context variable and sebum as an exploratory skin-barrier marker. Translation to EVA/IVA use requires standardized calibration, low-power edge processing, long-duration biocompatibility, resistance to sweat, pressure, motion and thermal cycling, and validation in suited analog environments before flight deployment.

Chemosensors doi: 10.3390/chemosensors14100229

Authors: Yunhua He Yuhang Xie Xin Zheng Hanyu Li Xianfeng Lang Yongsheng Luo

Real-time detection of the six characteristic gases dissolved in transformer oil (H2, CO, CO2, CH4, C2H4, C2H2), present from sub-ppm levels for C2H2 to several thousand ppm for CO2, is essential for early fault diagnosis in oil-immersed power equipment, yet conventional metal-oxide sensors require elevated operating temperatures and suffer from poor selectivity. Density functional theory is used here to evaluate two metal–organic frameworks, Cu-BTC and ZIF-8, as room-temperature sensing candidates for these gases. Adsorption energy, charge transfer, DOS, work-function variation and temperature-dependent recovery time are analyzed for all twelve systems. Cu-BTC exhibits the strongest response to CO (adsorption energy: −1.745 eV, charge transfer: 0.426 e) with pronounced C-2p/Cu-3d hybridization, whereas ZIF-8 shows the strongest affinity toward C2H2 (adsorption energy: −1.986 eV); the two frameworks thus display complementary selectivity. Weakly bound systems desorb within microseconds to seconds at room temperature, enabling rapid regeneration, while CO on Cu-BTC and C2H2 on ZIF-8 are retained almost irreversibly and suit gas capture better. These results provide a theoretical basis for MOF-based on-line monitoring of dissolved gases in transformer oil and power-equipment fault diagnosis.

Chemosensors doi: 10.3390/chemosensors14100228

Authors: Mahmoud El-Maghrabey Rania El-Shaheny Ali Abdel-Hakim Helal F. Hetta Naoya Kishikawa

Quinones have emerged as versatile optical signal transducers in spectroscopic analytical chemistry because of their unique redox activity, electron-transfer capability, reactive oxygen species generation, and tunable photophysical properties. Beyond their traditional roles as biologically active molecules and electrochemical mediators, quinones now function as derivatization reagents, fluorescence-modulating probes, recyclable chemiluminescent and colorimetric signal amplifiers, functional nanomaterial components, and stable labels for enzyme-free immunoassays. This review presents a unified analytical framework that organizes these diverse applications according to their mechanistic roles in molecular recognition, optical signal generation, redox amplification, and nanomaterial-assisted sensing. The molecular principles governing quinone redox cycling, photoinduced electron transfer, fluorescence quenching and recovery, reactive oxygen species-mediated amplification, and nanoparticle coupling are critically discussed to explain analytical performance and guide rational platform design. Recent advances in quinone-derived and quinone-functionalized carbon dots, chemiluminescent and colorimetric amplification systems, fluorogenic derivatization, and quinone-based enzyme-free immunoassays are comprehensively evaluated with emphasis on sensitivity, selectivity, stability, portability, and practical applicability. Current limitations, including matrix interference, mechanistic validation, aqueous compatibility, and reproducibility, are critically assessed, and future opportunities for biomedical diagnostics, food analysis, environmental monitoring, and point-of-care testing are highlighted. Collectively, this review establishes quinones as multifunctional analytical building blocks for next-generation optical sensing platform development.

Chemosensors doi: 10.3390/chemosensors14100227

Authors: Hui Li Yuhao Qiu Xuxu Wang Banglei Liang Zongtao Wang Yingying Dong Qiao Feng

Pt-functionalized SnO2 hollow nanotubular structures were fabricated by single-nozzle electrospinning followed by calcination and evaluated for H2S sensing. At an operating temperature of 350 °C, the 2 wt% Pt/SnO2 sensor exhibited a response of 567.1 to 50 ppm H2S, which was 11.9 times higher than that of pristine SnO2. The sensor showed response and recovery times of approximately 1 and 150 s, respectively, an experimental detection limit of 200 ppb, as well as excellent selectivity, repeatability, and long-term stability. Experimental characterizations and DFT calculations indicated that the improved performance arises from the accessible gas-diffusion pathways of the hollow nanotubular structure, together with Pt-enhanced H2S adsorption and interfacial electronic modulation. In a proof-of-concept shrimp storage test, the sensor response changed consistently with pH, color difference, and sensory scores, with a marked response increase near the observed transition in freshness indicators. These findings demonstrate the potential of Pt/SnO2 HNTSs for H2S sensing and non-destructive shrimp freshness monitoring.

Chemosensors doi: 10.3390/chemosensors14100226

Authors: Gustavo Adolfo Bautista Gomez Cristhian Manuel Durán Acevedo Jeniffer Katerine Carrillo Gómez

Electronic noses provide a pattern-based approach for investigating volatile profiles associated with colorectal cancer; however, the combined analysis of participant-matched sensor profiles from different biological matrices remains largely unexplored. This study evaluated breath, urine, and axillary sweat profiles acquired from the same participants using a single microelectromechanical system (MEMS)-based electronic nose system for colorectal cancer (CRC) classification. Matched records from 60 participants were analyzed, including 27 with CRC and 33 controls (CO). Signals from each biological matrix were processed independently using fractional-difference feature extraction, min–max scaling, Orthogonal Signal Correction (OSC), and Principal Component Analysis (PCA). Because OSC is a supervised preprocessing method that incorporates class information, the PCA score plots were used only for descriptive visualization of the transformed feature space, whereas predictive performance was assessed exclusively within the cross-validation framework. The selected PCA scores were subsequently integrated into a six-feature participant-level representation. Five supervised classifiers were evaluated using stratified five-fold cross-validation, with all data-dependent transformations fitted exclusively on the corresponding training sets. In this exploratory setting, Logistic Regression showed the highest observed mean accuracy of 98.3 ± 3.3% among the evaluated models, though this represents an internal estimate rather than a claim of statistical superiority. Aggregated out-of-fold predictions yielded 98.3% accuracy, 100.0% sensitivity, 97.0% specificity, a 98.2% F1-score, and an AUC of 1.00 (95% CI: 0.99–1.00). Our comparative analysis revealed that axillary sweat alone demonstrated a high discriminative power and that the pairwise combination of urine and sweat achieved 100% accuracy. While the complete three-matrix integration maintained high robustness, it did not provide incremental value over sweat alone. These findings highlight the remarkable potential of the axillary volatilome for CRC classification and suggest that selectively combining highly discriminative matrices may be more effective than integrating all available biological sources.

Chemosensors doi: 10.3390/chemosensors14100225

Authors: Li Sin Wong Xiaojing Zhang Yanran Xia Xinrui Tang Hongyang Guo Hongjian Zhang Ping Wang Boyang Xu Liujing Zhuang

Addictive drug abuse remains a major global public health concern, creating a need for rapid and reliable approaches for drug screening and on-site analysis. Conventional analytical techniques provide accurate chemical identification but generally require laboratory-based instrumentation and sample processing. Biomimetic sensors incorporating biological recognition elements offer an alternative approach by exploiting molecular recognition and, in some cases, receptor-mediated signaling. This review summarizes recent progress in the use of G-protein-coupled receptors (GPCRs) and odorant-binding proteins (OBPs) as recognition elements for addictive drug-related sensing. The structural basis of ligand recognition and the native signaling mechanisms of GPCRs and OBPs are discussed, together with structural and computational studies that provide insight into drug-recognition element interactions. Recent sensing strategies based on electrical, optical, electrochemical, and acoustic transduction are then examined in terms of recognition mechanism, analytical performance, and application characteristics. Reported systems include direct measurements of drug–protein interactions, as well as functional assays that monitor receptor activation and downstream signaling. These studies demonstrate the potential of GPCR- and OBP-based recognition elements for sensitive detection of addictive drugs and related ligands, but also reveal limitations in discriminating structurally related compounds. In particular, receptor activation or ligand binding does not necessarily provide definitive chemical identification, which limits the direct application of activity-based sensing to forensic and regulatory analysis. Other challenges include maintaining functional recognition elements following immobilization, achieving reproducible coupling between molecular recognition and signal generation, and establishing performance in complex samples. Future development will require improved control of recognition-element interfaces, broader use of multiplexed recognition, and complementary analytical confirmation to improve the reliability and practical applicability of biomimetic sensors for addictive drug detection.

Chemosensors doi: 10.3390/chemosensors14100224

Authors: Ligita Mežaraupe Evita Romanovska-Dzalbe Sanita Kecko Muza Kirjušina Veronika Pavlova Elena Kirilova Ilze Rubenina

Fluorescence imaging has emerged as a transformative tool in parasitological research, providing high-contrast visualization of complex morphologies often obscured in traditional bright-field microscopy. This review article synthesizes recent advancements in the application of polycyclic aromatic fluorophores, specifically benzanthrone and anthraquinone derivatives, for helminth imaging. It evaluates the critical interplay between chemical fixation strategies, such as ethanol-based protein precipitation, and optical clearing agents like ethanol–xylene and lactic acid to optimize tissue transparency for confocal laser scanning microscopy. Recent practical applications are examined across diverse taxa, demonstrating that the rational design of N-substituted sensors—including aminoamides and aminophosphonates—enables high-resolution characterization of muscular–tegumental systems and reproductive organs in under one hour. Key breakthroughs highlighted include the rapid sex determination of Trichinella larvae and the detailed resolution of the tripartite muscle framework in trematodes like Prosotocus confusus. These developments establish anthrone-based sensors as a robust, efficient alternative to conventional commercial kits, offering superior photostability and spectral tunability.

Chemosensors doi: 10.3390/chemosensors14100223

Authors: Xianchen Wang Yuanzhi Chen Zhengkang Yu Yongting Zhang Qiang Fu Keliang Wang

The sensing and detection of toxic and hazardous gases represents a critical technology in urgent need of development. Herein, density functional theory is employed to explore the adsorption characteristics and electronic response of CO2, CO, SO2, and H2S on an SbP monolayer. The four molecules are all weakly adsorbed on the surface with adsorption energies between −0.097 and −0.242 eV. In particular, SO2 shows the strongest interaction and accepts 0.096 e from the SbP monolayer. After adsorbing the gas molecules, the SbP monolayer maintains its semiconductor properties, but its energy bands and density of states are modulated to varying degrees. SO2 reduces the bandgap of the SbP monolayer from 1.71 eV to 1.60 eV and has the most pronounced effect on the electronic states near the Fermi level. The sensitivity shows that the response of the SbP monolayer to SO2 at 300 K reaches 5.913, and it remains consistently higher than that to CO2, CO, and H2S within the temperature range of 300–500 K. SO2 adsorption also induces a change in work function of 0.567 eV, while its recovery time remains on a relatively short time scale. The above results suggest that among the four gases investigated, SO2 has the most significant modulation effect on the electronic properties of the SbP monolayer and exhibits good theoretical desorption capability. Therefore, the SbP monolayer can be considered as a robust candidate gas-sensing material for SO2 detection.

Chemosensors doi: 10.3390/chemosensors14100222

Authors: Eleonora Alfinito Matteo Beccaria

Protein-based biosensors exploit the extraordinary selectivity of proteins toward specific ligands—such as other proteins, ions, and electromagnetic signals—often surpassing engineered recognition systems. Ligand binding induces structural rearrangements that alter a protein’s electrical and dielectric properties, and in electrical biosensors, these changes, particularly in conductance, are what allow the target analyte to be quantified. However, despite decades of effort, no unified strategy exists for defining and calculating protein permittivity (κ), a quantity central to interpreting such signals. Here, we present a computationally inexpensive framework for evaluating protein permittivity, built on a network representation of the protein derived from its three-dimensional topology, in which each amino acid’s permittivity is assigned continuously between a dry, intrinsic value and the bulk solvent value according to its coordination number. Applied to 60 structurally diverse proteins, the model reveals a robust relationship between effective permittivity and a simple topological compactness index. As a proof of concept, we compute the electrical impedance of apo-azurin and the protein NP_888769.1 under two electrode-contact configurations, finding a measurable, geometry-dependent response that vanishes under a uniform dielectric constant—an experimentally testable signature of internal dielectric structure that invites a re-examination of what protein permittivity experiments actually measure.

Chemosensors doi: 10.3390/chemosensors14100221

Authors: Mateja Budetić Korina Krejča Andrea Dandić Aleksandar Széchenyi Mirela Samardžić

Fluconazole (FLU) is a widely used antifungal drug, and its reliable determination is important for quality control in pharmaceutical analysis. This study aimed to develop a simple and accurate FLU-selective potentiometric sensor based on a molecularly imprinted polymer (MIP). The prepared MIP was evaluated by FT-IR spectroscopy and TG/DSC analysis. The sensor was characterized using direct potentiometry. The influence of membrane composition on sensor response was investigated by varying the MIP content and plasticizer type. The optimized sensor contained 12.5% MIP and dibutyl sebacate as the plasticizer. It exhibited a near-Nernstian response with a slope of 56.1 mV/decade of activity, a wide measuring range (1.0 × 10−7–1.0 × 10−3 M), and a low limit of detection (7.5 × 10−8 M). Additionally, it showed a fast response (5 s), low signal drift (−0.6 mV/h), and excellent selectivity attributed to the recognition properties of the MIP. The sensor applicability was confirmed by FLU determination in standard solutions and pharmaceutical samples using direct potentiometry and the Gran method. Both approaches provided acceptable results, while the Gran method showed slightly better agreement with the expected FLU concentrations, with recoveries within ±10%. The developed sensor enables reliable FLU determination in pharmaceutical samples.

Chemosensors doi: 10.3390/chemosensors14100220

Authors: Courtney Ann McGuire Istvan Balogh Brian Mence Paola A. Prada-Tiedemann

Knife crime has increased globally, yet the effectiveness of current detection methods used by law enforcement is often limited in operational settings. This challenge highlights the need for innovative detection approaches. The use of canines as highly mobile and sensitive biosensors for the detection of bladed objects may provide an effective and novel solution. A critical step toward implementing canine-based detection is the characterization of the odor signatures associated with bladed objects. Therefore, it is hypothesized that box cutter blades possess a distinct volatile organic compound (VOC) profile that can be differentiated from those of other common metal substrates. To test this hypothesis, headspace extraction and chemical analysis were conducted on box cutter blades of varying sizes, and their VOC profiles were compared with those of metal keys. Five compounds were consistently identified across blade samples and may be considered characteristic odorants of box cutter blades: decanal, eicosane, nonadecane, nonanal, and octadecane. The results suggest that box cutter blades possess detectable and distinguishable VOC profiles that are consistent across different blade sizes. These findings provide foundational data for future odor characterization studies and support the development of canine training aids and detection programs targeting bladed objects.

Chemosensors doi: 10.3390/chemosensors14100219

Authors: Mauro Tomassetti Federico Marini Mauro Castrucci Marco Petrangeli Papini Luigi Campanella

A simple, low-cost analytical platform based on a commercial direct methanol–ethanol fuel cell (D(M–E)FC) combined with chemometric data analysis is investigated for the quantitative analysis and exploratory differentiation of pharmaceutical and biomedical compounds containing at least one hydroxyl group with alcoholic or partially alcoholic character. Quantitative measurements were based on conventional calibration curves obtained from the steady-state current response, whereas compound differentiation exploited the dynamic current discharge profiles. Principal Component Analysis (PCA) and the multiblock Common Components and Specific Weights Analysis (ComDim) algorithm were employed to explore and integrate the information contained in the discharge curves and in their derived kinetic descriptors. Five representative compounds—chloramphenicol, imipenem, atropine, cortisone, and ethanol—were investigated. Steady-state calibration showed satisfactory linear responses for chloramphenicol, imipenem, and ethanol (R2 = 0.9961, 0.9868, and 0.9888, respectively), whereas poorer linear behaviour was observed for atropine and cortisone (R2 = 0.5076 and 0.7956), highlighting the analyte-dependent quantitative performance of the platform. Exploratory analysis of the averaged dynamic profiles revealed compound-dependent response patterns, with most of the relevant variance concentrated in the early transient. Restricting the acquisition to the first 120 s preserved the main structure observed for the complete profiles while substantially reducing the measurement time. Overall, the results indicate that dynamic fuel-cell responses contain analyte-dependent multivariate information that can be exploited for the exploratory differentiation of structurally diverse compounds, providing a proof of concept for combining commercial catalytic fuel cells with chemometric analysis.

Chemosensors doi: 10.3390/chemosensors14100218

Authors: Xiaojuan Zhou Hui Zhuge Bo Ma Zhiwei Ge Li Che Lihua Yang Xiyi Chen Cheng Jiang Shujing Zhang Han Wang Yingchao Wang Xiao Li Yu Tang

Herbal medicines (HMs) are chemically complex multi-component systems, yet analytical strategies that systematically connect chemical composition, biological activity, target prioritization, and quantitative analysis remain limited. Here, we established an integrated analytical workflow combining ultra-high-performance liquid chromatography–quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS/MS), bioactivity-guided fractionation, network pharmacology, molecular docking, surface plasmon resonance (SPR), and quantitative 1H NMR (qHNMR) to investigate Fu’an Granules. UPLC-QTOF-MS/MS provided a broad constituent-annotation profile of the formulation. Bioactivity-guided fractionation using an LPS-stimulated RAW 264.7 macrophage model showed that fractions FA3 and FA4 exhibited relatively stronger inhibition of nitric oxide (NO) production at the tested screening concentration. Corilagin, rutin, and isochlorogenic acid A were subsequently selected as representative activity-associated constituents according to their distribution in the chromatographic fractions and showed reduced NO production without detectable cytotoxicity under the tested conditions. Network pharmacology, protein–protein interaction analysis, pathway enrichment, and molecular docking prioritized EGFR and PTGS2 as candidate binding targets. SPR further demonstrated concentration-dependent interactions between the three representative constituents and both proteins, providing experimental support for ligand-target binding. An exploratory internal-standard-based qHNMR analysis estimated corilagin content at 5.7% (w/w) of the analyzed sugar-depleted extract. Collectively, this study presents an integrated workflow linking chemical profiling, activity-guided constituent prioritization, computational target analysis, biophysical binding assessment, and complementary quantitative analysis. The results support corilagin as a representative activity-associated quantitative constituent for Fu’an Granules while providing a general analytical framework for activity-oriented quality marker evaluation in complex herbal medicines.

Chemosensors doi: 10.3390/chemosensors14100217

Authors: Jiao Yang Chao Yang Chunyan Cao Shengde Yang Shisong Huang Yulan Jian Jie Ding

In this work, a three-dimensional (3D) tubular composite electrode was fabricated for the high-sensitivity electrochemical detection of ascorbic acid (AA). The differential pulse voltammetry (DPV) was employed to investigate the electrochemical behavior of AA at the composite electrode, and the overall electrochemical performance of the sensor was systematically evaluated. Experimental results indicated that this composite electrode exhibited excellent sensitivity toward AA detection. A favorable linear relationship between current intensity and AA concentration was obtained over the concentration range of 20 to 600 μM, with a limit of detection (LOD) of 4.8 μM. Moreover, the composite electrode was successfully applied to detect AA in orange juice for real-sample analysis. The 3D tubular-structured composite electrode developed in this work showed promising application potential for the rapid detection of AA in the field of food safety.

Chemosensors doi: 10.3390/chemosensors14100216

Authors: Yi-Zhi Lin Sheng-Tong Wu Zi-Chen Pan Shou-Nian Ding

Blue-emitting CdSe/ZnS quantum dots require precise control of crystal growth because variations in particle size and surface structure can strongly influence their emission properties. In this study, blue-optical CdSe/ZnS core–shell quantum dots were synthesized using a one-step hot-injection method. Structural and optical characterization confirmed the formation of crystalline QDs with stable fluorescence emission after ZnS shell coating. The synthesized QDs exhibited an average diameter of approximately 3 nm, blue emission at 461 nm, and a photoluminescence quantum yield of 93.31%. The QDs exhibited suitable optical properties for fluorescence-based bioanalytical applications for HCG.

Chemosensors doi: 10.3390/chemosensors14100215

Authors: Youssef O. Al-Ghamdi Amani Chrouda Manahil Babiker Elamin Sabri Messaoudi Hamdi Ben Halima Nicole Jaffrezic-Renault

Green nanomaterials have attracted considerable attention for environmental monitoring due to their sustainability and multifunctional properties. In this study, copper oxide nanoparticles were synthesized using pomegranate peel (pp) extract (CuO@PP) as a reducing and stabilizing agent and investigated for both electrochemical detection and adsorptive removal of tetracycline. The synthesized CuO@PP nanoparticles were characterized by UV–Vis spectroscopy, Fourier-transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDX), X-ray diffraction (XRD) and thermogravimetric analysis (TGA). CuO@PP nanoparticles were immobilized onto a screen-printed electrode for electrochemical detection of tetracycline. Square wave voltammetry (SWV) showed good analytical performance, with a low detection limit of 3.1 nM, good selectivity, and successful application in real-water samples, including untreated tap water and hospital wastewater. In addition, CuO@PP showed effective tetracycline adsorption, reaching 88% removal within 25 min. The Langmuir model gave an estimated maximum adsorption capacity of 312.77 mg g−1, while the Freundlich model provided a better fit to the experimental data, indicating adsorption on a heterogeneous surface. These results demonstrate the potential of green-synthesized CuO@PP as a multifunctional material for both tetracycline detection and removal, with promising applications in environmental monitoring and water treatment.

Chemosensors doi: 10.3390/chemosensors14100214

Authors: Doyeon Im Kyung Hwan Lim Gayoung Seo Sangdoo Ahn

Fourier-transform infrared (FT-IR) spectroscopy provides rapid, accessible, and chemically informative molecular fingerprints, but quantitative analysis of complex samples is often hindered by band overlap, baseline variation, matrix effects, and nonlinear relationships between spectra and target properties. This review examines how artificial intelligence (AI), including machine learning, deep learning, and explainable artificial intelligence (XAI), can convert FT-IR fingerprints into quantitative and interpretable chemical information. Recent studies were organized according to analytical function and examined with respect to spectral acquisition and data organization, preprocessing and input preparation, model development, validation, uncertainty, transferability, and chemical interpretation. AI-assisted FT-IR has been applied to composition and concentration prediction, adulteration and contamination-level estimation, physicochemical and material-attribute prediction, and process-, time-, and state-dependent prediction. Across these applications, robust performance depends on representative independent samples, leakage-free validation, transparent preprocessing, uncertainty assessment, calibration transfer, and spectroscopically plausible interpretation of model-relevant regions. Data augmentation, transfer learning, and XAI may improve model robustness and interpretability, but they require validation with independent real samples. Overall, AI can extend FT-IR from qualitative fingerprinting to quantitative and predictive spectral sensing, provided that future studies emphasize method-centered validation, reproducibility, transferability, and chemically meaningful explanation rather than relying solely on classification accuracy and regression error metrics.

Chemosensors doi: 10.3390/chemosensors14100213

Authors: Hongxi Wang Maolong Chen Runqi Yao Jixing Huang Jiangbo Zhao Liyun Ding

Chloride quantification is crucial for environmental monitoring, marine research, and the durability assessment of geotechnical infrastructures, yet accurate measurement in complex aqueous and soil matrices remains challenging. Optical fiber sensors offer distinct advantages, including resistance to electromagnetic interference, high sensitivity and low cost. Unfortunately, existing fiber-optic chloride sensors are often limited by insufficient sensitivity, poor anti-ion interference, inadequate portability, and an inability to perform long-term in situ monitoring. Herein, a novel fluorescent optical fiber chloride sensor based on UiO-66-NH2/lucigenin composite is fabricated, in which the mixture formed lucigenin encapsulated with UiO-66-NH2 is immobilized within a dual-layer membrane composed of an inner cellulose acetate (CA) matrix and an outer polyvinyl alcohol (PVA) membrane. The chloride-responsive fluorescent probe operates on the principle of fluorescence quenching. The dual-layer sensitive membrane is engineered to significantly enhance sensitivity, linearity, and stability for long-term monitoring. Furthermore, the portable device eliminates the constraints of bulky optical systems and mitigates instability caused by photobleaching, enabling reliable in situ chloride detection in water, soil, and geotechnical environments. Experimental results confirm the superior analytical performance of the sensor across various soil and aquatic media. This work represents a notable advancement in the field of fluorescent optical fiber sensors and holds substantial promise for practical applications.

Chemosensors doi: 10.3390/chemosensors14100212

Authors: Xiujuan Feng Chuhao Huang Liangji Xu Lindani Ncube Haotong Guo

To address the dual challenges of environmental temperature–humidity fluctuations and spurious impulse artifacts in real-time IoT monitoring, this paper proposes a novel digital sensor signal denoising method based on wavelet packet decomposition (WPD) with a dual-feedback adaptive threshold. A four-layer IoT monitoring architecture spanning the perception, control, transmission, and cloud service layers is constructed, with an STM32F103C8T6 microcontroller, an SGP30 multi-pixel metal oxide (MOX) gas sensor, and an ESP8266 WiFi module as the core components; the proposed denoising algorithm is executed entirely on the STM32 edge node at the perception layer. Real-time acquisition of volatile organic compound (VOC) concentration and environmental parameters was conducted across 20 independent experimental groups in a sealed, newly renovated laboratory, using a single SGP30 sensor in chronologically ordered sessions. Based on the non-stationary time–frequency characteristics of the SGP30 on-chip-processed output signals, the optimal four-level decomposition scale was determined through Parseval energy conservation analysis, and the optimal wavelet basis (sym5) was identified through fifth-order Fourier polynomial fitting. The core innovation lies in the dual-feedback adaptive threshold strategy, which integrates physical environmental compensation with statistical node energy weights through a multiplicative mechanism. An ablation study confirms that the multiplicative coupling outperforms the best single-feedback variant by 2.52 dB and the additive-fusion baseline by 1.97 dB. Using the ensemble median of 50 repeated measurements under stable conditions as the pseudo-reference signal, experiments on 66,888 samples demonstrate an SNR improvement of 9.86 dB, a normalized mean square error (NMSE) of 0.0009, and a weighted-average abnormal impulse suppression rate of 79.1%, with an embedded execution latency of only 1.4 ms per frame on the STM32F103 platform.

Chemosensors doi: 10.3390/chemosensors14090211

Authors: Himali Upadhyay Subhash Gurappa S. Sitharama Iyengar Kenneth G. Furton

Food fraud and adulteration continue to threaten food safety, consumer confidence and international trade. The challenges of investigating food-forensics reliably are compounded by increasingly complex supply chains, changing deceptive practices and difficulties in interpreting high-dimensional analytical data. The review critically evaluates current analytical chemistry and artificial intelligence (AI) approaches for the detection of adulteration, authenticity verification and determination of geographical or botanical origin in seafood, spices, meat, dairy products, oils, honey and beverages, distinguishing between direct forensic applications and complementary studies of food quality. The methodological framework includes liquid chromatography–mass spectrometry (LC-MS), headspace solid-phase microextraction coupled to gas chromatography–mass spectrometry (HS-SPME-GC-MS), isotope-ratio mass spectrometry (IRMS), Raman and near-infrared spectroscopy, and sensor arrays, chemometrics, machine learning (ML), convolutional neural networks (CNNs), explainable artificial intelligence (XAI), and multimodal data fusion. The reviewed literature shows that classical chemometric approaches are still useful for the small datasets usually employed in food-forensic studies, while deep learning offers more advantages for larger or high-dimensional datasets but the reliability is dependent on appropriate validation, model interpretability and chemically meaningful features. Future research should be geared toward developing standardized cross-laboratory datasets, strong external validation, explainable and data-efficient models, multimodal data fusion, portable analytical tools and regulatory structures to promote transparent and defensible forensic choices.

Chemosensors doi: 10.3390/chemosensors14090210

Authors: Junhan Yang Guan Xin Daliang Li

Reduced nicotinamide adenine dinucleotide phosphate (NADPH) sustains reductive biosynthesis, antioxidant defense, oxidase activity, and redox signaling; nevertheless, studies framed as “NADPH imaging” often interrogate biochemically non-equivalent variables and analytically distinct endpoints. To resolve that ambiguity, this review organizes current methods by analyte-defining event, optical transduction or readout, acquisition modality, and deployment context. Extraction and enzyme-coupled assays can define recovered NADPH, oxidized nicotinamide adenine dinucleotide phosphate (NADP+), total NADP(H), or a derived ratio, albeit at the cost of subcellular information. By contrast, protein-based, genetically encoded, and chemigenetic systems enable reversible, compartment-addressable measurements of sensor-accessible cofactor binding, ligand-dependent assembly, relay output, or NADPH/NADP+ balance; however, quantitative interpretation remains contingent on affinity, sensor abundance, pH, maturation, calibration, and sensor-induced buffering. Reaction-based probes offer the broadest spectral and imaging flexibility—including ratiometric, near-infrared, two-photon, and photoacoustic formats—yet most rely on hydride-transfer chemistry shared by reduced nicotinamide adenine dinucleotide (NADH) and NADPH. Absent matched kinetics and simultaneous mixed-cofactor experiments, NAD(P)H-responsive remains the most defensible designation for these platforms. Label-free autofluorescence, fluorescence lifetime imaging microscopy (FLIM), and phasor analysis preserve native spatial context; even so, they report composite intensity, binding-state, or metabolic contrast rather than a universal absolute NADPH concentration. Across modalities, rigorous interpretation requires physiologically relevant concentration ranges, product or binding-mechanism verification, matrix- and organelle-specific controls, time-resolved calibration, and orthogonal measurements of pool size or flux. By aligning each signal-generating event with the endpoint it can legitimately support, the review establishes a mechanistic basis for platform selection and for interpreting NADPH-related optical changes across purified systems, cells, tissues, and biofluids.

Chemosensors doi: 10.3390/chemosensors14090209

Authors: Donghao Xie Mengna Zhou Huan Liu Xianlei Chen Jianghuan Shi Yinbao Cheng

X-ray fluorescence (XRF) chlorine analyzers are widely used for chlorine determination in crude oil and petroleum products. Although analytical test procedures are standardized, a calibration-oriented uncertainty framework for the analyzer itself remains insufficiently developed. This study establishes a metrological framework for relative indication error and limit of detection (LOD), supported by repeatability and linearity evaluations, and compares uncertainty propagation using the Guide to the Expression of Uncertainty in Measurement (GUM) and the Monte Carlo method (MCM). For relative indication error, residual matrix mismatch was quantified using paired isooctane-based light-oil and 75 cSt mineral-oil reference materials and incorporated with measurement repeatability and certified-value uncertainty. At 100, 300 and 500 mg/L, GUM and MCM produced closely agreeing standard uncertainties of approximately 0.0115, 0.0103 and 0.0081, respectively. For LOD, both methods used the same dual-axis error-weighted regression, accounting for uncertainties in certified concentrations and mean instrumental responses; in the MCM, the regression was repeated for every simulated calibration data set. The resulting standard uncertainties were 0.0048 mg/L (GUM) and 0.0047 mg/L (MCM), and the MCM equal-tailed 95% coverage interval was 0.0122–0.0307 mg/L. The framework’s distinctive value is the consistent treatment of calibration measurands, matrix transfer and dual-axis error-weighted regression across both propagation methods. It provides an auditable GUM budget for routine calibration and certificate reporting, while MCM offers a verification route when repeated regression, finite-sample blank dispersion or asymmetric output intervals are important.

Chemosensors doi: 10.3390/chemosensors14090208

Authors: Li-Ke Wang Jie-Kai Zhu Xin-Yu Pei Ke-Da Chen Shao-Hui Wang Dan-Dan Zhu Feng-Geng Li Zhen-Zhen Meng Tong-Yu Lin Xin-Ru Chen Yu-Bao Lan

Chiral covalent organic frameworks (CCOFs) merge the structural versatility of covalent organic frameworks with chirality, offering a powerful platform for enantioselective electrochemical sensing. This review comprehensively summarizes recent advances in CCOF-based sensors, focusing on signal transduction mechanisms. Three primary synthetic strategies (direct synthesis, post-synthetic modification, and chiral induction) and four electrode immobilization approaches (drop-casting, in situ growth, membrane fabrication, and nanopipette modification) are outlined and critically assessed with respect to their advantages and limitations. Based on photon involvement, the sensing applications are categorized into three operational modes: pure electrochemical, electrochemiluminescence (ECL), and photoelectrochemical (PEC) sensing. For each mode, the design principles, analytical performance, recognition mechanisms, and structure–property relationships are discussed. Current challenges and future directions are examined, spanning from analyte scope expansion and mechanistic elucidation to conductivity enhancement, aqueous stability, fabrication–performance correlations, multi-mode synergistic platforms, and real-sample validation. This review provides a coherent framework for understanding CCOF-based enantioselective electrochemical sensing and perspectives for future development.

Chemosensors doi: 10.3390/chemosensors14090207

Authors: Tingting Wang Baoshuai Xing Yingming Xu

Traditional tin dioxide (SnO2) gas sensors suffer from high operating temperatures and poor sensing response toward ammonia (NH3) at room temperature. Herein, flower-like CeO2/SnO2 n-n heterostructures are fabricated via a facile one-pot solvothermal route followed by high-temperature calcination for ppm-level NH3 detection at room temperature. The CeO2/SnO2 heterostructure calcined at 800 °C presents porous flower microspheres assembled by nanoparticles. Gas-sensing measurements demonstrate that the CeO2/SnO2-800 sensor delivers a high response of 6.5 toward 50 ppm NH3 at room temperature. Moreover, the sensor exhibits outstanding selectivity, reproducibility, and long-term stability over 60 days. Combined XPS, in situ FT-IR, O2-TPD, and EPR characterizations verify that the construction of n-n heterojunctions and abundant oxygen vacancies synergistically boost the concentration of surface-adsorbed oxygen species, thus improving the NH3 sensing performance. The CeO2/SnO2 heterostructure in this work offers a promising strategy for developing low-power room-temperature NH3 gas sensors.

Chemosensors doi: 10.3390/chemosensors14090206

Authors: Giulia My Angiola Forleo Antonio V. Radogna Giuseppe Grassi Maria Teresa Cimmarusti Marco Fiorentino Alessandra Stasi Rossana Franzin Monica Campioni Stefania Rotella Loreto Gesualdo Pietro Siciliano Simonetta Capone

Chronic kidney disease (CKD) is associated with metabolic alterations that may affect the volatile organic compound (VOC) profiles of biological fluids. Electronic nose systems capture complex VOC patterns as characteristic sensor-response fingerprints rather than identifying individual VOCs, thereby providing a global representation of the underlying metabolic state and enabling disease assessment and stratification. In this pilot study, a prototype electronic nose (SPYROX) was applied to blood and urine samples collected from healthy individuals and patients with moderate or advanced CKD. Sensor responses were analyzed using partial least squares–discriminant analysis (PLS-DA) to assess the ability of the sensor array to discriminate different stages of renal impairment. PLS-DA models built from blood and urine sensor responses achieved cross-validated classification accuracy of 76.9% and 68.7%, respectively. Distinct sensor-response fingerprints associated with CKD status and severity were identified, while variable importance analysis highlighted the sensors contributing most strongly to class discrimination. Significant associations were observed between sensor responses and clinical parameters, including hemoglobin (Hb), serum creatinine (sCr), serum urea (sUrea), and estimated glomerular filtration rate (eGFR). Regression models achieved coefficients of determination ranging from 0.675 to 0.884, with the strongest prediction obtained for serum creatinine (R2 = 0.884, p = 0.003). These findings support the feasibility of electronic nose-based volatile fingerprinting as a rapid and minimally invasive approach for CKD stratification. Although limited by cohort size, this proof-of-concept study provides a foundation for future validation in larger and independent patient populations.

Chemosensors doi: 10.3390/chemosensors14090205

Authors: Ibrahim Uyanik Arzu Uyanik

A calix[4]arene-based fluorescent chemosensor containing two quinolinium units (C4-BisQ) was synthesized and evaluated for the selective detection of cyanide (CN−). C4-BisQ showed a dual-mode optical response with a green-to-blue color change under ambient light and fluorescence quenching upon CN− addition in CH3CN/H2O (9:1, v/v). UV–Vis and fluorescence titration studies indicated a 1:1 interaction between C4-BisQ and CN− with an association constant of 1.08 × 105 M−1. The limits of detection were 0.080 µM by ratiometric UV–Vis analysis and 0.54 µM by fluorescence spectroscopy. C4-BisQ was highly selective for CN− in the presence of common competing anions and metal cations such as F−, Cl−, Br−, I−, ClO4−, NO3−, H2PO4−, HSO4−, Ac−, and Li+, Na+, Mg2+, Ca2+, Mn2+, Fe3+, Co2+, Zn2+, Cd2+, Al3+. Spectroscopic data suggest that cyanide recognition mainly involves deprotonation and is accompanied by fluorescence quenching under the applied conditions. Recovery experiments in tap water and seawater, together with visual detection in apricot and peach kernel extracts, confirmed the applicability of the sensor. Overall, C4-BisQ offers a simple dual-mode approach for cyanide detection in environmental water and food-related samples.

Chemosensors doi: 10.3390/chemosensors14090204

Authors: Diego Escobar-Olivos Simón Castro-Volpato Paul Jara Marisol Gómez Carlos Rojas-Romo

The increasing use of germanium in advanced technologies and its potential release into aquatic environments demand sensitive and accessible analytical methods for its determination at trace levels. This work reports the development of an adsorptive stripping voltammetric method for Ge(IV) determination in freshwater samples using an ex situ bismuth film electrode and quercetin as a complexing agent. The stoichiometry of the Ge(IV)–quercetin complex was established as 1:2 by UV-Vis spectrophotometric titration and non-linear least-squares fitting. The effect of the sulfonic substituent was evaluated by comparing quercetin with quercetin-5′-sulfonic acid under otherwise identical conditions. The method was optimized with respect to pH, buffer concentration, ligand concentration, accumulation potential and time, and electrode cleaning conditions. Under optimized conditions, the method exhibited a linear range of 0.23–24.4 µg L−1 (3.17–335.9 nmol L−1), a limit of detection of 0.08 µg L−1 (1.10 nmol L−1), and a repeatability of 4.41%. The higher sensitivity achieved with quercetin relative to quercetin-5′-sulfonic acid was attributed to the neutral charge of the Ge(IV)–quercetin complex, which favors adsorption at the electrode surface. The method was successfully applied to groundwater, river water, and tap water samples with relative errors below 5.0%, demonstrating its suitability for environmental water analysis.

Chemosensors doi: 10.3390/chemosensors14090203

Authors: Chong Li Kexin Yan Jia Yang Xinxin Wang Fengqin Wang Zeyu Wang Qianjin Song Jiaheng Zhang

A red-emissive, derivatization-assisted aptasensor was developed for acrylamide analysis in food. Acrylamide was first converted into the xanthydrol–acrylamide adduct (XAA), which was recognized by the DNA aptamer AptXAA. A π-conjugation-extended thioflavin T analogue, ThT-Red, was synthesized as the fluorescent signal molecule. ThT-Red exhibited an emission maximum at 600 nm and showed enhanced fluorescence upon binding to AptXAA, attributable to restricted intramolecular motion. XAA binding perturbed the ThT-Red–AptXAA interaction, producing a concentration-dependent fluorescence decrease consistent with competitive displacement. Following a 30 min xanthydrol derivatization step, the fluorescence sensing response was obtained within 3 min. Under optimized conditions, the sensor showed a linear response to XAA over 0–2.5 μM, with a limit of detection of 0.23 μM. Recoveries of 90.14–119.72% were obtained in spiked potato-chip extracts. The sensing response was also measurable using a portable fluorometer, supporting its potential for rapid on-site analysis. This work demonstrates that structural engineering of an aptamer-responsive molecular rotor can provide an effective red-fluorescence transduction strategy for derivatization-assisted detection of low-epitope food contaminants.

Chemosensors doi: 10.3390/chemosensors14090202

Authors: Marc Henry Jean Cumps Michel Van Wassenhoven Martine Goyens

Background: The characterisations of highly diluted preparations impregnated onto solid carriers remains analytically challenging, particularly when residual material is present at very low levels. Electrophotonic analysis (EPA), based on corona-discharge imaging and quantitative image analysis, has recently been explored as a possible complementary approach, but its applicability to impregnated pilules requires further investigation. Purpose: This exploratory study examined whether EPA-derived image parameters could reveal measurable differences among size-6 pilules impregnated with stepwise diluted and dynamised preparations of Cuprum metallicum and Gelsemium sempervirens and whether such differences might vary according to source material and manufacturing protocol. Methods: Size-6 pilules were impregnated with Hahnemannian and Korsakovian preparations of Cuprum metallicum and Gelsemium sempervirens. Control samples included non-impregnated pilules, pilules impregnated with pure solvent, and pilules impregnated with simply diluted, non-dynamised preparations. Images were acquired in randomised and blinded conditions using a prototype EPA device. Image intensity, contrast, entropy, and fast Fourier transform (FFT)-derived period/spatial-frequency parameters were analysed. Results: The EPA images showed patterns that may indicate differences between impregnated pilules and controls, between the two source materials, and between manufacturing protocols under the experimental conditions used here. FFT-based analysis suggested that some spatial-period/spatial-frequency indices may contribute to describing these preliminary differences. Among the parameters examined, the smallest diameter of the selected high-intensity FFT-image regions remained statistically significant after correction for multiple comparisons, whereas other parameters should be interpreted cautiously. Observations of aged samples also suggested possible differences from reference preparations, but these findings remain preliminary and require confirmation. Conclusions: These results suggest that EPA combined with FFT-based image analysis may provide exploratory information on EPA-derived image patterns in potentised preparations impregnated onto solid carriers. However, the present data are not sufficient to establish EPA as a reliable discriminatory analytical tool. Further measurements, larger sample sets, independent replication, improved control of environmental variables, and comparison with complementary analytical methods are required before firm conclusions can be drawn about its analytical value.

Chemosensors doi: 10.3390/chemosensors14090201

Authors: Dawei Zhang Lijin Zhong Sijie Lin Jie Bao

To address the quantitative distortion problem caused by the primary internal filtration effect (PIFE) resulting from coexisting substances in the fluorescence detection of aromatic pollutants, this study proposes a multi-component concentration quantification correction model that integrates transmittance absorbance and lateral (90°) fluorescence intensity. Using styrene as the target analyte and anthracene/phenanthrene as representative interferents, by establishing a coupled framework of excitation light decay dynamics and fluorescence emission, the nonlinear PIFE problem was transformed into a linear regression task. The experimental results show that: under the coexistence of anthracene and phenanthrene, the model reduces the detection deviation of styrene from 40 to 63% to within 10%, and the correction accuracy of the three-component mixed system is increased by 3–5 times. This work provides a theoretical framework for fluorescence quantitative analysis in complex systems, and also offers a new method for high-precision online monitoring of aromatic organic pollutants.

Chemosensors doi: 10.3390/chemosensors14090200

Authors: Huahao Tang Wei Qu Jiahua Su Muzhi Li Huili Hao

In this study, an electrochemical sensor based on a graphitized carboxylated multi-walled carbon nanotube/polyaniline (G-COOH-MWCNTs/PANI) composite was developed for the highly sensitive detection of Pb2+ in seawater. A G-COOH-MWCNTs/PANI composite dispersion was prepared via a solution blending method and subsequently drop-cast onto a glassy carbon electrode (GCE) to fabricate the modified electrode. Differential pulse anodic stripping voltammetry (DPASV) was employed for the quantitative determination of Pb2+. The morphology of the composite was characterized by scanning electron microscopy (SEM), while the electrochemical behavior of the modified electrode was investigated using cyclic voltammetry (CV) and differential pulse voltammetry (DPV). Critical experimental parameters, including the type and pH of the supporting electrolyte, deposition potential, deposition time, and loading amount of the composite film, were systematically optimized. In addition, the optimal concentration ratio of G-COOH-MWCNTs to PANI was determined using an orthogonal experimental design. Under the optimized experimental conditions, the proposed sensor exhibited a linear response toward Pb2+ over the concentration range of 25–220 μg/L, with the regression equation Ip = 2.757C + 4.316 (R2 = 0.997). The limit of detection (LOD), calculated at a signal-to-noise ratio (S/N) of 3, was 0.0337 μg/L. The sensor also demonstrated excellent reproducibility (relative standard deviation, RSD = 1.7%), satisfactory anti-interference capability, and good long-term stability, retaining 94.1% of its initial response after 35 days of storage. Spike recovery experiments using real seawater samples yielded recoveries ranging from 96.73% to 99.73%, with RSD values below 3%, indicating excellent accuracy and precision in complex seawater matrices. These results demonstrate that the proposed sensor enables accurate determination of Pb2+ in seawater without complicated sample pretreatment and exhibits considerable potential for applications in marine environmental monitoring of heavy metal contamination.

Chemosensors doi: 10.3390/chemosensors14090199

Authors: Francesco Lavecchia di Tocco Anna Rita Bizzarri

MicroRNAs (miRNAs) represent highly promising biomarkers for non-invasive clinical diagnostics. To overcome the limitations of conventional methods, Field-Effect Transistor biosensors (bioFETs) emerge as compelling alternatives by providing label-free and real-time readouts. However, bioFETs employing canonical DNA/RNA probes face fundamental limitations due to the Debye screening effect. Peptide Nucleic Acid (PNA), a synthetic nucleic acid analog featuring an uncharged peptide-like backbone, offers an effective solution. PNA’s electroneutrality allows for efficient hybridization at a low ionic strength, thereby mitigating Debye screening constraints. PNA also offers additional key advantages, including high specificity and enzymatic resistance. This work examines recent advancements in PNA–bioFETs, highlighting their potential for the development of robust platforms for clinical miRNA evaluation. Surface Plasmon Resonance (SPR) and Atomic Force Spectroscopy (AFS) are valuable tools to preliminarily characterize probe–target interactions. Key approaches, focused on implementing alternative PNA probe designs and optimizing Self-Assembled Monolayer (SAM) organization, are outlined. By presenting a cross-study comparison that correlates each strategy with the analytical outcomes of a custom-designed bioFET platform targeting miRNA 155, complemented by an overview of PNA–bioFETs across other miRNAs, we illustrate how the synergy between PNA and bioFET can approach reliable miRNA detection, paving the way for the development of Point-of-Care (POC) devices.

Chemosensors doi: 10.3390/chemosensors14090198

Authors: Brankica Kartalovic Jasmina Vidic

Micro- and nanoplastics have emerged as persistent, mobile water contaminants capable of carrying co-pollutants such as phthalates, polycyclic aromatic hydrocarbons and antibiotic-resistant pathogens, yet detection methods for this size range remain fragmented across classical analytical chemistry and a rapidly diversifying set of electrochemical, optical and biosensor platforms. This review compiles and classifies 53 original research articles published between 2021 and mid-2026, identified through a PRISMA-guided search of Google Scholar and ScienceDirect, covering detection principle, sensor type, sample matrix, target polymer, water source and particle size class. Optical techniques account for over half of the studies, mass spectrometry and electrochemical sensors divide most of the remainder, and biosensors show the fastest growth in recent years. Water dominates as the tested matrix, but seven in ten water-based studies rely on laboratory-spiked or unspecified samples rather than named environmental water, and nearly half now address the nanoplastic fraction. These findings are interpreted alongside contamination pathways, including atmospheric deposition, and against current EU and WHO frameworks, which place microplastics and antimicrobial resistance indicators on a watch list pending harmonised testing rather than binding limits. The review identifies matrix realism, nanoplastic capability and monitoring frequency as the central bottlenecks separating current detection science from field-ready water monitoring.

Chemosensors doi: 10.3390/chemosensors14090197

Authors: Ismael Montero Fernández Mario Figueras Corrochano Víctor Manrique Fernández Selvin Antonio Saravia Maldonado Daniel Martín-Vertedor

The characterization of plant-derived extracts is strongly influenced by the extraction procedure and solvent polarity, which determine the recovery of bioactive compounds and volatile constituents. In this study, an electronic nose (E-nose) based on a metal oxide semiconductor (MOS) sensor array was evaluated as a rapid analytical tool for the classification of Cistus extracts obtained using solvents of different polarity. Leaves of Cistus salviifolius, Cistus crispus, and Cistus ladanifer were sequentially extracted with hexane, diethyl ether, tetrahydrofuran (THF), chloroform, ethanol, and methanol. Extraction yield, total phenolic content, flavonoid content, and antioxidant activity were determined, and volatile fingerprints were acquired using the E-nose system. Chemometric analysis was performed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Differences were observed among extraction solvents regarding extraction efficiency, phytochemical composition, and antioxidant activity. PCA successfully discriminated extracts according to solvent polarity, explaining between 70.96% and 90.22% of the total variance depending on the Cistus species analyzed. Furthermore, the PLS-DA model achieved a classification accuracy of 87.5%, with sensitivity and specificity values of 87.5% and 97.5%, respectively. These results demonstrate that the combination of E-nose technology and chemometric tools provides a rapid, non-destructive, and cost-effective approach for the classification and characterization of Cistus extracts according to extraction solvent, highlighting its potential application in quality control and process monitoring of plant-derived products.

Chemosensors doi: 10.3390/chemosensors14090196

Authors: Khursheed Ahmad Shanmugam Vignesh Rohit Kumar Singh Gautam Sanjeevamuthu Suganthi Vivek Mani Tripathi Tae Hwan Oh

Triethylamine (TEA) is a common industrial contaminant and an important indicator of seafood spoilage. Therefore, determination of rapid and selective TEA is of great significance. This review article critically compares tungsten oxide (WO3)- and molybdenum oxide (MoO3)-based chemiresistive sensors by relating their crystal structure, surface chemistry, defect states, morphology, and interfacial electronic properties to TEA-sensing performance. Pristine WO3- and MoO3-based sensors generally operate at approximately 133–325 °C and provide sub-ppm detection, whereas doping, noble-metal sensitization, heterojunction formation, and light activation can reduce the operating temperature to 100–180 °C and extend detection into the low-ppb range. WO3-based sensors have exhibited a response of 1100 to 20 ppm TEA at 160 °C, with an estimated detection limit of 5 ppb, whereas modified MoO3-based sensors have also achieved decent detection limit of 1.7 ppb. WO3 is particularly responsive to phase, facet, work-function, and catalytic-interface engineering, whereas α-MoO3 benefits from its anisotropic structure, variable Mo valence, and favorable Lewis acid–base interactions with amines. Noble metals enhance gas sensing through catalytic and electronic sensitization, dopants regulate adsorption and defect chemistry, and n-n or p-n heterojunctions amplify resistance changes through depletion-layer modulation. Despite considerable advances in sensitivity, humidity interference, high power consumption, slow recovery, baseline drift, and limited long term stability remain unresolved. Future advances may require standardized performance assessment, operando mechanistic studies, humidity-resistant low-power devices, and validation under realistic seafood-storage and industrial conditions.

Chemosensors doi: 10.3390/chemosensors14090195

Authors: Ashwini Dantanarayana Gymama Slaughter

Hemoglobin (Hb) and its glycated variant (HbA1c) are essential clinical biomarkers for diagnosing anemia, acute hemorrhage, and long-term glycemic control. The growing demand for decentralized point-of-care (POC) testing has accelerated the development of rapid, inexpensive, and portable electrochemical sensing platforms. These technologies exploit the intrinsic redox activity of the heme prosthetic group, yet achieving efficient direct electron transfer (DET) remains a fundamental challenge because the electroactive iron center is deeply embedded within the globin structure. This review critically examines recent advances in electrochemical Hb sensing, tracing the evolution of electrode architectures from conventional carbon substrates to nanostructured materials, including graphene, MXenes, metal–organic frameworks (MOFs), and molecularly imprinted polymers (MIPs). We compare the advantages and limitations of non-enzymatic biomimetic platforms and affinity-based sensing strategies, including aptamer- and antibody-based biosensors, with emphasis on electron-transfer efficiency, molecular selectivity, analytical performance, and suitability for POC implementation. Beyond analytical performance, we evaluate the principal barriers to clinical translation, including biofouling, whole-blood matrix effects, viscosity-dependent mass transport, manufacturing scalability, and the persistent gap between validation in synthetic media and performance in clinical samples. Finally, we discuss emerging applications in wearable menstrual health monitoring and ingestible gastrointestinal bleeding sensors, highlighting the integration of advanced electrochemical materials with miniaturized electronics as a pathway toward practical, consumer-oriented diagnostics.

Chemosensors doi: 10.3390/chemosensors14090194

Authors: Ganesan Punithakumari Govindhasamy Murugadoss Shaik Ashmath Tae-Gwan Lee

A novel anthracene-based receptor was synthesized via a facile condensation method. The anion-sensing behavior of the receptor was systematically investigated using colorimetric analysis, UV–visible spectroscopy, photoluminescence studies, and binding constant calculations. The synthesized receptor exhibited effective and selective recognition of cyanide (CN−) ions, accompanied by a distinct visual color change from pale yellow to light pink, whereas other tested metal ions produced no appreciable color response. Notably, in the presence of CN− ions, the receptor displayed a pronounced and selective response toward Fe3+ ions, as evidenced by a subsequent color change from light pink to light yellow. Furthermore, the receptor enabled the construction of implication (IMP) and inhibition (INH) logic gates through the selective detection of CN− and Fe3+ ions. The detection limit for CN− ions was determined to be 7.3 × 10−7 M. The practical applicability of the receptor for CN− detection was further demonstrated using sprouted potato samples and optical sensing platforms. In addition, the receptor exhibited excellent sensing performance and was successfully applied for the fluorescence labeling and imaging of CN− ions in living biological cells.

Chemosensors doi: 10.3390/chemosensors14090193

Authors: Sylvain Thevenot Patrick Salagnac Patrick Glouannec Jean-François Feller

Conductive polymer nanocomposites (CPC) are widely investigated as chemo-resistive materials for the detection of volatile organic compounds (VOC). However, the physical mechanisms governing their transient electrical response remain only partially understood, limiting the development of predictive models and highly selective sensors. In this work, the chemo-resistive behaviour of carbon nanoparticle-filled poly(ethylene-co-ethyl acrylate) (EEA-CNP) was investigated through a multiphysics experimental approach combining simultaneous measurements of solvent uptake, dimensional changes, temperature and electrical resistance during toluene sorption and desorption. Thick specimens were deliberately employed to amplify transient diffusion phenomena and enable direct observation of the coupling between mass transport, polymer swelling and conductive network evolution. The results demonstrate that electrical resistance cannot be interpreted solely from the average solvent concentration within the material. Instead, the transient response is primarily governed by solvent concentration gradients, which continuously modify the connectivity of the conductive nanoparticle network during diffusion. This mechanism explains the pronounced hysteresis observed between sorption and desorption, the transient resistance overshoot during sorption, and the absence of a unique relationship between resistance and solvent content under dynamic conditions. A dedicated quasi-static desorption protocol was therefore developed to minimise concentration gradients and establish the intrinsic correlation between electrical resistivity and solvent fraction. The experiments further show that a solvent content of approximately 6 wt% is sufficient to completely disrupt the conductive percolation network. These findings provide new insights into the multiphysics mechanisms governing chemo-resistive sensing and establish an experimental basis for the development and validation of predictive models for conductive polymer nanocomposites. The proposed methodology is expected to contribute to the optimisation of next-generation VOC sensors and electronic noses with improved selectivity and predictive capability.

Chemosensors doi: 10.3390/chemosensors14090192

Authors: Yelyzaveta Bazalieieva David C. Magri

Two 4-piperazine-1,8-naphthalimides were designed as fluorescent probes for exploring the local polarity and proton concentration at the interface of micelles. Designed with an anchor1-fluorophore-spacer-receptor-anchor2 layout, the hydrophobicity of the pH probes was tuned by substitution of the anchor modules with different alkyl chains (methyl, butyl and octyl) to facilitate the micellar penetration depth. Fluorescence switching ‘on’ in methanol/water media upon protonation of the piperazine receptor is driven by a competition between solvent polarity and photoinduced charge transfer. The solvatochromic properties were investigated to enhance visual naked-eye communication. The fluorescent probes were tasked with reporting on the local polarity and proton content within sodium dodecyl sulphate (SDS), cetyltrimethylammonium chloride (CTAC) and Triton X-100 micelles. The dimethyl-substituted probe reported on the environment about the micelle/bulk water interface. The more hydrophobic octyl-butyl-substituted probe explored deeper into the micelles. Insight was gained into the local polarity and proton gradients inside micelles, as corroborated by emission wavelength, fluorescence quantum yield and ΔpKa values. The findings are discussed in the context of the benzofurazan polarity–ΔpKa maps reported by Uchiyama and de Silva.

Chemosensors doi: 10.3390/chemosensors14090191

Authors: Honghong Rao Jiayin Wang Kehui Zhang Zhonghua Xue

Existing colorimetric sensing strategies based on gold nanoparticles (AuNPs) are largely limited to the detection of free metal ions, leaving metal–organic complexes widely present in real water bodies undetectable. Herein, we develop a simple colorimetric method based on the anti-aggregation effect of bare AuNPs for the simultaneous detection of free Cr3+ and its organic complexes. Tetrabutylphosphonium bromide (TPB) dissociates into phosphonium cations ([P(C4H9)4]+), which neutralize the negatively charged surface of AuNPs via electrostatic attraction, thereby inducing particle aggregation and a color change from wine-red to blue. Under strongly alkaline conditions, Cr3+ converts to negatively charged [Cr(OH)6]3− species. These anionic species then compete with the AuNPs for the phosphonium cations, blocking the aggregation process and preserving the wine-red color. This competitive mechanism is further extended to the detection of Cr3+-EDTA, Cr3+-tartrate, and Cr3+-oxalate complexes, as they also transform into [Cr(OH)6]3− under alkaline conditions. Without the need for tedious surface modification, the proposed method is simple to operate and exhibits excellent sensitivity and selectivity for both free and complexed Cr3+ in real water samples. It thus provides a reliable analytical tool for monitoring chromium pollution in environmental waters.

Chemosensors doi: 10.3390/chemosensors14090190

Authors: Ramona Russo Alberto Bottacin Giuseppina Arcamone Francesca Durbiano Chiara Musacchio Stefano Pavarelli Anna Pellegrino Francesca Romana Pennecchi Michela Sega Francesca Rolle Fabio Favoino

This paper presents the metrological characterization of low-cost sensors integrated into a multi-sensor system for Indoor Environmental Quality monitoring, developed within the MIRABLE project. The analysis focuses on two domains: the thermal domain, using Sensirion SHT45 and SEN55 temperature sensors; and the Indoor Air Quality (IAQ) domain, using an Infineon photoacoustic spectroscopy (PAS)-based sensor for carbon dioxide (CO2). All tests were conducted under controlled laboratory conditions using calibrated reference instruments. In the thermal domain, the influence of sensor integration within the device case was investigated at temperature (T) between 15 °C and 35 °C and relative humidities (RH) between 30 %rh and 60 %rh. The results revealed self-heating effects in the desk unit, causing temperature biases of up to 0.6 °C. In the IAQ domain, the repeatability and the impact of T and RH on CO2 measurements were evaluated. T was identified as the main influencing factor; whereas, RH had a negligible effect. These results were supported by statistical analysis ANOVA. A correction strategy based on concentration intervals is proposed for operation between 15 °C and 25 °C, with an expanded uncertainty (k = 2) of (3.26–6.42) ppm for the with-case configuration. These results support the reliable use of the MIRABLE system.

Chemosensors doi: 10.3390/chemosensors14090189

Authors: Min-Kun Kim Ku Kang Shin Hum Cho Yoon Jeong Jang Soohwan Kim Jin Yoo Myeongsik Shin Sungbong Kim Doo-Hee Lee

Real-time detection of chemical warfare agents (CWAs) and toxic industrial chemicals underpins military protection, counter-terrorism, and emergency response. Yet field instruments usually fail for a reason unrelated to sensitivity: they cannot identify agents that are not already in their reference libraries, such as novel analogs, mixtures, and degradation products. We argue that this unknown-agent problem is a structural limitation of single-modality sensing, because any one class of information (molecular bonds, ion mobility, elemental composition, or chemical reactivity) is rarely sufficient to resolve an unfamiliar threat. We review the dominant field modalities, including FTIR, Raman/SERS, ion mobility and field-asymmetric ion mobility spectrometry, laser- and spark-induced plasma spectroscopy, metal-oxide sensor arrays, and portable mass spectrometry, and show that their weaknesses are largely complementary. We then set out the principle of orthogonal multimodal sensing, in which complementary information axes are combined by machine learning with anomaly and open-set detection so that unfamiliar agents are recognized as such rather than misidentified. Four hybrid architectures are critically compared, and we examine spark-induced decomposition diagnostics, consumable-free self-decontaminating field systems with edge AI, and the open challenges of standardized datasets, calibration transfer, and validation, before outlining a roadmap toward field-relevant recognition of unidentified chemical threats.

Chemosensors doi: 10.3390/chemosensors14080188

Authors: Bo Tang Huiling Zhao Shan He Lin Zeng Bin Zhang

Tapered optical fiber-based surface-enhanced Raman scattering (SERS) probes have emerged as promising miniaturized platforms for chemical and molecular sensing by integrating optical excitation, plasmonic enhancement, and Raman signal collection within a single fiber architecture. Their enhanced light–matter interaction, compact geometry, and remote interrogation capability make them particularly attractive for in situ sensing in confined and complex environments. This review systematically examines recent advances in tapered optical fiber SERS probes, covering enhancement mechanisms, taper fabrication, plasmonic hotspot engineering, and analytical applications. Particular emphasis is placed on how taper geometry and plasmonic nanostructure organization jointly influence sensing performance. Fabrication and hotspot-engineering strategies are critically compared in terms of sensitivity, reproducibility, stability, fabrication complexity, and scalability. Representative applications in biomedical analysis, food safety, and environmental monitoring are further evaluated. Despite these advances, practical implementation remains constrained by insufficient hotspot reproducibility, quantitative reliability in complex matrices, long-term stability and antifouling performance, as well as the limited scalability of current fabrication protocols. Future progress will require balancing analytical sensitivity with reproducibility, robustness, and real-sample compatibility, while advancing deterministic hotspot engineering, selective recognition interfaces, standardized performance evaluation, intelligent spectral analysis, and Lab-on-Fiber integration. Together, these developments could accelerate the transition of tapered optical fiber SERS from laboratory-scale demonstrations to field-deployable platforms for remote and in situ molecular sensing.

Chemosensors doi: 10.3390/chemosensors14080187

Authors: Xueya Song Jing Yang Shunrun Zhang Dongyun Zheng

Molecularly imprinted polymers combined with carbon nanomaterials have proven effective in constructing electrochemical sensors with high selectivity, sensitivity, and robustness. Herein, a polypyrrole-based molecularly imprinted electrochemical sensor was developed on a multi-walled carbon nanotube-modified glassy carbon electrode for homocysteine detection in human serum. The sensor was fabricated via drop-coating of sodium dodecyl sulfate-dispersed multi-walled carbon nanotubes, followed by in situ electropolymerization of pyrrole using homocysteine as the template. The morphology, interfacial properties, and electrochemical behavior of the electrode were systematically characterized by scanning electron microscopy and electrochemical techniques. Under optimized conditions, the sensor showed a linear response to homocysteine in the range of 1.0 × 10−10 mol/L to 1.0 × 10−5 mol/L, with a detection limit of 7.12 × 10−11 mol/L (S/N = 3). The sensor also exhibited good selectivity against common interferents, as well as acceptable reproducibility and stability. Recovery tests in human serum yielded recoveries of 91.00~110.50% (average: 100.73%), demonstrating its potential for practical homocysteine analysis in complex biological matrices.

Chemosensors doi: 10.3390/chemosensors14080186

Authors: Xingkui Tao Fangkai Han Leiming Yuan

This study evaluates calibration strategies for predicting soluble solids content (SSC) in Ambrosia apples using a low-cost, portable short-wave near-infrared (SW-NIR) spectrometer (640–1050 nm) under interactance acquisitions. Replicate sampling spectral curves exhibited notable variability due to asymmetric illumination, peel color heterogeneity, and probe contact inconsistencies. Regression models were comparatively built on averaged spectra compared with those trained directly on multiply sampled replicate spectra, applying piecewise Savitzky–Golay smoothing and detrending as pretreatment. Variable selection was performed via uninformative variable elimination (UVE) and backward interval partial least squares (BiPLS). Models calibrated on replicate spectra demonstrated superior generalization to unseen replicate measurements, despite slightly higher cross-validation errors. The BiPLS model on replicate spectra achieved the best predictive performance (mean RMSEP = 0.677 °Brix, Rp = 0.796, RPD = 1.656), with improved trueness (lower relative absolute bias) and precision (lower relative standard deviation). For comparison, the BiPLS model on averaged spectra yielded a mean RMSEP = 0.899 °Brix, Rp = 0.593, RPD = 1.25; the replicate-spectra strategy thus reduced the RMSEP by 24.7% and increased Rp and RPD accordingly. This suggests that for low-cost NIR instruments, using replicate sampling spectral modeling combined with interval variable selection can provide better prediction performance and achieve the purpose of on-site sorting in food quality analysis.

Chemosensors doi: 10.3390/chemosensors14080185

Authors: Ábel Zsubrits Éva Fekete Győző G. Láng

During the experiments presented in this work, the electrochemical synthesis of ferrate ions was performed from high-purity iron electrode in a 45% (m/m) aqueous NaOH solution at different temperatures. The synthesis process was investigated using dual dynamic voltammetry (DDV), which involves applying independent potential–time waveforms (dynamic potential programs) simultaneously to the disk and ring electrodes of a rotating ring–disk electrode (RRDE, Pt-ring—Fe-disk) setup. This innovative technique facilitates the instantaneous measurement of the concentration of ferrate ions generated at the disk electrode. The effect of temperature on ferrate ion formation was examined, and the optimal potential range and applied current density at various temperatures were determined to maximize ferrate ion production and current efficiency. The results indicate that the rate of both ferrate ion production and oxygen evolution increases with temperature within the investigated temperature range (15–45 °C). It was found that there is an optimal potential range at each temperature where ferrate ion formation occurs at the highest rate (limited by other factors). The maximum current efficiency was determined at each temperature, with the highest value obtained at approximately 35 °C.

Chemosensors doi: 10.3390/chemosensors14080184

Authors: Shikha Jain Sonam Kumari Aman Kumar Neeraj Dilbaghi Ganga Ram Chaudhary Giovanna Marrazza Sandeep Kumar

The widespread use of thiamethoxam, a neonicotinoid pesticide, poses increasing risks to environmental and food safety, highlighting the urgent need for rapid, selective, and biomass-derived analytical platforms. In this study, fluorescent carbon dots (CDs) were synthesized from rice straw via a water-based hydrothermal approach and subsequently functionalized with a biopolymer-based molecularly imprinted polymer (MIP) for the selective detection of thiamethoxam. The synthesized CDs exhibited strong blue fluorescence with a maximum emission wavelength at 455 nm. Integration of the CDs with a thin MIP layer generated specific binding cavities complementary to thiamethoxam, significantly enhancing selectivity toward the target analyte over structurally related neonicotinoids. The resulting CD@MIP sensor demonstrated rapid and concentration-dependent fluorescence quenching, with a wide linear detection range from 1 to 500 nM and a limit of detection of 9.15 nM. An imprinting factor of 3.54 confirmed the selective recognition capability of the imprinted system. The developed sensing platform was successfully validated using real environmental water samples, achieving recovery values between 100% and 102% with relative standard deviations ranging from 0.9% to 2.5%, thereby demonstrating good accuracy and reproducibility. Overall, this work presents a biomass-derived and efficient fluorescent sensing platform for monitoring pesticide residues, with promising applications in environmental monitoring and food safety analysis.

Chemosensors doi: 10.3390/chemosensors14080183

Authors: Wenxuan Lin Shuqiang Lyu Feng Gao Shuo Zhang Xiaoxuan Pan Hongying Zhao

The nondestructive detection of sodium chloride in mural plaster layers is important for assessing salt-related deterioration in cultural heritage materials. However, the weak and indirect spectral response of sodium chloride makes accurate hyperspectral detection challenging. This study developed a hyperspectral regression framework centered on Sparrow Search Algorithm (SSA) optimization, in which continuous wavelet transform (CWT) was used to construct multiscale spectral representations and Pearson correlation analysis combined with the Successive Projections Algorithm (PCC-SPA) was used for compact variable selection. Partial least squares regression (PLSR), support vector regression (SVR), extreme gradient boosting (XGBoost), SSA-optimized SVR, and SSA-optimized XGBoost were evaluated under nested stratified specimen-grouped five-fold cross-validation. Feature selection and hyperparameter optimization were independently performed within each outer training fold, whereas the held-out specimens were reserved for performance evaluation. SVR-SSA maintained high predictive capability across both conventional and multiscale spectral representations. SG + SNV yielded an R2 of 0.8167 ± 0.0690 and an RMSE of 0.3701 ± 0.0636 percentage points. Scale 6 CWT achieved closely comparable R2 and RMSE values of 0.8100 ± 0.0812 and 0.3731 ± 0.0767 percentage points, respectively, together with a lower MAE of 0.2788 ± 0.0620 percentage points. Among the ten CWT scales, Scale 6 achieved the highest mean prediction accuracy, whereas Scale 2 provided the best comprehensive balance between predictive accuracy and fold-to-fold stability. These results demonstrate that the effectiveness of SSA optimization depends on the input feature representation and that CWT provides scale-resolved information beyond a single conventional spectral representation. The proposed framework provides methodological support for the nondestructive quantitative assessment of NaCl-related deterioration in mural plaster materials and establishes a basis for further application in mural conservation.

Chemosensors doi: 10.3390/chemosensors14080182

Authors: Jiapei Cao Jie Tang Zhenlin Hu Jie Ouyang Ting Luo Junfei Nie

High-voltage wiring harnesses in new energy vehicles (NEVs) are susceptible to thermal aging in high-temperature environments, whereas conventional assessment methods are difficult to deploy rapidly. This study combines laser-induced breakdown spectroscopy (LIBS) with random forest (RF) classification to assess thermal aging levels in cross-linked polyethylene (XLPE) insulation. Thirteen laboratory-aged XLPE wiring harness samples were prepared, and 100 single-shot spectra were acquired at fresh positions for each aging level. The specific methodological contribution is the use of class-wise median absolute deviation (MAD) at each wavelength as a variable-selection criterion before RF training. Three models were compared: RF, principal component analysis (PCA) combined with RF (PCA–RF), and MAD combined with RF (MAD–RF). RF achieved 100% internal hold-out accuracy for the non-aged versus 60-day comparison and 84.23% across all 13 aging levels. PCA–RF increased the multiclass accuracy to 87.31%, whereas MAD–RF reached 95.00% under the original exploratory hold-out workflow. These results indicate that wavelength-wise robust dispersion can provide a compact, discriminative representation of the present LIBS dataset.

Chemosensors doi: 10.3390/chemosensors14080181

Authors: Axel Wolfram Elaheh Dana Tobias Schnabel Peter Kurzweil

The detection of organic carbon in wastewater is essential for process monitoring and regulatory assessment. Yet conventional chemical oxygen demand (COD) and total organic carbon (TOC) methods remain reagent-dependent, slow, and unsuitable for inline operation. Photoelectrochemical (PEC) sensing based on TiO2 offers a reagent-free alternative, but its response to wastewater-relevant dissolved organic matter (DOM) and real effluent matrices is still poorly understood. In this study, a TiO2-based PEC system was systematically evaluated using four representative model compounds—glucose, potassium hydrogen phthalate, L-tryptophan, and urea—covering major fractions typically present in municipal wastewater. For the first time, representative wastewater-associated organic compound classes, conductivity effects, and the transferability of the PEC response to real wastewater effluent were systematically investigated. The photocurrent response showed distinct, highly linear concentration–signal relationships for each substance, suggesting a dominant contribution of surface-associated electronic effects. Conductivity variations across a relevant range had no measurable influence on sensitivity or photocurrent magnitude, indicating that the PEC response is not governed by bulk ionic transport but primarily is an interfacial process at the site of TiO2. When applied to real wastewater effluent, the sensor exhibited an excellent linear correlation with dilution level (R2 = 0.9954), demonstrating a linear response within a defined matrix and an LOD of 1.12 mg L−1 COD. For the investigated model compounds, LOD values ranged from 1.06 to 3.00 mg L−1 COD, while a linear response was maintained up to approximately 80–100 mg L−1 COD. These findings establish TiO2-based PEC sensing as a promising platform for the reagent-free, online monitoring of organic loads in wastewater treatment.

Chemosensors doi: 10.3390/chemosensors14080180

Authors: Viktória Éles Haruna Gado Yakubu György Kövér Hedvig Fébel Róbert Romvári George Bazar

Electronic nose (EN) technology, based on electrochemical sensor arrays, has emerged as a rapid and objective tool for aroma characterization in food systems. This study investigated factors influencing cat food preference using physicochemical analysis, texture measurement, preference testing, and EN technology. Nine (9) commercial cat foods from three brands (A: premium; B and C: medium price) with different flavors were evaluated. Proximate analysis revealed no significant differences (p > 0.05) in most nutrients, except crude fiber (CF) (p < 0.05), which was higher in lower-priced cat foods. Shear force showed a positive correlation with consumption (r = 0.72), while CF was negatively correlated (r = −0.52). Preference tests indicated that Brand A was most preferred, followed by Brand C, while Brand B was least preferred. Principal component analysis (PCA) identified variability in individual cat preferences, and one outlier cat was excluded. Discriminant analysis of EN data showed clear separation by brand rather than flavor, suggesting brand-related aroma profiles can be monitored rapidly through the digital odor fingerprint. Cat food acceptance is mainly driven by texture, CF content, and aroma rather than macronutrient composition. The results of the EN evaluation of cat food samples revealed the same group similarities and differences as the 8-month preference test performed with cats. Similar to this study, EN classification models can be developed using food preference data and the digital aroma fingerprints of foods. After validations, the EN models can be used as an effective tool to monitor the quality and assess new diets according to the known preferred odor fingerprint.

Chemosensors doi: 10.3390/chemosensors14080179

Authors: Yijian Meng Jesper B. Christensen Carsten Thirstrup Lucia Ronda Rute Konstantinos Stergiou Danylo Komisar Oleksii Ilchenko Ditte Rask Tornby Thomas Emil Andersen Hüsnü Aslan Mikael Lassen

Raman spectroscopy combined with machine learning offers a rapid, label-free approach for bacterial identification, but robust translation remains challenged by spectral variability, biological heterogeneity, and limited model interpretability. Here, we present an integrated evaluation of an optimized Spectral Transformer (ST) framework for Raman-based bacterial classification benchmarked against a systematically optimized one-dimensional convolutional neural network (1D-CNN). The comparison was performed using a curated 36-class dataset comprising 15 Gram-negative bacterial entries, 15 Gram-positive bacterial entries, one non-bacterial microorganism, and five background/reference classes, enabling evaluation of both species-level and fine-grained bacterial classification. Under 15 dB noise-augmented evaluation, the ST achieved 80.6% ± 0.3% accuracy and a Matthews correlation coefficient (MCC) of 0.801 ± 0.003, outperforming the 1D-CNN baseline with 72.9% ± 0.3% accuracy and an MCC of 0.721 ± 0.003. Integrated Gradients analysis combined with attention map visualization enabled multi-level model interpretation, revealing that the ST’s improved robustness correlates with more bounded attribution patterns during misclassification, whereas the 1D-CNN’s feature attribution becomes scattered under noise perturbation. Importantly, this interpretability-driven analysis identified model-specific failure modes in the baseline architecture, including an over-reliance on non-specific spectral regions under noise, which can inform future data collection strategies and guide refinements to experimental protocols. These results demonstrate that attention-based spectral modeling improves Raman-based bacterial classification under noise-perturbed conditions while enabling multi-level interpretability that bridges model understanding with actionable feedback on experimental design and data quality requirements.

Chemosensors doi: 10.3390/chemosensors14080178

Authors: Jiangtao Tian Min Sun Zeyun Lu Xuexia Yang Deming Jiang Zhongyi Chang Hongliang Gao

Curdlan is an industrially important β-1,3-glucan with applications in the food, pharmaceutical, and biomaterial industries. However, the identification of high-yielding curdlan-producing strains is hindered by the absence of rapid and efficient screening methods. To address this limitation, we developed a systematically optimized integrated microscale workflow combining 48-well plate fermentation with a quantitative aniline blue-based colorimetric assay in 96-well plates for high-throughput screening of curdlan-producing strains. Fermentation was miniaturized using 48-well plates, while curdlan quantification was performed in 96-well plates through formation of a curdlan–aniline blue complex. Key parameters were systematically optimized. Under optimal conditions, curdlan dissolved in 1.0 mol/L NaOH was reacted with 2.0 mg/mL aniline blue in 0.5 mol/L phosphate buffer (pH 7.0) for 90 min, and absorbance was measured at 550 nm. The assay demonstrated excellent linearity between curdlan concentration and absorbance (y = 1.0008x + 0.1536, R2 = 0.9957, p < 0.001). To validate the method, curdlan yields from nine mutants derived from ATCC31749 were determined using both gravimetric and colorimetric approaches, revealing a strong correlation (R2 = 0.8683, p < 0.001), that confirmed the assay’s reliability for rapid screening. Application of this platform to 132 UV-mutagenized strains identified nine mutants with enhanced curdlan production. The best-performing strain, UV150824-02, produced 46.8 ± 0.08 g/L curdlan, an 11.4% increase over the wild-type strain ATCC31749 (41.6 ± 1.54 g/L), and maintained stable curdlan production over nine laboratory passages (coefficient of variation = 2.72%). This method significantly improves the efficiency of mutagenesis-based strain screening and provides a practical and efficient tool for accelerating strain improvement in industrial polysaccharide fermentation.

Chemosensors doi: 10.3390/chemosensors14080177

Authors: Guangyao Ying Huili Xia Jun Li

Tea quality is fundamentally determined by the complex biochemical transformations occurring during manufacturing. Traditional sensory evaluation, while essential, suffers from inherent subjectivity and cannot meet the demands of modern, industrial-scale production monitoring. This review critically examines the application of electronic nose (E-nose) technology throughout the tea-processing pipeline, covering multiple transducer technologies including metal oxide semiconductor (MOS) sensors, quartz crystal microbalance (QCM) sensors, conducting polymer (CP) sensors, and emerging chemiresistive platforms. Particular emphasis is placed on the E-nose’s capacity for real-time, non-destructive monitoring of key processing stages, including withering, rolling, fermentation, and drying. We analyze how optimized sensor arrays capture dynamic volatile organic compound (VOC) evolution, enabling the precise identification of optimal processing endpoints, especially in black tea fermentation control. Furthermore, this review evaluates how advanced pattern recognition algorithms (such as deep learning models) and multi-sensor data fusion strategies enhance the robustness and accuracy of process monitoring. By correlating E-nose response patterns with critical biochemical markers and traditional taster metrics, this paper demonstrates the technology’s pivotal role in transitioning tea manufacturing from experience-based craftsmanship to data-driven automation, ultimately ensuring superior product consistency and efficiency.

Chemosensors doi: 10.3390/chemosensors14080176

Authors: Xiaochun Han Xuan Wang Runze Liu Junjie Yin Zhiqiang Ai Ruiyuan Xue Qixue Liao Huili Hao

In this study, an electrochemical immunosensor based on polyaniline/nano-Fe3O4 (PANI/Nano-Fe3O4) nanocomposite (PANI/Nano-Fe3O4/Anti-FLA/BSA/GCE) was developed for the highly sensitive and selective detection of trace levels of fluoranthene (FLA) in marine environments. Fluoranthene antibodies (Anti-FLA) were covalently immobilized on a glassy carbon electrode (GCE) modified with PANI/Nano-Fe3O4 via an EDC/NHS activation strategy, enabling specific recognition of FLA based on the antigen–antibody binding mechanism. The performance of the sensor was systematically optimized using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), linear sweep voltammetry (LSV), and differential pulse voltammetry (DPV). The results demonstrated a linear inverse relationship between peak current (Ip) and FLA concentration in the range of 0.5~80 ng/mL, with a regression equation of I = −1.55C + 174.602 (R2 = 0.996). The limit of detection (LOD) was as low as 0.354 ng/mL (S/N = 3). In real seawater sample analysis, spiked recovery tests at three representative sites in the Maowei Sea, Guangxi, yielded recoveries of 95.44%~97.51%, with RSDs below 3%, confirming the sensor’s resistance to matrix interference. The synergistic effect of the porous conductive network of PANI and the high specific surface area of Nano-Fe3O4 significantly amplified the electrochemical signal, while the molecular specificity of the antibody ensured targeted recognition. This sensor provides a novel and effective approach for the on-site rapid detection of polycyclic aromatic hydrocarbon (PAH) pollutants in complex marine environments, offering both high sensitivity and selectivity.

Chemosensors doi: 10.3390/chemosensors14080175

Authors: Qinwen Wang Yiqing Wang Yang Huang Jidong Jiang

The widespread use of tetracycline (TC) has resulted in its frequent occurrence in aquatic environments because of incomplete metabolism and continuous release, raising increasing concerns regarding water quality and environmental safety. Reliable determination of trace TC in real water samples remains challenging owing to its low concentration and the complex sample matrix. In this work, an aptamer-functionalized extended-gate ion-sensitive field-effect transistor (EG-ISFET) was developed for sensitive TC detection. The sensing interface was constructed by immobilizing a TC-specific aptamer on a Ta2O5 extended gate. Upon target recognition, aptamer folding redistributes the interfacial charge, thereby modulating the surface potential of the extended gate and producing a measurable field-effect response. The proposed sensor exhibits a broad linear detection range from 1 pM to 1 μM with a detection limit of 0.33 pM. To facilitate practical applications, a compact plug-and-play point-of-care testing (POCT) platform integrating the EG-ISFET sensor was further developed for rapid on-site analysis. The portable system demonstrated satisfactory accuracy and reliability for the determination of TC in river water samples, highlighting its potential for environmental monitoring and point-of-use water quality assessment.

Chemosensors doi: 10.3390/chemosensors14080174

Authors: Diana E. Soto-Nájera María Teresa Alarcón-Herrera José Rafael Irigoyen-Campuzano Liliana Reynoso-Cuevas

In this study, a carbon paste electrode modified with silver and magnetite nanoparticles was used to quantify arsenic via square-wave anodic stripping voltammetry. Under optimized parameters, a deposition potential of −0.4 V for a period of 70 s, the setup achieved a linear quantification range from 0 to 40 µg L−1 of As (III) (R2 = 0.982). Electrochemical characterization was performed using cyclic voltammetry and electrochemical impedance spectroscopy (EIS). The evaluated arsenic quantification framework encompasses the World Health Organization (WHO) limit and the Mexican maximum permissible level for arsenic in drinking water (10 µg L−1), as well as arsenic concentrations frequently reported in groundwater wells throughout northern Mexico. Key advantages of this electrode include its low fabrication cost, ease of operation, and analytical stability spanning a 30-day period. Additionally, selectivity tests revealed that Cu(II) masked the arsenic stripping signal at a 1:1 Cu(II): arsenic concentration ratio, whereas no modification of the target signal was observed in the presence of cadmium.

Chemosensors doi: 10.3390/chemosensors14080173

Authors: Xuemei Yin Pengfei Song Xiao Luo Yu Bai Yuang Wang Xin Hu Haiyan Wang Daji Ergu Juanjuan Zhang Fangyao Liu

Biomimetic sensors offer a rapid route for estimating active constituent contents in Chinese medicinal materials, but their practical use at procurement sites and production workshops is constrained by small and skewed datasets, repeated task-specific model configuration, and the limited operational traceability of many nonlinear models. This study proposes the Traditional Chinese Medicine Unified Sensor-based Prediction (TCM-USP) framework for traceable preliminary screening rather than confirmatory laboratory quantification. The same modeling workflow was applied across different medicinal material–sensor combinations and integrates controlled symbolic feature construction with density-aware robust partial least squares modeling. The framework was evaluated on six prediction tasks involving five medicinal materials and three sensor types. TCM-USP achieved higher R2 values than raw-feature PLS in all six tasks and achieved the highest R2 among the evaluated models in four tasks. Although SVR or GPR achieved higher R2 values in the remaining two tasks, TCM-USP generated compact prediction formulas directly expressed in terms of the original sensor readings, enabling independent calculation, audit, and rapid batch-level decision support. These results support the feasibility of TCM-USP for traceable preliminary screening across the investigated small-sample and low-dimensional sensor tasks, while pharmacopoeial methods remain necessary for confirmatory quantification.

Chemosensors doi: 10.3390/chemosensors14080172

Authors: Nigora Qutlimurotova Dilsora Axmadova Dilnoza Ismailova Jasur Tursunqulov Rukhiya Qutlimurotova Lola Yusupova Sholpan Yespenbetova Nargiza Atakulova

A simple and cost-effective graphite paste electrode modified with 5-(4-aminophenyl)-4-amino-1,2,4-triazole-3(2H)-thione was developed for the selective voltammetric determination of Cu(II) ions in environmental water samples. The N,S-donor ligand was incorporated into a graphite–polystyrene matrix without the use of nanomaterials, providing a reproducible and straightforward electrode fabrication route. Scanning electron microscopy revealed a rough, porous surface morphology with an enhanced electroactive surface area of 0.065 cm2, approximately twice the geometric area. Electrochemical impedance spectroscopy confirmed diffusion-controlled mass transport, while cyclic voltammetry indicated quasi-reversible behaviour of the Cu(II)/Cu(0) redox system with a linear dependence of peak current on the square root of the scan rate. Differential pulse voltammetry under optimised conditions (0.1 mol·L−1 H2SO4, pH 1.0–1.2) yielded a linear analytical response over the concentration range of 0.01–0.4 μmol·L−1 (R2 = 0.99507), with a limit of detection of 0.02 μmol·L−1 and a limit of quantification of 0.06 μmol·L−1—well below the WHO guideline for copper in drinking water. The sensing mechanism involves selective N,S-bidentate coordination of Cu(II) at the electrode surface, followed by electrochemical reduction, as supported by FT-IR spectroscopic evidence. The sensor demonstrated good selectivity toward Cu(II) in the presence of common interfering metal ions at up to 20-fold excess. The method was successfully validated against ICP-OES (recovery 99.8%, RSD < 0.33%) and confirmed by spike–recovery experiments (99.0–99.5%), confirming its practical applicability for trace-level environmental monitoring. The modified electrode retained approximately 93% of its initial response after 30 consecutive measurements and 91% after 14 days of storage, demonstrating good operational stability.

Chemosensors doi: 10.3390/chemosensors14080171

Authors: Najamuddin Naveed Khaja Sushma Yadav Niranjan Haridas Menon Sreerag Kaaliveetil Guangliang Liu Yu-Hsuan Cheng Kathleen McEnnis Sagnik Basuray

The demand for a versatile and portable point-of-use (POU) sensor platform has surged due to the pandemic, especially in countries with limited medical laboratory facilities. We recently unveiled a portable, non-planar, interdigitated, flow-through, porous electrode platform that automatically measures electrochemical impedance spectroscopy (EIS) signals from various biomarkers. However, the packed powder exhibited a loss of performance over time due to displacement, leaching, and poor stability. Herein, we modified the packing strategy by synthesizing the sensing material within the channel, thereby improving adhesion, structural integrity, and stability. Leveraging the exceptional thermal stability, mechanical strength, and chemical resistance of polyimide (PI), we developed a novel fabrication approach that combines liquid-phase inversion and breath-figure techniques to create a porous PI manifold with single-walled carbon nanotubes (SWCNTs) under varying humidity conditions. Scanning electron microscope (SEM) analysis revealed that lower relative humidity (RH) conditions yield larger but less uniformly distributed pores, leading to increased channel pressure. The manifold demonstrated exceptional stability under rigorous flow conditions, withstanding a high flow rate of 30 µL/min while maintaining consistent pressure-EIS responses. The device produced a measurable proof-of-concept impedance response following exposure to a femtomolar concentration of complementary target ssDNA in 1× PBS within 15 min. A formal limit of detection was not determined in the present study. We developed a mechanically stable sensor design with improved durability under repeated flow conditions by systematically optimizing synthesis conditions and manifold configuration. This innovative fabrication strategy demonstrates the importance of packing methodology in sensor design and paves the way for robust, scalable, and efficient diagnostic solutions in resource-limited settings.

Chemosensors doi: 10.3390/chemosensors14080170

Authors: Meiqing Jin Qingwei Zhou Li Fu

This critical perspective review first classifies glyphosate-sensing platforms and then evaluates their analytical applicability to agricultural runoff. Platforms are divided at the primary level into optical and electrochemical transduction, because these families measure different physical signals and have different sources of matrix interference. They are then grouped by the process that produces selectivity or signal change: direct interaction or metal coordination, affinity recognition by aptamers, antibodies, or molecularly imprinted polymers, catalytic modulation by enzymes or nanozymes, and separation or preconcentration before detection. This hierarchy distinguishes recognition chemistry from transduction method and device configuration. The review next defines four intended analytical applications—trace surveillance, runoff event screening, spill triage, and laboratory-adjacent confirmation—and compares them in terms of matrix, target concentration range, sample preparation, reporting metrics, and quality control requirements. Glyphosate occurs in dissolved and particle-associated forms, degrades mainly to AMPA, and coexists with phosphate, glufosinate, divalent cations, natural organic matter, and suspended sediment. Consequently, the lowest reported LOD is rarely the sole criterion for selecting a method. Matrix-matched calibration, spike recovery, selectivity, response time, storage stability, reader requirements, and invalid result rules determine whether an assay is suitable for a specified analytical application. The most defensible near-term approach combines matrix-specific sample preparation, platform-specific controls, and LC-MS/MS confirmation when results are regulatory, contested, or close to a decision threshold.

Chemosensors doi: 10.3390/chemosensors14070169

Authors: Jordi Roig-Rubio Carmen Coll Salvador Gil Pau Arroyo José A. Sáez Pablo Gaviña

The rising prevalence of Drug-Facilitated Sexual Assault (DFSA) underscores the urgent need for rapid, sensitive, and selective analytical tools. Among the most concerning substances are γ-hydroxybutyric acid (GHB) and its precursor γ-butyrolactone (GBL), both of which pose significant forensic challenges due to their rapid metabolism and narrow detection windows. While detection methods for GHB have advanced, the monitoring of GBL remains less explored. This study reports the extended application of a fluorescein-based chemosensor previously validated for GHB for the selective detection of GBL in aqueous media and synthetic saliva. The probe operates via fluorescence quenching proportional to GBL concentration, achieving detection limits well below toxicological thresholds. Mechanistic investigations reveal that the recognition process is driven by an acid–base equilibrium and the reversible opening of the fluorescein lactone ring, with the 2-aminonaphthoxazole moiety playing a pivotal role. The sensor exhibits high selectivity against common DFSA-related interferents. Furthermore, the system was integrated into a portable lateral flow assay coupled with a smartphone-based readout, providing a cost-effective and user-friendly platform. Meeting the WHO “ASSURED” criteria, this methodology represents a promising tool for forensic, clinical, and preventive applications against chemical submission.

Chemosensors doi: 10.3390/chemosensors14070168

Authors: Jingjing He Min Ye Huiting Lian Xuexia Lin

As a core component of vitamin B12, Co2+ is closely associated with the health, life performance, and productivity of ruminants. Therefore, the selective and sensitive detection of Co2+ is of great significance. In this work, using the Au–S bond as an “anchor”, a ternary nanocomposite (CDs-AuNPs-GSH) with synergistic functions was constructed by combining gold nanoparticles (AuNPs) with glutathione (GSH) through hydrogen bonding and electrostatic interactions with functional groups on the surface of carbon dots (CDs). The resulting nanocomposite was employed as a fluorescence sensor for Co2+ detection. Under optimal conditions at pH 6.0, the sensor exhibited a good linear relationship over the Co2+ concentration range of 0.5–125.0 mM, with a limit of detection (LOD) of 0.38 mM. The interaction mechanism between Co2+ and the composite was systematically investigated using various characterization methods. The results indicated that Co2+, owing to its strong coordination ability, enriches on the surface of the composite, subsequently triggering dynamic fluorescence quenching via an electron transfer pathway. The sensor was successfully applied to determine Co2+ in mixed livestock and poultry feed samples, with recovery rates ranging from 89.4% to 103.9%, demonstrating its potential for on-site detection in feed analysis.

Chemosensors doi: 10.3390/chemosensors14070167

Authors: Cornelia Veronica Floare-Avram Olivian Marincas Dana Alina Magdas Ioana Feher

The study aimed to characterize Romanian lavender essential oils (LEOs) according to geographical regions and harvested years by combining gas chromatography–mass spectrometry (GC/MS) with chemometric analysis and quality evaluation. Sixty-four LEO samples were collected from three Romanian areas (Moldova, Transylvania, Muntenia) during three consecutive years (2023–2025). GC–MS profiling identified 45 volatile compounds, with linalool, linalyl acetate, 1,8-cineole as predominant constituents. Oil quality was assessed using both a quality scoring algorithm based on characteristic volatile compounds and compliance with the ISO 3515:2002 standard. Most samples were classified as very good quality according to the proposed quality score, whereas only three samples fully complied with all ISO requirements. Pearson correlation analysis revealed significant positive associations among several biosynthetically related compounds, indicating identical variation within the volatile fingerprint. Linear discriminant analysis (LDA) successfully discriminated samples according to both geographical origin and harvest year, identifying characteristic combinations of volatile compounds responsible for sample classification. The results demonstrate that the overall volatile fingerprint, rather than individual compounds, provides a reliable basis for the authentication and differentiation of Romanian lavender essential oils. The combined GC–MS and chemometric approach represents an effective tool for quality evaluation, geographical traceability and authenticity assessment of lavender essential oils.

Chemosensors doi: 10.3390/chemosensors14070166

Authors: Peng Chen Xin Zhang Jiacheng Liu Xu Li Min Chen Qingji Wang

Hydrogen is a clean energy carrier essential for carbon neutrality, but its invisible and odorless nature poses significant safety risks, particularly during low-concentration leaks. Although metal oxide semiconductor (MOS) sensors offer fast response and high sensitivity, their ability to detect ppb-level hydrogen remains limited. In this work, we present a high-performance hydrogen gas sensor based on nickel-doped tin dioxide (Ni-SnO2) nanorods, directly grown on planar electrodes via aerosol-assisted chemical vapor deposition (AACVD). By optimizing the Ni doping ratio and nanorod morphology, the 3 wt% Ni-SnO2 sensor achieves a low detection limit of 100 ppb for H2, demonstrating promising potential for low-concentration hydrogen detection. Moreover, the sensor exhibits outstanding selectivity, with a response to 100 ppm H2 nearly six times higher than that to the next most responsive interfering gas (NH3). Comprehensive XPS and Raman analyses reveal that Ni doping introduces abundant oxygen vacancies and lattice defects, which are the key origins of the enhanced sensing performance. Notably, the 3 wt% Ni-SnO2 sensor strikes an optimal balance between lattice defects and structural stability, delivering both high sensitivity and good moisture resistance with minimal baseline drift over weeks of operation. This work establishes a facile and scalable AACVD strategy for engineering defect-rich SnO2 nanostructures, enabling sub-ppm hydrogen detection with high selectivity and long-term stability—addressing a critical gap in practical hydrogen safety monitoring.

Chemosensors doi: 10.3390/chemosensors14070165

Authors: Kien Wen Sun Muthaiah Shellaiah

Chemosensors for diverse analytes have become a significant research topic experiencing innovative real-time development [...]

Chemosensors doi: 10.3390/chemosensors14070164

Authors: Ru Wang Jinghang Li Siqi Huang Yuguang Lv Zhiling Hou Nuan Wen

Reperfusion after acute ischemic stroke (AIS) triggers a series of ferroptosis-related molecular events, including iron dyshomeostasis, oxidative/nitrative stress, antioxidant depletion, and membrane lipid peroxidation. Conventional ferroptosis assays mainly rely on ex vivo or endpoint measurements, limiting their ability to dynamically monitor the spatiotemporal evolution of these events during ischemia–reperfusion. Recent advances in chemical sensing and optical imaging have enabled in situ detection of key ferroptosis-related nodes, such as Fe2+/labile iron pool, ROS/ONOO−, GSH/Cys/GPX4, H2S/Cys–Met metabolism, and lipid peroxidation. In this review, we summarize sensing targets, reaction-based probe design, near-infrared and two-photon imaging, photoacoustic imaging, and multimodal validation strategies for AIS-related ferroptosis. Representative probes for H2O2, ONOO−, H2S, Fe2+, and lipid peroxidation are discussed in the context of cellular models, oxygen-glucose deprivation/reoxygenation, middle cerebral artery occlusion/reperfusion, and in vivo brain imaging. We emphasize that a single probe signal cannot independently confirm ferroptosis and should be interpreted together with GPX4/ACSL4 alterations, MDA/4-HNE levels, tissue injury, neurological outcomes, and Fer-1/Lip-1 rescue experiments. Finally, we discuss current challenges, including limited tissue penetration, blood–brain barrier delivery, quantitative stability, probe safety, and clinical translation, and highlight future directions involving ratiometric, NIR/NIR-II, two-photon, multitarget, and imaging-guided validation strategies.

Chemosensors doi: 10.3390/chemosensors14070163

Authors: Yan Tan Haonan Wu Junjun Lan Ting Qian Xin Zhou Shiyang Zhao Hui Wang

On-site nucleic acid analysis for infectious diseases can be rapidly achieved through molecular point-of-care testing (molecular POCT), which plays an indispensable role in early pathogen identification, timely clinical intervention, and public health emergency response. Performance improvements in such systems are largely driven by innovations in functional materials that refine nucleic acid extraction, amplification, and signal output. This article reviews recent developments in functional materials deployed in molecular POCT, with emphasis on nucleic acid capture matrices, amplification-promoting agents, and signal transduction components. From a medical device regulatory standpoint, we examine how material characteristics shape key analytical indicators, including sensitivity and specificity, and discuss critical risks such as off-target amplification and batch inconsistency. Finally, we outline future directions, highlighting cross-disciplinary cooperation to reconcile technological innovation with risk control for translating advanced materials into high-performance molecular POCT products.

Chemosensors doi: 10.3390/chemosensors14070162

Authors: Hye Yeon Jang Sthitodhi Ghosh Chong H. Ahn Narendhar Chandrasekar Michael Taeyoung Hwang Moon-Soo Kim

The detection of RASSF1A (Ras-associated domain family 1 isoform A) promoter methylation in body fluids can offer a powerful tool for the early diagnosis of bladder cancer. Zinc finger proteins (ZFPs) serve as sequence-specific recognition elements for targeting double-stranded DNA sequences. Here, we report a cyclic olefin copolymer (COC) chip-integrated ZFP array-based molecular sensor that bypasses the need for bisulfite conversion and PCR amplification to recognize the specific site of DNA methylation in the RASSF1A promoter. Building upon the SEER-LAC (SEquence-Enabled Reassembly of β-Lactamase) framework, we engineered a dual-recognition split-enzyme system in which a COC chip-immobilized ZFP array confers sequence specificity while a co-recruited methyl-binding domain (MBD) enforces methylation-dependent gating, together driving the proximity-induced reconstitution of functional β-lactamase at methylated target loci. Accordingly, this sensor specifically reassembles and restores enzymatic activity only in the presence of specific methylated DNA in the RASSF1A promoter region. We demonstrate that this dual-component array effectively differentiates methylation status with high specificity. Given its rapid turnaround and non-PCR-based mechanism, this system can be well-suited for developing diagnostic assays for bladder cancer, offering a potential alternative to conventional epigenetic screening methods.

Chemosensors doi: 10.3390/chemosensors14070161

Authors: Jiahao Zhang Zaihua Duan Yuanming Wu Zhen Yuan Yadong Jiang Huiling Tai

Low-cost gas sensor arrays are attractive for mixed-gas monitoring, but deployment-oriented modeling remains challenging because mixed-gas responses are nonlinear, cross-sensitive, and strongly dependent on sensor dynamic states. Existing electronic-nose models often rely on handcrafted response descriptors or generic sequential networks, which may either compress transient response information or introduce unnecessary computational cost. This work proposes SDRMixer, a lightweight sensor-specific framework for mixed-gas concentration quantification. SDRMixer uses a parameter-free sparse dynamic response encoding to organize the original sensor response, baseline-referenced excitation, and smoothed response kinetics into a physically meaningful dynamic response field. A compact temporal-feature mixer is then applied over fixed response-stage tokens for simultaneous multi-gas regression. To improve calibration coverage, a response-consistent augmentation strategy is used during model training. The proposed framework is evaluated on a previously reported mixed-gas sensor array dataset containing NO2, NH3, CH4, and CO2 mixtures. Both augmentation-enriched calibration domain benchmarking and original-measurement-based validation are conducted to assess prediction performance, computational efficiency, and stability on measured calibration samples. The results show that SDRMixer provides a good trade-off between accuracy and efficiency compared with generic deep learning architectures and compact gas-sensing baselines. These findings indicate that explicit dynamic response encoding combined with lightweight temporal-feature mixing is an effective modeling strategy for compact mixed-gas quantification within the investigated calibration domain.

Chemosensors doi: 10.3390/chemosensors14070160

Authors: Lu Chen Changyun Dai Mingjun Wang Feilong Ren Zhiming Zeng Hui Ao

The dried rhizome of Atractylodes lancea (RAL) is a widely used traditional Chinese medicine (TCM). Its quality evaluation and origin authentication have long relied on time-consuming chromatographic methods, which are poorly suited for rapid, on-site decisions in commercial supply chains, and existing studies generally focus on isolated morphological indicators without systematic digital characterization and practical on-site grading tools. Guided by the traditional empirical knowledge of “Bianzhuang Lunzhi”, which holds that external morphological traits can reflect the internal quality of TCM, this study presents the first systematic multi-dimensional digital characterization of RAL morphological traits using an integrated multi-sensor approach and quantitatively explores the underlying correlations between digital traits and key bioactive constituent contents. Nighty samples from three major producing regions were analyzed. Significant correlations were observed between odor indices, color parameters, density, oil cavity area ratio and bioactive component contents in the authentic Maoshan-sourced RAL (p < 0.01 or p < 0.05). Such associations were absent in the emerging regions (Dabie and Qin−Ba Mountains). A three-grade quality classification system based on density thresholds (Grade A: ≥0.73 g/cm3; B: 0.58–0.73 g/cm3; C: <0.58 g/cm3) was established specifically for Maoshan RAL. Additionally, an electronic nose-based classification model was constructed for geographical origin discrimination, which delivered reliable and robust classification performance in external validation with independent blind test samples. This work provides practical, low-cost tools for rapid quality grading and origin identification of RAL. The proposed trait-driven analytical strategy offers a generalizable framework for the quality control of other complex herbal medicines.

Chemosensors doi: 10.3390/chemosensors14070159

Authors: Yuqing Zhang Yanjie Wang Kun Zhu Zhiqiang Lan Jie Wang Jian He Xiujian Chou Yong Zhou

Exhaled breath analysis has emerged as a promising non-invasive approach for disease diagnosis, leveraging gas sensors for their high sensitivity, portability, and real-time monitoring capabilities. Two-dimensional nanomaterials, such as graphene, transition metal dichalcogenides (TMDs), MXenes, black phosphorus, and metal–organic frameworks (MOFs), exhibit exceptional gas-sensing properties due to their atomic-scale thickness, ultra-large specific surface area, and tunable electronic structures. These characteristics enable enhanced gas adsorption and room-temperature operation, making them ideal for detecting ppb-level biomarkers like acetone, ammonia, and nitric oxide in breath. However, sensors based on pristine 2D materials face challenges including slow response/recovery kinetics, poor stability, weak humidity resistance, and limited selectivity in complex breath environments. To address these limitations, functionalization strategies have been developed to engineer material properties. Key approaches include heteroatom doping to modulate electronic band structures, heterojunction construction to facilitate charge transfer and improve selectivity, and noble metal decoration for catalytic enhancement of gas adsorption. Additionally, light irradiation has been employed to regulate the carrier concentration on the surface of sensitive materials. These strategies significantly boost sensor performance, achieving ppb-level detection limits, robust humidity resistance, and rapid response. Future directions involve integrating functionalized 2D materials into wearable, multiplexed sensor arrays for simultaneous biomarker detection, coupled with machine learning for real-time diagnostic platforms.

Chemosensors doi: 10.3390/chemosensors14070158

Authors: Lia Vasconcelos Javier Mateo Nuno A. S. Dias Ana Leite Alfredo Teixeira Sandra S. Q. Rodrigues Luís G. Dias

This study evaluated the use of an E-nose using a piercing-assisted volatile extraction as a practical and non-destructive tool for distinguishing between three muscle types (biceps femoris—BF; semitendinosus—ST; and semimebranosus—SM) in 30-month ripened dry-cured Bísaro hams (n = 23). The muscles were analyzed for volatile organic compounds (VOC) using gas chromatography-mass spectrometry (GC-MS) and for signal profiles obtained from an E-nose system composed of metal oxide (SnO2) sensors. Sensor signals were standardized using Z-score normalization prior to chemometric modeling. Linear discriminant analysis (LDA) was used to evaluate the capability of the MOS-based E-nose to differentiate the VOC profiles of Bísaro ham across its main muscle types. The model trained on Z-score-standardized sensor signals achieved classification accuracies of 94.3% and 80.0% for the training and external test sets, respectively, demonstrating good predictive performance and robustness. When compared with the VOC-based LDA model (94.4% and 78.6% for the training and test sets, respectively), the E-nose showed comparable classification performance and slightly higher predictive capability in the external validation set. The first two discriminant functions explained 88.01% and 11.99% of the discriminant variance, respectively, indicating that most of the discrimination occurred along a single dominant axis. To chemically interpret the sensor-based discrimination, multiple linear regression models were established between the LDA scores and VOC concentrations. The first discriminant function was significantly associated with compounds related to lipid oxidation and aroma development, particularly 2-pentylfuran, butanoic acid, hexanoic acid, hexanal, and benzaldehyde (R2 = 0.617; p < 0.001), whereas the second discriminant function showed a weaker but significant relationship with hexanal, 3-methylbutanal, butanoic acid, and hexanoic acid (R2 = 0.202; p = 0.006). These findings demonstrate that the E-nose is capable of capturing meaningful chemical information associated with muscle-specific volatile profiles and can provide a rapid, non-destructive, and cost-effective alternative for the characterization and classification of dry-cured Bísaro ham.

Chemosensors doi: 10.3390/chemosensors14070157

Authors: Fengchao Zhou Liyan Xie Guorong Lin Yilin Lin Jiandong Shen Shibin Deng Gaowa Xing

Intelligent food packaging (IFP) is among the key technologies for overcoming global challenges of food safety and food resource waste. Its core lies in monitoring the quality of food in real-time without damage. Sodium alginate (SA), a natural polysaccharide characterized by biodegradability and excellent biocompatibility, can form hydrogels with a 3D network structure, high water content, and functional modification capability, making it an ideal matrix for developing IFP sensing and indicator platforms. Based on the gel chemistry fundamentals of SA, this paper deeply analyzes the structure-activity relationship between sensing mechanism and material structure, and summarizes the existing modification strategies and functional integration paths. The paper also provides a detailed discussion on the application principles and latest advancements of SA-based hydrogels in colorimetric/visual sensing, gas sensing, time-temperature indicator (TTI), and controlled-release carriers for active substances. The current research results show that the detection limit of SA hydrogel beads loaded with anthocyanins for volatile amines can reach 15–25 ppm, and the color difference ΔE can reach 34.2 after 7 days of storage at 4 °C, which is strongly correlated with microbial indicators, total volatile basic nitrogen (TVB-N), pH, etc. The color difference value (ΔE) response of Co-Imd microcrystalline functionalized SA film to ammonia gas reached 23.7 within 60 min, and it had antibacterial activity. The activation energy of Immobilization of laccase on sodium alginate/soluble starch microcapsules to develop a TTI (27.32–61.13 kJ/mol) was highly matched with the activation energy of Agaricus bisporus. The hydrogel microspheres loaded with Cur@Se reduced the total oxidation value of the oils by 53%. The G/SA/nZnOs cryogel pad extended the shelf life of shrimp from 4 days to 6 days at 4 °C. In addition, this paper also discusses the challenges faced by SA-based hydrogels in large-scale production and long-term stability evaluation, and looks forward to future development trends such as integration with artificial intelligence (AI), Internet of Things (IoT), and multi-functional integration, in order to provide theoretical support for in-depth research and industrial application in this field.

Chemosensors doi: 10.3390/chemosensors14070156

Authors: Tong Xiang Chongyang Zhang Qiongqiong Ren Jinlong Chang Linyan Xie Xuming Sun

Nitrite (NO2−) is a crucial environmental and food safety indicator, and excessive intake poses severe threats to human health; thus, highly sensitive and selective detection methods are urgently needed. Herein, a ratiometric fluorescent nanosensor based on N-CDs@UiO-66-NH2 was constructed via a facile assembly strategy for sensitive and selective nitrite detection. The composite demonstrated dual-emission fluorescence at 456 nm and 730 nm under 365 nm excitation, originating from UiO-66-NH2 and N-CDs, respectively, enabling an intrinsic self-referencing signal. The fluorescence intensity ratio exhibited a good linear response toward nitrite in the range of 10–100 μM and 100–450 μM with a limit of detection (LOD) 1.76 μM. The nanosensor showed high selectivity and anti-interference ability against various ions and molecules. Furthermore, it was successfully applied to nitrite detection in lake water samples with satisfactory recoveries (97–101%) and low relative standard deviations (<1.8%). This work provides a simple and effective approach for nitrite monitoring in environmental samples.

Chemosensors doi: 10.3390/chemosensors14070155

Authors: Andrei-Silviu Zancu Mihai Robert Zamfir Nicolae Cristian Mihailescu Constantin Pintilie Nicu Doinel Scărișoreanu

In our day-to-day lives, we are regularly exposed to a wide spectrum of dangerous gases. Their origins vary, ranging from industrial activities to objects found within our very homes. Naturally, there is an interest in developing cost-efficient and durable devices that can successfully track these gases within our environment. One such candidate is represented by chemoresistive gas sensors based on metal oxides. This is due to their simple architecture and the possibility of scaling down their size, making them valid contenders for future advancements in portable gas sensors. This review focuses on chemoresistive gas sensors that have been obtained through different Physical Vapor Deposition (PVD) methods, which are easily scalable for potential technological transfer towards commercialization or are already exploited at the industrial level, and how varying different deposition parameters impacts the structure of the active material, thus modifying the gas sensing properties of the device. In this review, we report results obtained for different metal oxides: WO3, ZnO, CeO2, TiO2, NiO, and SnO2. The main findings of these studies revealed that the sensor’s response was highly impacted by oxygen deficiencies within the deposited material, the specific surface area, and the thickness of the film. Moreover, this study also delves into different strategies of functionalization that result in improved gas sensing properties. Thus, we herein report how tailoring functional properties modifies the gas sensing performance of different metal oxides.

Chemosensors doi: 10.3390/chemosensors14070154

Authors: Hayrettin Gökozan

In this study, an integrated machine learning (ML)-based calibration approach was employed for the data-driven calibration of a low-cost PM2.5 sensor. The primary objective was to systematically evaluate the relative contributions of location-specific environmental covariates, auxiliary gaseous pollutants, and Feature Engineering (FE) strategies in the calibration of a low-cost PM2.5 sensor deployed in an outdoor residential environment. For this purpose, a multi-stage experimental framework based on an ablation study design incorporating different environmental information groups was implemented, and the models were evaluated using a leakage-safe time-based validation approach. The results demonstrated that FE strategies significantly improved model performance across all experimental configurations. The highest performance was obtained under the configuration using raw PM2.5 sensor data together with FE-derived features, where the RF model increased the Test R2 value from 0.7237 to 0.8467 and reduced the RMSE from 6.96 µg/m3 to 5.19 µg/m3. Feature importance analyses indicated that humidity-based interaction features and cyclic temporal encoding structures provided substantial contributions to model performance. The findings suggest that meaningful PM2.5 calibration performance can be achieved using location-specific environmental covariates and data-driven FE strategies in scenarios where continuous co-location is not operationally feasible.

Chemosensors doi: 10.3390/chemosensors14070153

Authors: Li Fu Fei Chen Yanfei Lv Shichao Zhao Cheng-Te Lin

Single-entity electrochemistry (SEE) expands the scope of analytical electrochemical measurement by shifting attention from ensemble-averaged currents to individually resolved stochastic events. This review evaluates progress toward two analytical endpoints, trustworthy detection of single molecules and context-preserving interrogation of single cells, with emphasis on quantitative rigor rather than platform novelty alone. Across nanoparticle collisions, nanopores, confined nanoelectrodes, vesicle electrochemical cytometry, intracellular nanopipettes, and array-enabled single-cell devices, the decisive analytical issue is no longer simply whether one entity can be detected, but whether event assignment, calibration, throughput, and reproducibility are sufficient to support credible inference. Representative primary studies are compared through shared metrics including event frequency, temporal resolution, bandwidth, molecular counts, detection limit, affinity, and effective yield of analyzable events. Particular attention is given to three recurring bottlenecks: interfacial variability, model-dependent event interpretation, and incomplete reporting of denominators such as rejected events, insertion success, and pore-to-pore or cell-to-cell reproducibility. The current evidence base is strongest in secretion and vesicle studies, whereas confinement-enabled and multimodal routes define the leading edge of single-molecule analysis. Overall, SEE is developing not as a single universal platform, but as a family of interface-controlled, data-rich analytical strategies whose future analytical value will depend on standardized reporting, multimodal validation, and benchmarking practices that preserve both sensitivity and confidence of assignment.

Chemosensors doi: 10.3390/chemosensors14070152

Authors: Ying Li Meng Liu Zhaomin Sun Lei Yu Feifei Gong Guangyu Yan

Rapid geographical origin discrimination of Tremella fuciformis is important for quality control and authenticity assessment; however, conventional analytical methods are often time-consuming and require complex sample preparation. In this study, a rapid discrimination approach was established by integrating electronic nose (E-nose) response fingerprints with machine learning. To capture temporal variation in the E-nose signals, fingerprint features were extracted from three response windows: the selected overall response window (0–69 s), the early response window (0–29 s), and the relatively stable response window (56–65 s). Random forest, partial least squares discriminant analysis (PLS-DA), Gaussian naive Bayes, nearest centroid, and decision tree were then constructed and evaluated. Classification performance varied among the temporal-window feature sets. Based on 100 repeated stratified random splits, PLS-DA model using the 56–65 s feature window achieved the best overall classification performance, with accuracy, balanced accuracy, F1-score (the harmonic mean of precision and recall), and ROC-AUC (the area under the receiver operating characteristic curve) values of 0.9933 ± 0.0255, 0.9928 ± 0.0256, 0.9919 ± 0.0293, 0.9991 ± 0.0085, respectively. These findings indicate that E-nose fingerprinting combined with PLS-DA may provide a rapid and effective method for geographical origin discrimination of T. fuciformis.

Chemosensors doi: 10.3390/chemosensors14070151

Authors: Shuai Lu Jiakang Sheng Yibo Xu Fan Zhang Xingyu Song

Short-wave near-infrared (SW-NIR) spectroscopy provides a rapid and nondestructive sensing route for monitoring bread staling, but formulation-dependent moisture redistribution and starch retrogradation can make pooled spectral regression unstable. This study investigated a stratified SW-NIR modeling strategy for bread staling prediction using 324 spectra from control bread (CR) and two maltogenic α-amylase treatments (EZ1 and EZ2). A global full-spectrum partial least squares (PLS) model was compared with bread-type-specific PLS models; competitive adaptive reweighted sampling (CARS), support vector machine recursive feature elimination (SVM-RFE), and multiple feature-spaces ensemble LASSO (MFE-LASSO) were then each coupled with PLS and evaluated within each bread type. The pooled benchmark achieved a root mean square error of prediction (RMSEP) of 2.28 days, whereas stratified full-spectrum PLS reduced this to 1.86, 2.14, and 2.15 days for CR, EZ1, and EZ2, respectively. In repeated wavelength-selection runs, MFE-LASSO was the most consistently competitive method across bread types. In the representative best-model comparison, MFE-LASSO-PLS yielded the strongest performance for CR (RMSEP = 1.71 days) and EZ1 (RMSEP = 1.43 days), while CARS-PLS gave the lowest RMSEP for EZ2 (2.00 days). An exploratory position-specific analysis within the CR subset further suggested that the middle crumb region carried stronger staling-related spectral information than the top and bottom regions. These results indicate that formulation-aware SW-NIR spectroscopic sensing is a practical strategy for nondestructive bread-staling assessment and that the optimal wavelength-selection method is bread-type-dependent.