Search Results (540)

Food safety and quality analysis is shifting from laboratory-based end-point testing toward faster, lower-volume and matrix-adapted on-site decision-making. Near-infrared (NIR), visible-near-infrared (Vis-NIR), hyperspectral, Raman, surface-enhanced Raman scattering (SERS), fluorescence, colorimetric and terahertz approaches, together with impedance time-series readout, provide complementary information on composition, molecular vibrations, spatial distribution, reaction outputs, or electrical responses. In real foods, however, lipids, proteins, sugars, salts, pigments, particles and native fluorescence can alter spectral baselines, mass transfer and model stability. The value of microfluidics is therefore not limited to miniaturization but lies in organizing filtration, homogenization, splitting, mixing, extraction, enrichment, reaction, and readout positions into a controllable sample-to-signal workflow. This review first distinguishes chemical hazards, biological hazards, authenticity issues, and quality changes according to target and matrix characteristics, and then compares the functional boundaries of continuous-flow, paper-based, droplet, digital-hybrid and enrichment-oriented chips. It further analyses how microfluidics affects detection time, sample and reagent consumption, sensitivity, selectivity, repeatability, portability and cross-matrix applicability through spectral interfaces, signal enhancement, labelled and label-free detection, chemometrics, and machine learning. Representative applications involving pesticides, mycotoxins, pathogens, antibiotics, heavy metals, adulterants, oxidation products, and freshness indicators in real foods are discussed within a unified chain linking chip architecture, spectral signal generation and decision models. Finally, requirements for translation are proposed in terms of standard and real samples, chip-to-chip variation, external model validation, data traceability and scalable manufacturing, providing an operational framework for the joint design of broad-spectrum spectroscopic technologies and microfluidic systems. Full article

Rapid assessment of internal fruit quality is essential for fruit grading, postharvest management, and consumer-oriented quality evaluation. Among the quality attributes, soluble solids content (SSC) is a key indicator of citrus sweetness and maturity. Visible and near-infrared (Vis–NIR) spectroscopy provides an effective approach for rapid SSC detection in fruit. However, most existing studies rely on single full-spectrum models or simple band stacking strategies, which limits their ability to fully exploit complementary information among different spectral sub-bands. To address this limitation, a color residual-variance gated attention fusion network (CR-VGAFNet) is proposed for efficient SSC assessment in Hongmeiren. On the independent prediction set, CR-VGAFNet achieved a prediction correlation coefficient (R_P) of 0.7744, a root mean square error of prediction (RMSE_P) of 0.6530 °Brix, and a mean absolute percentage error of prediction (MAPE_P) of 4.83%. These findings suggest the potential of the framework for multi-band spectral fusion. This study provides a new technical perspective for multi-band spectral fusion and rapid fruit quality assessment. Full article

Accurate forest soil organic carbon (SOC) monitoring is essential for forest soil quality assessment and carbon-sink evaluation. Visible-near-infrared (Vis–NIR) hyperspectral sensing provides rapid and information-rich measurements for SOC prediction, but obtaining sufficiently large labeled soil-spectral datasets remains difficult because field sampling, sample preparation, and reference SOC determination are labor and time intensive. This study developed a latent conditional diffusion-based data augmentation framework for SOC prediction from hyperspectral sensor data. A total of 248 forest red-soil samples from Guangxi, China, were measured using laboratory Vis–NIR reflectance spectroscopy over 350–2500 nm and divided by the Kennard-Stone algorithm into a 174-sample modeling set and a fixed 74-sample validation set. Four generative models, including VAE, GAN, WGAN-GP, and the proposed hyperspectral latent conditional denoising diffusion implicit model (HsDDIM), were evaluated using spectral visualization, t-SNE distributions, maximum mean discrepancy (MMD), Fréchet Inception Distance (FID), and downstream prediction performance. Unlike joint spectral-label generation, HsDDIM treats SOC as an external condition and generates spectra in the latent space under specified SOC conditions; the SOC condition itself is not generated by the diffusion process. Among the compared augmentation strategies, HsDDIM showed the closest distributional agreement with the real spectral samples according to MMD and FID, with values of 0.0806 and 0.5281, respectively. Without augmentation, FD1-SVR achieved the best validation result (R² = 0.83, RMSE = 4.71 g kg⁻¹). After 300% HsDDIM augmentation, 1D-CNN achieved R² = 0.91, RPD = 3.39, and RMSE = 3.40 g kg⁻¹. These results suggest that the SOC-conditioned latent DDIM framework can improve small-sample hyperspectral SOC prediction under the present fixed-validation protocol. Full article

Economically motivated adulteration of powdered ginger with low-cost cereal flours represents an increasing concern for food authenticity and quality control. The aim of this study was to develop and validate a rapid, reliable, and non-destructive method for the quantitative determination of powdered ginger adulteration using visible and near-infrared (Vis–NIR) spectroscopy coupled with chemometric modelling. Ginger powder samples were adulterated with wheat, corn, and rice flours at concentrations ranging from 5 to 50% (w/w), with particular emphasis on the low-to-medium adulteration interval (10–25%), where reliable quantification is especially relevant for food fraud detection. Spectral data acquired in the visible (400–700 nm), near-infrared (700–2500 nm), and combined Vis–NIR (400–2500 nm) regions were preprocessed using Savitzky–Golay filtering and evaluated using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR). In addition, Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Random Forest (RF) were compared for sample classification. Among the evaluated approaches, LDA achieved the highest classification accuracy (>95%) using the NIR spectroscopic region, while PLSR models developed from the NIR spectral region provided the best quantitative performance, with validation coefficients of determination above 0.99, prediction errors below 1%, and RPD values greater than 13. The results demonstrate that Vis–NIR spectroscopy combined with chemometric modelling enables accurate discrimination between authentic and adulterated samples, as well as reliable quantification of flour adulteration in powdered ginger without sample preparation or chemical reagents. The proposed methodology constitutes a rapid, environmentally friendly, and cost-effective analytical strategy with strong potential for routine quality control and food fraud prevention. Full article

Rapid and non-destructive assessment of sweet potato quality is important for postharvest management, processing suitability evaluation, and market quality control. In this study, a 12-channel visible/near-infrared (Vis/NIR) spectral sensing system was applied to predict multiple physicochemical quality attributes of postharvest sweet potatoes. Sixty independent sweet potato storage roots were measured at three representative positions, producing 180 position-specific observations; measurements from the same root were retained within the same validation group. The measured attributes included dry matter content (DMC), starch content (SC), soluble solids content (SSC), and the CIE 1976 L*a*b* (CIELAB) color coordinates L*, a*, and b*. Four spectral treatment conditions, including original spectra, normalization, standardization, and first-derivative transformation, were combined with partial least squares regression (PLSR), multiple linear regression (MLR), extreme gradient boosting (XGBoost), and random forest (RF), generating 16 prediction strategies for each quality attribute. Root-grouped five-fold cross-validation showed that the optimal models achieved coefficients of determination for cross-validation (R²CV) ranging from 0.9083 to 0.9190 and residual predictive deviation (RPD) values ranging from 3.3112 to 3.5230. Repeated grouped cross-validation produced mean R²CV values of 0.9113–0.9176, and root-block Y-scrambling yielded empirical p values of 0.005 for all six attributes. PLSR provided the highest cross-validated performance for all six quality attributes, although MLR showed comparable performance for several targets. These results provide preliminary evidence that discrete Vis/NIR spectral sensing can support simultaneous non-destructive estimation of multiple sweet potato quality attributes. External multi-batch and multi-cultivar validation is required before the models can be considered robust for practical deployment. Full article

Afghanistan’s Panjshir Valley is an important emerald-producing region in Asia. In this study, emeralds from Kamar Safid in Southeastern Panjshir were investigated by Fourier-transform infrared (FTIR), Raman spectroscopy, ultraviolet–visible–near-infrared (UV-Vis-NIR) spectroscopy, and laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS). These Kamar Safid emeralds are generally small, light-green-to-green crystals. Microscopic observations revealed abundant acicular and tubular three- or two-phase fluid inclusions, with transparent feldspar-group mineral inclusions. Solid phases in the fluid inclusions commonly consist of carbonate crystals or several transparent halite daughter crystals. FTIR spectra of samples indicated that the absorption of type II H₂O was higher than type I H₂O in the emeralds from Kamar Safid. The UV-Vis-NIR spectra are characterized by Cr- and V-related absorption bands, which are stronger than Fe-related absorptions. LA-ICP-MS results indicate slightly higher V contents and lower Cr contents than emeralds from other Panjshir mining areas. The relatively low total Cr and V contents of Kamar Safid emeralds account for the overall lighter color, suggesting that Cr and V are the principal chromophores, whereas Fe secondarily modifies hue. Rb, Cs, and Sc contents are 6.2–24.3 ppm, 11.9–141.6 ppm, and 92–1461 ppm, with total alkali contents of 4903.10–14,257.18 ppm. Cs-Rb, Cs-Sc, Li-Cs, and Li-Sc binary logarithmic diagrams indicate enrichment in Sc and Rb and depletion in Li and Cs. Full article

Multispectral remote sensing classification benefits from the complementary information carried by visible (VIS), near-infrared (NIR), and short-wave infrared (SWIR) Sentinel-2 bands, yet most deep models process them as a single stacked tensor without explicitly preserving their inter-group relationships. We propose Relation-Consistency Group Contrastive Learning (Group-CL-RC), a robustness-oriented framework combining group-level contrastive alignment with a relation-consistency regularizer defined over a compact VIS–NIR–SWIR similarity descriptor. The method is evaluated on EuroSAT All Bands using four backbones under radiometric drift, spatial masking, K-drop band removal, and compound spectral–spatial corruption (CS2C), and externally validated on Sentinel-2-only SEN12MS under standard and seasonal-shift protocols. Group-CL-RC preserves strong clean performance and yields statistically supported robustness gains over the multispectral-only baseline, with the largest improvements under K-drop and CS2C. SEN12MS supports the transfer of these robustness trends beyond EuroSAT, while showing that gains over standard Group-CL remain perturbation-dependent. Ablation studies further indicate that relation consistency is an effective robustness mechanism, particularly when spectral-group availability is degraded. Relation-deformation diagnostics show that Group-CL-RC primarily reduces decision-level sensitivity to relational distortions rather than uniformly minimizing raw deformation. Overall, inter-group relational geometry provides an interpretable and effective robustness target under controlled structured spectral and spectral–spatial degradation. Full article

Soil organic carbon (SOC) is a key indicator of soil quality, farmland productivity, and the terrestrial carbon cycle. Visible and near-infrared (Vis-NIR) spectroscopy offers a rapid approach for SOC estimation, but wavelength point selection may disrupt continuous spectral structures, whereas conventional wavelength interval selection may fail to fully exploit complementary information across spectral regions. In this study, a synergistic interval-constrained Ridge regression framework, termed sicRidge, was developed for SOC prediction. Continuous candidate intervals were generated using a sliding-window strategy, and a dual-interval synergistic search with an overlap constraint was applied to identify complementary and low-redundancy interval combinations. The selected intervals were then used to construct Ridge regression models. Using Vis-NIR spectra from 168 soil samples, sicRidge was compared with full-spectrum Ridge regression, five wavelength point selection-based Ridge models, and several wavelength interval selection-related benchmark models. sicRidge achieved the best prediction performance using 140 selected bands, with an R²_P of 0.834, RMSE_P of 2.010 g kg⁻¹, RPD of 2.483, and RPIQ of 3.777. The optimal intervals were 570~649 nm and 1880~1939 nm. These results indicate that sicRidge can improve SOC prediction by preserving continuous spectral structures while exploiting complementary cross-region information. Full article

Potato quality is determined by multiple physicochemical indicators, including dry matter content (DC), starch content (SC), and color parameters (lightness L*, redness a*, yellowness b*, and browning index (BI)). Conventional spectrometers are costly, non-portable and lack wireless in-situ monitoring, restricting efficient postharvest quality assessment. Chemical methods are destructive and inefficient for field inspection and high-throughput detection. The primary objective of this study was to develop and validate a low-cost wireless 12-channel visible/near-infrared (Vis/NIR) spectral sensing system, comprising 6 Vis channels and 6 NIR channels, for the real-time non-destructive prediction of six potato quality indicators. After preprocessing the spectral data with mean normalization, a multiple linear regression (MLR) model was established to optimize the prediction performance of quality parameters. The six indicators evaluated were DC, SC, L*, a*, b*, and BI. Statistical analysis and cross-validation were further conducted to quantitatively evaluate the stability and credibility of the prediction model. Among these, the b* parameter demonstrated the most robust predictive performance, achieving a cross-validated coefficient of determination (R²_CV) of 0.881. The MLR model was integrated into the sensing hardware to realize synchronous data collection and prediction. This study provides a validated, low-cost, wireless solution for rapid potato quality assessment under controlled conditions, offering a potential alternative to conventional spectrometers and destructive chemical methods. Full article

Photosynthetic phenotypic parameters of winter wheat are important indicators of canopy physiological status, photosynthetic function, and crop growth. However, canopy-scale hyperspectral estimation of these parameters remains affected by canopy structural heterogeneity, environmental variation, and mixed spectral signals. This study evaluated the contribution of visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) regions and their combinations to estimating photosynthetic phenotypic parameters of winter wheat. Field experiments were conducted under three nitrogen application levels and 65 winter wheat genotypes, and a total of 507 valid canopy-level samples were used for model development and validation. Competitive adaptive reweighted sampling (CARS) was used to select characteristic wavelengths, and partial least squares regression (PLSR), Bayesian ridge regression (BR), and backpropagation neural network (BPNN) were applied to construct estimation models. Model performance was assessed using R², RMSE, and RPD. Results showed that NIR-based models achieved the best overall performance, with the highest validation R² of 0.828 for photosynthetic rate. The VIS + NIR combination showed stable predictive ability across multiple parameters, whereas SWIR-only models showed limited performance, with R² values below 0.5 for most parameters. Photosynthetic rate, intercellular CO₂ concentration, performance index on an absorption basis, and chlorophyll a content were predicted more accurately than the other traits. These findings indicate that canopy hyperspectral data can support quantitative monitoring of photosynthetic phenotypic parameters, and that NIR-related structural and scattering information plays a key role in winter wheat canopy phenotyping. Full article

Pastures are the main feed source for beef cattle production, a sector in which Brazil plays a prominent global role. This study aimed to develop predictive models for dry matter yield (DM yield, kg ha⁻¹), crude protein (CP), and neutral detergent fiber (NDF) in Urochloa brizantha tropical pastures using laboratory VIS-NIR-SWIR spectroscopy, and to identify spectral patterns associated with these variables. Samples were collected from a commercial pasture area of approximately 200 ha, subdivided into 19 paddocks cultivated with Urochloa brizantha cv. Marandu and managed under rotational grazing during 2023. Forage samples were oven-dried, ground, and spectrally measured using a FieldSpec spectroradiometer (350–2500 nm). Partial least squares regression (PLSR) models were calibrated and evaluated using cross-validation, and informative wavelengths were identified using Variable Importance in Projection (VIP) scores. DM variability was mainly associated with near-infrared regions, CP with visible and near-infrared regions, and NDF with the visible region. Models calibrated with VIP-selected wavelengths achieved acceptable performance for CP (R²CV = 0.74) and NDF (R²CV = 0.72), whereas the general full-spectrum models showed moderate performance for CP (R²CV = 0.57) and acceptable performance for NDF (R²CV = 0.75). Temporal transferability varied among sampling periods, with greater robustness for CP and NDF than for DM. Overall, DM prediction remained limited and showed poor temporal transferability. Full article

Quality deterioration of chilled lamb during storage poses a challenge to meat quality and safety control, making rapid and accurate freshness-grade classification essential. Existing methods based on either spectral information or RGB image information alone are insufficient to simultaneously characterize internal chemical changes and external appearance changes during lamb quality deterioration. To address this issue, this study developed a chilled lamb freshness-grade classification method by integrating deep features from two-dimensional visible–near-infrared (Vis–NIR) spectral encoding with RGB image features. In this method, one-dimensional Vis–NIR spectra were transformed into two-dimensional encoded images using Gramian angular difference field (GADF), Gramian angular summation field (GASF), Markov transition field (MTF), and recurrence plot (RP) to enhance the representation of inter-wavelength structural relationships in spectral sequences, thereby compensating for the limited ability of conventional one-dimensional spectral modeling to capture global correlations and local variation information. Meanwhile, recursive feature elimination (RFE)-selected spectral deep features were fused with Spearman-selected RGB image features to construct a deep–shallow classification model. The results showed that the fusion models outperformed the single-modality models, with GADF(10%)+Image-SVM achieving the best performance, yielding an accuracy, F1-score, and MCC of 0.966, 0.957, and 0.946, respectively. Shapley additive explanations (SHAP) analysis further indicated that GADF deep features were the primary contributors, while RGB image features provided effective complementary information, demonstrating the potential of the proposed method for rapid and nondestructive freshness-grade classification of chilled lamb. Full article

The development of transparent multifunctional coatings capable of combining optical properties with mechanical durability remains a significant challenge in advanced materials engineering. In this study, novel hybrid coatings based on a poly(vinyl butyral)/epoxy resin (PVB/epoxy) matrix reinforced with tungsten disulfide (WS₂) nanostructures were developed and examined for potential application in camouflage protection of glass surfaces. Camouflage aims to reduce the detectability of an object by minimizing the optical contrast between the object and its surrounding environment. For transparent substrates such as glass, this objective is particularly demanding because the transparency must be preserved while reducing unwanted surface reflection and optical signatures over relevant spectral ranges. For this purpose, in this research two types of nanostructures were investigated: fullerene-like nanoparticles (IF-WS₂) and inorganic nanotubes (INT-WS₂). The coatings were fabricated via ultrasonically assisted solution dispersion followed by casting over the glass plates and Teflon molds, and solvent evaporation. Structural, thermal, optical, and mechanical properties were systematically evaluated using SEM, FTIR, DSC, UV-Vis-NIR spectroscopy, gloss measurements, hardness testing, and cavitation wear resistance analysis. The incorporation of WS₂ nanostructures led to improved mechanical performance, with increased hardness and enhanced resistance to cavitation-induced wear. Optical characterization showed moderate reductions in reflectance and controlled transmittance in the visible and near-infrared regions, while overall transparency was maintained. The results indicate that WS₂ nanostructures contribute to both light scattering and absorption, leading to reduced specular reflection and improved optical masking potential. The findings demonstrate that hybrid PVB/epoxy/WS₂ coatings offer a promising approach for designing transparent, mechanically resistant coatings with tunable optical properties, with potential applications in protective glass systems and advanced functional surfaces. Full article

Rapid and non-destructive authentication of herbal medicines is important for quality control and market supervision. This study established an interpretable spectral evidence learning framework for visible and near-infrared (Vis/NIR) imaging-based authentication of Codonopsis Radix (CR) and Aurantii Fructus (AF). Compact 31-band mean gray-value spectra were analyzed at ROI and sample levels. CR sample-level spectra were obtained by ROI-group averaging, whereas AF records were retained as individual sample spectra with image-group information used for leakage-controlled validation. Raw spectra, Savitzky–Golay smoothing, multiplicative scatter correction, and standard normal variate correction were compared with machine-learning and deep-learning classifiers. A fold-contained lightweight diffusion (LD) module was further introduced to provide class-conditioned spectral augmentation and denoising-error evidence. Under grouped cross-validation, the strongest non-LD Linear SVM models achieved accuracy/macro-F1 values of 0.9231/0.9238 for CR and 0.9025/0.9018 for AF. After LD augmentation, the best LD-augmented SVM models reached macro-F1 values of 0.9427 and 0.9197, respectively. Across all evaluated model–dataset combinations, LD increased the overall mean macro-F1 from 0.7302 to 0.8189. Model-aligned wavelength evidence and top-wavelength subset tests further showed that selected LED-band subsets retained useful discriminative information within the present imaging configuration. These results support the feasibility of compact Vis/NIR image-based authentication of herbal materials under grouped validation. Full article

Solar-driven hydrogen evolution is limited by the poor ability of conventional photocatalysts to utilize the visible–near-infrared (Vis–NIR) region, which accounts for ~95% of the solar spectrum. Here, we design an upconversion-plasmonic Fabry–Pérot cavity (Al/NaYF₄:Yb³⁺,Tm³⁺/Au/TiO₂) to achieve cascaded photon upcycling for efficient solar hydrogen production. In this architecture, the NaYF₄:Yb³⁺,Tm³⁺ layer serves as the dielectric medium of the cavity, enabling multiple light reflections and enhanced NIR-to-UV/Vis upconversion. The upconverted photons, together with the incident light, are further concentrated by the adjacent Au layer via surface plasmon resonance, promoting hot-electron generation and injection into TiO₂. As a result, the optimized structure achieves a broadband absorption efficiency of 60.57% and a hydrogen evolution rate of 19.86 mmol·g⁻¹·h⁻¹ from polylactic acid wastewater, 124 times higher than that of pristine TiO₂. This work provides a scalable strategy for broadband solar harvesting and plastic-waste-to-hydrogen conversion. Full article