Search Results (31)

Proper nutrition is a fundamental aspect to maintaining overall health and well-being, influencing both physical and social aspects of human life; an unbalanced or inadequate diet can lead to various nutritional deficiencies and chronic health conditions. In today’s fast-paced world, monitoring nutritional intake has become increasingly important, particularly for those with specific dietary needs. While smartphone-based applications using image recognition have simplified food tracking, they still rely heavily on user interaction and raise concerns about practicality and privacy. To address these limitations, this paper proposes a novel, compact spectroscopic sensing platform for automatic beverage recognition. The system utilizes the AS7265x commercial sensor to capture the spectral signature of beverages, combined with a K-Nearest Neighbors (KNN) machine learning algorithm for classification. The approach is designed for integration into everyday objects, such as smart glasses or cups, offering a noninvasive and user-friendly alternative to manual tracking. Through optimization of both the sensor configuration and KNN parameters, we identified a reduced set of four wavelengths that achieves over 96% classification accuracy across a diverse range of common beverages. This demonstrates the potential for embedding accurate, low-power, and cost-efficient sensors into Internet of Things (IoT) devices for real-time nutritional monitoring, reducing the need for user input while enhancing accessibility and usability. Full article

Background/Objectives: The increased SNR available at 7T combined with fast readout trajectories enables accelerated spectroscopic imaging acquisitions for clinical applications. In this report, we evaluate the performance of a Hadamard slice encoding strategy with a 2D rosette trajectory for multi-slice fast spectroscopic imaging at 7T. Methods: Moderate-TE (~40 ms) spin echo and J-refocused polarization transfer sequences were acquired with simultaneous Hadamard multi-slice excitations and rosette in-plane encoding. The moderate spin echo sequence, which targets singlet compounds (i.e., N-acetyl aspartate, creatine, and choline), uses cascaded multi-slice RF excitation pulses to minimize the chemical shift dispersion error. The J-refocused sequence targets coupled spin systems (i.e., glutamate and myo-inositol) using simultaneous multi-slice excitation to maintain the same TE across all slices. A modified Hadamard slice encoding strategy was used to decrease the peak RF pulse amplitude of the simultaneous multi-slice excitation pulse for the J-refocused acquisition. Results: The accuracy of multi-slice and single-slice rosette spectroscopic imaging (RSI) is comparable to conventional Cartesian-encoded spectroscopic imaging (CSI). Spectral analyses for the J-refocused studies of glutamate and myo-inositol show that the Cramer Rao lower bounds are not significantly different between the fast RSI and conventional CSI studies. Linear regressions of creatine/N-acetyl aspartate and glutamate/N-acetyl aspartate with tissue gray matter content are consistent with literature values. Conclusions: With minimal gradient demands and fast acquisition times, the 2.2 min to 9 min for single- to four-slice RSI acquisitions are well tolerated by healthy subjects and tumor patients, and show results that are consistent with clinical outcomes. Full article

Anthocyanins are widely found in plants and have significant functions. The accurate detection and quantitative assessment of anthocyanin content are essential to assess its functions. The anthocyanin content in plant tissues is typically quantified by wet chemistry and spectroscopic techniques. However, these methods are time-consuming, labor-intensive, tedious, expensive, destructive, or require expensive equipment. Digital photography is a fast, economical, efficient, reliable, and non-invasive method for estimating plant pigment content. This study examined the anthocyanin content of Rosa chinensis petals using digital images, a back-propagation neural network (BPNN), and the random forest (RF) algorithm. The objective was to determine whether using RGB indices and BPNN and RF algorithms to accurately predict the anthocyanin content of R. chinensis petals is feasible. The anthocyanin content ranged from 0.832 to 4.549 µmol g⁻¹ for 168 samples. Most RGB indices were strongly correlated with the anthocyanin content. The coefficient of determination (R²) and the ratio of performance to deviation (RPD) of the BPNN and RF models exceeded 0.75 and 2.00, respectively, indicating the high accuracy of both models in predicting the anthocyanin content of R. chinensis petals using RGB indices. The RF model had higher R² and RPD values, and lower root mean square error (RMSE) and mean absolute error (MAE) values than the BPNN, indicating that it outperformed the BPNN model. This study provides an alternative method for determining the anthocyanin content of flowers. Full article

Halloumi cheese has recently gained a Protected Designation of Origin (PDO) indicator, which is related to the place (Cyprus) in which halloumi cheese is made. The PDO label is linked with several requirements, e.g., milk species, quantities, etc.; thus, it is important to study this product regarding authenticity. The utility of using two spectroscopic techniques, hyperspectral imaging (HSI) (400–1000 nm) and conventional near-infrared spectroscopy (NIR) (800–2500 nm) were assessed for the discrimination of 17 Cypriot halloumi cheese types, which could be categorized as of cow or goat–sheep origin. The aim of this study was to obtain spectral information for halloumi cheese using other promising infrared and imaging spectroscopic techniques as a comparison to a previously acquired mid-infrared (MIR) spectroscopy dataset. NIR and HSI are both fast and easy techniques in application, both of which provide significant information in food analysis. Chemometric analysis was crucial for interpreting the spectroscopic data by applying the unsupervised methods: principal component analysis (PCA) and hierarchical cluster analysis (HCA). The HSI model was found to be based intuitively on the appearance of cheese samples after freeze-drying (e.g., color; yellow/white, and texture; oily/dry), while the NIR grouping of samples was determined to be based on composition, mainly fat, protein and lactose content of the cheese samples. The HSI model returned distinct clusters of the two halloumi cheese types, cow and goat–sheep origin, with one outlier (16/17 accuracy; 94%), while the NIR model proved less accurate (13/17; 76%). Full article

Tea plant cultivar identification is normally achieved manually or by spectroscopic, chromatographic, and other methods that are time-consuming and often inaccurate. In this paper, a method for the identification of three tea cultivars with similar leaf morphology is proposed using transfer learning by five pre-trained models: EfficientNet-B0, MobileNetV2, MobileNetV3, MobileViT-S, and ShuffleNetV2. The results showed that the best test accuracy percentages for EfficientNet-B0, MobileNetV2, MobileNetV3, MobileViT-S, and ShuffleNetV2 were 98.33, 99.67, 99.33, 98.67, and 99.00%, respectively. The most lightweight model was ShuffleNetV2, and the fastest combination was ShuffleNetV2 with 112 × 112 image resolution. Considering accuracy, the number of parameters, and floating point operations (FLOPs), MobileNetV2 was not only the most accurate model, but also both lightweight and fast. The present research could benefit both farmers and consumers via identifying tea cultivars without destructive techniques, a factor that would reduce the adulteration of commodity tea. Full article

To reduce the water footprint in agriculture, the recent push toward precision irrigation management has initiated a sharp rise in photonics-based hydration sensing in plants in a non-contact, non-invasive manner. Here, this aspect of sensing was employed in the terahertz (THz) range for mapping liquid water in the plucked leaves of Bambusa vulgaris and Celtis sinensis. Two complementary techniques, broadband THz time-domain spectroscopic imaging and THz quantum cascade laser-based imaging, were utilized. The resulting hydration maps capture the spatial variations within the leaves as well as the hydration dynamics in various time scales. Although both techniques employed raster scanning to acquire the THz image, the results provide very distinct and different information. Terahertz time-domain spectroscopy provides rich spectral and phase information detailing the dehydration effects on the leaf structure, while THz quantum cascade laser-based laser feedback interferometry gives insight into the fast dynamic variation in dehydration patterns. Full article

Macrophages are important cells of the innate immune system that play many different roles in host defense, a fact that is reflected by their polarization into many distinct subtypes. Depending on their function and phenotype, macrophages can be grossly classified into classically activated macrophages (pro-inflammatory M1 cells), alternatively activated macrophages (anti-inflammatory M2 cells), and non-activated cells (resting M0 cells). A fast, label-free and non-destructive characterization of macrophage phenotypes could be of importance for studying the contribution of the various subtypes to numerous pathologies. In this work, single cell Raman spectroscopic imaging was applied to visualize the characteristic phenotype as well as to discriminate between different human macrophage phenotypes without any label and in a non-destructive manner. Macrophages were derived by differentiation of peripheral blood monocytes of human healthy donors and differently treated to yield M0, M1 and M2 phenotypes, as confirmed by marker analysis using flow cytometry and fluorescence imaging. Raman images of chemically fixed cells of those three macrophage phenotypes were processed using chemometric methods of unmixing (N-FINDR) and discrimination (PCA-LDA). The discrimination models were validated using leave-one donor-out cross-validation. The results show that Raman imaging is able to discriminate between pro- and anti-inflammatory macrophage phenotypes with high accuracy in a non-invasive, non-destructive and label-free manner. The spectral differences observed can be explained by the biochemical characteristics of the different phenotypes. Full article

With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend. Full article

Current triage for women with post-menopausal bleeding (PMB) to diagnose endometrial cancer rely on specialist referral for intimate tests to sequentially image, visualise and sample the endometrium. A point-of-care non-invasive triage tool with an instant readout could provide immediate reassurance for low-risk symptomatic women, whilst fast-tracking high-risk women for urgent intrauterine investigations. This study assessed the potential for infrared (IR) spectroscopy and attenuated total reflection (ATR) technology coupled with chemometric analysis of the resulting spectra for endometrial cancer detection in urine samples. Standardised urine collection and processing protocols were developed to ensure spectroscopic differences between cases and controls reflected cancer status. Urine spectroscopy distinguished endometrial cancer (n = 109) from benign gynaecological conditions (n = 110) with a sensitivity of 98% and specificity of 97%. If confirmed in subsequent low prevalence studies embedded in PMB clinics, this novel endometrial cancer detection tool could transform clinical practice by accurately selecting women with malignant pathology for urgent diagnostic work up whilst safely reassuring those without. Full article

The large-scale configuration of the UV solar corona at the minimum activity between solar cycles 22 and 23 is explored in this paper. Exploiting a large sample of spectroscopic observations acquired by the Ultraviolet Coronagraph Spectrometer aboard the Solar and Heliospheric Observatory in the two-year period of 1996–1997, this work provides the first-ever monochromatic O vi 1032 Å image of the extended corona, and the first-ever two-dimensional maps of the kinetic temperature of oxygen ions and the O vi$1037/1032$ Å doublet intensity ratio (a proxy for the outflow velocity of the oxygen component of the solar wind), statistically representative of solar minimum conditions. A clear dipolar magnetic structure, both equator- and axis-symmetric, is distinctly shown to shape the solar minimum corona, both in UV emission and in temperature and expansion rate. This statistical approach allows for robust establishment of the key role played by the magnetic field divergence in modulating the speed and temperature of the coronal flows, and identification of the coronal sources of the fast and slow solar wind. Full article

Bioconjugation of antibodies with various payloads has diverse applications across various fields, including drug delivery and targeted imaging techniques. Fluorescent immunoconjugates provide a promising tool for cancer diagnostics due to their high brightness, specificity, stability and target affinity. Fluorescent antibodies are widely used in flow cytometry for fast and sensitive identification and collection of cells expressing the target surface antigen. Nonetheless, current approaches to fluorescent labeling of antibodies most often use random modification, along with a few rather sophisticated site-specific techniques. The aim of our work was to develop a procedure for fluorescent labeling of immunoglobulin G via periodate oxidation of antibody glycans, followed by oxime ligation with fluorescent oxyamines. Here, we report a novel technique based on an in situ oxime ligation of ethoxyethylidene-protected aminooxy compounds with oxidized antibody glycans. The approach is suitable for easy modification of any immunoglobulin G, while ensuring that antigen-binding domains remain intact, thus revealing various possibilities for fluorescent probe design. The technique was used to label an antibody to PRAME, a cancer-testis protein overexpressed in a number of cancers. A 6H8 monoclonal antibody to the PRAME protein was directly modified with protected-oxyamine derivatives of fluorescein-type dyes (FAM, Alexa488, BDP-FL); the stoichiometry of the resulting conjugates was characterized spectroscopically. The immunofluorescent conjugates obtained were applied to the analysis of bone marrow samples from patients with oncohematological diseases and demonstrated high efficiency in flow cytometry quantification. The approach can be applied for the development of various immunofluorescent probes for detection of diagnostic and prognostic markers, which can be useful in anticancer therapy. Full article

Optical systems are used for analysing the internal composition and the external properties in food. The measurement of the lycopene content in fruits and vegetables is important because of its benefits to human health. Lycopene prevents cardiovascular diseases, cataracts, cancer, osteoporosis, male infertility, and peritonitis. Among the optical systems focused on the estimation and identification of lycopene molecule are high-performance liquid chromatography (HPLC), the colorimeter, infrared near NIR spectroscopy, UV-VIS spectroscopy, Raman spectroscopy, and the systems of multispectral imaging (MSI) and hyperspectral imaging (HSI). The main objective of this paper is to present a review of the current state of optical systems used to measure lycopene in fruits. It also reports important factors to be considered in order to improve the design and implementation of those optical systems. Finally, it was observed that measurements with HPLC and spectrophotometry present the best results but use toxic solvents and require specialized personnel for their use. Moreover, another widely used technique is colorimetry, which correlates the lycopene content using color descriptors, typically those of CIELAB. Likewise, it was identified that spectroscopic techniques and multispectral images are gaining importance because they are fast and non-invasive. Full article

Modern light microscopy imaging techniques have substantially advanced our knowledge about the ultrastructure of plant cells and their organelles. Laser-scanning microscopy and digital light microscopy imaging techniques, in general—in addition to their high sensitivity, fast data acquisition, and great versatility of 2D–4D image analyses—also opened the technical possibilities to combine microscopy imaging with spectroscopic measurements. In this review, we focus our attention on differential polarization (DP) imaging techniques and on their applications on plant cell walls and chloroplasts, and show how these techniques provided unique and quantitative information on the anisotropic molecular organization of plant cell constituents: (i) We briefly describe how laser-scanning microscopes (LSMs) and the enhanced-resolution Re-scan Confocal Microscope (RCM of Confocal.nl Ltd. Amsterdam, Netherlands) can be equipped with DP attachments—making them capable of measuring different polarization spectroscopy parameters, parallel with the ‘conventional’ intensity imaging. (ii) We show examples of different faces of the strong anisotropic molecular organization of chloroplast thylakoid membranes. (iii) We illustrate the use of DP imaging of cell walls from a variety of wood samples and demonstrate the use of quantitative analysis. (iv) Finally, we outline the perspectives of further technical developments of micro-spectropolarimetry imaging and its use in plant cell studies. Full article

Colorectal cancer is the second most frequently diagnosed cancer worldwide. Conventional diagnostics methods of colorectal cancer can detect it at an advanced stage. Spectroscopic methods, including Raman spectroscopy and imaging, are becoming more and more popular in medical applications, and allow fast, precise, and unambiguous differentiation of healthy and cancerous samples. The most important advantage of Raman spectroscopy is the ability to identify biomarkers that help in the differentiation of healthy and cancerous cells based on biochemistry of sample and spectra typical for lipids, proteins, and DNA. The aim of the study was to evaluate the biochemical and structural features of human colon cell lines based on Raman spectroscopy and imaging: normal cells CCD-18 Co, normal cells CCD-18 Co under oxidative stress conditions, and normal cells CCD-18 Co at first treated by using tert-Butyl hydroperoxide and then supplemented by vitamin C in high concentration to show the protective role of vitamin C in micromolar concentrations against ROS (Reactive Oxygen Species). Raman data obtained for normal cells injured by ROS were compared with spectra typical for cancerous cells. Statistically assisted analysis has shown that normal ROS-injured and cancerous human colon cells can be distinguished based on their unique vibrational properties. The research carried out proves that label-free Raman spectroscopy may play an important role in clinical diagnostics differentiation of normal and cancerous colon cells and may be a source of intraoperative information supporting histopathological analysis. Full article

Hyperpolarized metabolic MRI with ${}^{13}$C-labeled agents has emerged as a powerful technique for in vivo assessments of real-time metabolism that can be used across scales of cells, tissue slices, animal models, and human subjects. Hyperpolarized contrast agents have unique properties compared to conventional MRI scanning and MRI contrast agents that require specialized imaging methods. Hyperpolarized contrast agents have a limited amount of available signal, irreversible decay back to thermal equilibrium, bolus injection and perfusion kinetics, cellular uptake and metabolic conversion kinetics, and frequency shifts between metabolites. This article describes state-of-the-art methods for hyperpolarized metabolic MRI, summarizing data acquisition, reconstruction, and analysis methods in order to guide the design and execution of studies. Full article