Spectral Data Research Articles

Multispectral two-dimensional temperature field reconstruction using particle swarm optimization and the Broyden–Fletcher–Goldfarb–Shanno algorithm with multiple extreme value optimization

The flame temperature is a crucial parameter in combustion, indicating heat generation and transfer. However, determining the correct temperature becomes challenging when flame emissivity is unknown. This article presents a multispectral 2D temperature field reconstruction method using particle swarm optimization and the Broyden–Fletcher–Goldfarb–Shanno algorithm, combined with multivariate extreme-value optimization. This method establishes an objective function based on the correlation between the true temperature and spectral data. The objective function was minimized by adjusting the emissivity, enabling the reconstruction of 2D temperature and emissivity images without assuming an emissivity model. Calibration with a multispectral camera yielded the spectral response coefficients, and three-time spline interpolation was used to verify the calibration data. The reconstructed 2D temperature field showed a mean absolute error (MAE) of 4.61 and structural similarity (SSIM) of 0.995. The reconstructed emissivity image had an MAE range of 0.04 to 0.053 and an SSIM range of 0.897 to 0.915. The system, tested on a butane flame, showed a temperature range of 1001 K to 1356 K, with an average temperature of 1194 K and emissivity values ranging from 0.3166 to 0.8621. The results align with the expected combustion process and radiation characteristics distribution of the butane flame.

Rapid quantitative analysis of three elements (Al, Mg and Fe) in molten zinc based on laser-induced breakdown spectroscopy combined with machine learning algorithm

Hot-dip galvanizing represents one of the most cost-effective methods for the prevention of metal corrosion, and is therefore employed extensively across a range of fields. Carrying out the research and development of technology and devices for quantitative analysis of chemical elements in hot-dip galvanising process can provide theoretical basis and technical support for the efficiency of hot-dip galvanising process and reduction of energy consumption. A machine learning-assisted LIBS combined with a programmable logic controller (PLC) for simultaneous on-line/in-site monitoring of multiple elements in hot-dip galvanising solution (molten zinc) was developed in the current study. The LIBS spectral data of the on-site hot-dip galvanising solution was collected under optimised experimental conditions. In order to further reduce the influence of experimental noise on the analysis performance, the on-site LIBS spectral data were preprocessed and anomalous spectral data were screened based on normalisation and principal component analysis-mahalanobis distance (PCA-MD). On the basis of the optimised data, component prediction models for three key elements of on-site hot-dip galvanising solution were constructed. The storage and re-call of the model was achieved based on python combined with LabVIEW, thus real-time prediction of the on-site component content of hot-dip galvanising solution was achieved. The results show that the random forest model presents the best prediction results, in which the R2 is 0.9978 and the RMSE is 0.0013% for Al, the R2 is 0.9984 and the RMSE is 0.0011% for Mg, and the R2 is 0.9932 and the RMSE is 0.0001% for Fe. From the on-site analysis results of the constructed model, its MRE of Al, Mg and Fe is 0.0098, 0.0236, and 0.2102, respectively. In summary, the in-situ/on-line analysis system of hot-dip galvanising solution combined with machine learning constructed in this study shows excellent performance, which can satisfy the needs of hot-dip galvanising solution production site. This study is expected to provide theoretical basis and technical reference for quality control and process optimisation in other production sites in the metallurgical field.

Inverse spectral Love problem via Weyl–Titchmarsh function

In this paper, we prove an inverse resonance theorem for the half-solid with vanishing stresses on the surface via Weyl–Titchmarsh function. Using a semi-classical approach, it is possible to simplify this three-dimensional problem of the elastic wave equation for the half-solid as a Schrödinger equation with Robin boundary conditions on the half-line. The goal of the paper is to establish a method to recover the potential from eigenvalues and resonances through the Weyl–Titchmarsh function for non-self-adjoint problems and to establish a one-to-one and onto map between suitable function spaces. Moreover, we produce an algorithm in order to retrieve the shear modulus from the eigenvalues and resonances, via the spectral data.

Synthesis of new pyrazoles, thiadiazoles, and trizolotriazines compounds that act as an antibacterial agents using C-ethoxycarbonylhydrazonoyl chloride precursor: Molecular docking simulation and in silico ADMET prediction studies

Treatment of C-ethoxycarbonylhydrazonoyl chloride with active methylene-containing compounds such as dibenzoylmethane or malononitrile in sodium ethoxide solution yielded in each case pyrazole derivatives. The latter pyrazoles were used as a useful precursors in the synthesis of new heterocyclic compounds like pyrazoles and 1,3,4-thiadizaoles upon reaction with hydrazonoyl halides. Also, the reaction of C-ethoxycarbonylhydrazonoyl chloride with the approperiate triazinethiones in chloroform in the presence of catalytic amount of triethylamine at reflux yielded the corresponding triazolotriazines. The structures of the newly synthesized compounds were confirmed based on elemental analyses and spectral data. The antibacterial activities were studied against two gram-positive and two gram-negative bacteria, the results represented that compound 26a showed antibacterial activity against Salmonella (22 mm) and E. coli (6 mm), as well as compound 26b against Salmonella (5 mm) and Staphylococcus aureus (10 mm) while the rest compounds showed no antibacterial activities. The molecular docking simulation was investigated for the most active compounds 26a and 26b. The results confirm that compounds 26a and 26b are promising candidates for potential inhibitors of DNA gryA (P37411) of Salmonella and Transpeptidases of Staphylococcus aureus.

Correlation Study of Graphitic Shell Encapsulated Nickel: A Multi-Spectroscopic Approach

Graphite-encapsulated metal nanoparticles hold great potential across various applications due to their unique combination of metal and graphitic properties. While Raman spectroscopy is widely used to assess the graphitic shells, metrics derived from prominent Raman bands often prove unreliable when analyzing such structures with a large number of heterogeneous flakes and are mostly applicable to few-layer graphene. However, researchers continue to face challenges in identifying reliable metrics that accurately reflect two key properties of graphitic shells: thickness and degree of graphitization. In this study, graphitic shells were synthesized via plasma-enhanced chemical vapor deposition (PECVD) at low temperatures (500˚C), seeded by nickel nano-islands. A full-factorial experiment was designed to explore a diverse sample space by varying two key input factors: the initial thickness of the nickel seeding film and the duration of carbon deposition. Using resource-intensive surface characterization techniques (SEM, EDS, XPS, and AFM), we verified encapsulation efficiency and morphology, identifying strong correlations between the input factors and properties of the shells. To gain insights into structural characteristics, we employed conventional spectroscopy techniques (Raman, UV-Vis-nIR transmission, and Transient Absorption Spectroscopy), introducing 107 metrics derived from spectral data. Principal Component Analysis (PCA) aligned the input factors, and their corresponding shell properties, with the first two principal components. Accordingly, we identified the most relevant metrics including new candidates derived from less prominent Raman bands. Additionally, inter-metric correlations enable interchangeable use of spectroscopy techniques. This study marks the first multi-spectroscopic correlation analysis of graphitic shells and provides a novel framework for evaluating their properties.

Contribution of data acquired from spectroscopic, genomic and microbiological analyses to enhance mussels’ quality assessment

In this study, a large amount of heterogeneous data (i.e., microbiological, spectral and Next Generation Sequencing data) were obtained analyzing mussels of different species and origin, to acquire a comprehensive view about the quality and safety of these products. More specifically, spectral data were collected through Fourier transform Infrared (FTIR) spectroscopy, while the overall profile of microorganisms present in these samples, affecting quality and safety of mussels throughout storage, was determined through Next Generation Sequencing (NGS) using 16S rRNA metabarcoding analysis. In parallel, conventional microbiological analysis for the estimation of culturable spoilage microorganisms (total aerobes, Pseudomonas spp., B. thermosphacta, Shewanella spp. and Enterobacteriaceae) was applied. Different machine learning algorithms, namely Partial Least Square (PLS), Support Vector Machines (SVM), k-Nearest Neighbors (kNN), Random Forest (RF) Neural Networks (NN)) were applied accordingly, to assess the potential of FTIR and NGS data to provide useful information about mussels’ microbiological quality.Microbial counts ranged from 3.5 to 9.0 log CFU/g, while NGS revealed several bacterial genera such as Pseudoalteromonas, Psychrobacter, Acinetobacter, Pseudomonas, B. thermosphacta, Psychrobacter, Kistimonas, Psychrilyobacter to affect the quality of mussels, depending on the mussel species, batch and storage conditions. According to the performance metrics, the SVM algorithm in tandem with FTIR achieved the highest prediction accuracy for microbial counts in M. chilensis samples (Rsquared; 0.89, RMSE; 0,74), while in the case of predicting the abundance of microbial genera using spectroscopic data, the best performing algorithm varied by bacterial genus. Indicatively, in M. chilensis, RF, kNN and NN performed better in predicting Enterococcus, Enhydrobacterium and Pseudoalteromonas, respectively (Rsquared = 0.92, 0.93, 0.99). Associations between genomics data and specific spectral regions were further investigated, revealing certain spectral regions that are associated with mussels’ quality and safety.The application of “multi-omics” in seafood supply chain can provide insightful information about mussels’ quality and safety compared to the methodologies followed in current quality and safety management systems.

Assessing water quality environmental grades using hyperspectral images and a deep learning model: A case study in Jiangsu, China

Water quality assessment is essential for effective environmental management, yet traditional methods such as chemical sampling are often labor-intensive and inefficient for large-scale, continual monitoring. This study addresses these limitations by leveraging hyperspectral images (HSIs) analysis and introducing a capsule network (CapsNet) model enhanced with a multidimensional integration attention (MDIA) mechanism. The model is specifically designed to integrate both channel and spatial information, enabling precise water quality grade assessment by detecting subtle features within HSIs data. To validate the performance of the model, spectral data from 5 water quality regions are collected and processed via a UAV-carried spectrometer, with 4503 water quality data samples. Rigorous classification experiments demonstrated that the model achieves 98.73 % accuracy, with an average improvement of 4.89 % compared with the other models. This approach significantly improves decision support systems for water resource management, facilitating the sustainable use of water resources.

Spectral data analysis based on bagging stochastic configuration networks

Although stochastic configuration networds(SCN) has the universal approximation property and faster learning speed,during the process of model construction,the randomness of weight and biases assignment as well as the uncertainty of model structure lead to instability.Inspired by bagging,an ensemble model named bagging SCN is proposed to adress the limitation of the single model.Firstly,multiple different training subsets are extracted by bootstrap sampling.Then the SCN submodels are trained on each subset.Finally,the median output of these submodels is taken as the final prediction.Predictions made by bagging SCN are tested on two public datasets.The performance of bagging SCN is then compared with other techniques,including SCN,bagging SCN with other aggregating rules.Experimental results demonstrate that bagging SCN proposed in this study exhibits good stability and high prediction accuracy,making it suitable for quantitative analysis of spectral data.

Non-Destructive Identification of Wool and Cashmere Fibers Based on Cascade Optimizations of Interval-Wavelength Selection Using NIR Spectroscopy

ABSTRACT Near-infrared (NIR) spectroscopy is an effective method for identifying wool and cashmere fibers, with high spectral data providing a wealth of information. However, a key issue is that the accuracy and robustness of subsequent estimates can be reduced by redundant and interfering wavelengths. For this reason, a novel interval-wavelength cascaded optimization method is proposed. Initially, the collected spectral data are preprocessed by standard normal variate transformation (SNV) to eliminate the scattering effect. Then, the backward interval partial least squares (BiPLS) algorithm is applied for the preliminary selection of spectral intervals, followed by the application of three different variable selection algorithms, competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and whale optimization algorithm (WOA), for secondary wavelength optimization, respectively. Finally, both support vector machine (SVM) and random forest (RF) discriminant models are built to identify the extracted subset of wavelengths. In the experimental stage, the cascade method BiPLS-WOA selects 36 wavelengths, in SVM, the accuracy of the validation set reaches 96.9%, and the area under the ROC curve (AUC) can reach 99.3%. The results demonstrate that the proposed method can eliminate redundant and collinear variables, thereby validating the effectiveness of distinguishing wool and cashmere fibers.

A NIRS-based recognition of coal and rock using convolution-multiview broad learning system

Achieving high production in the top coal caving process from thick coal seams is crucial. Thus, the timely decision of when to stop caving poses an urgent challenge to impact the mining loss rate and cost recovery. To address this issue, an innovative recognition system has been developed using Near-Infrared Spectroscopy (NIRS) technology. It stands out for its on-site usability, it enables rapid data collection and local recognition at the longwall face. Furthermore, to overcome the limitations of existing methods in adapting to variations in spectral data quality during on-site collection and the lack of integration of spectral data across different feature processing stages, a coal-rock recognition method has been developed which can ignore the influence of acquisition factors(granularity, light source angle, and detection sensor angle). This method incorporates the features of convolution and multi-view into the BLS model, the designed model structure exhibits a remarkable recognition accuracy of 99.78 %. The model was deployed into the recognition system, and experimental tests were conducted on the working face. The results showed that the recognition system can effectively identify the entire coal-caving process and achieve a recognition accuracy of 92.3 %. This capability is crucial for determining the optimal point to stop roof caving.

Feasibility and potential of terahertz spectral and imaging technology for Apple Valsa canker detection: A preliminary investigation

Apple Valsa canker (AVC) caused by the Ascomycete Valsa mali, seriously constrains the production and quality of apple fruits. The symptomless incubation characteristics of Valsa mali make it highly challenging to detect AVC at an early infection stage. After infecting the wound of apple bark, the pathogenic hyphae of AVC will expand and colonize the phloem tissue. Meanwhile, various enzymes and toxic substances released by hyphae cause the decomposition of cellulose and lignin, and the generation of poisonous secondary metabolites in bark tissue. However, these early symptoms of AVC are invisible from the bark’s appearance. Fortunately, Terahertz Spectral Imaging (ThzSI) technology with the advantage of penetrating, and fingerprinting is promising for detecting hidden or slight symptoms of the fungal infection. This study is a preliminary investigation of terahertz frequency-domain spectra for AVC in the early stage of infection. Healthy and two-week-infected apple tree branches were prepared for capturing ThzS images, and the spectral data were preprocessed by Multivariate scattering correction (MSC), Savitzky-Golay convolution smoothing (SG), and standard normal variate (SNV) respectively to remove data noise and improve data quality. Principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and random frog (RFROG) were employed to extract the spectral feature bands to eliminate redundant data and improve computational efficiency. Machine learning models were established based on the spectral features to detect AVC at an early infection stage, where 11 of them exhibited the best performance with F1-score of 99.72%. To further explore disease information in spatial spectra, imaging data were acquired using terahertz imaging technology. Based on imaging data, pseudo-color imaging, histogram equalization, and Otsu segmentation were employed to visualize early infection areas in apple barks. Furthermore, histogram feature (HF), shape feature (SF), and local binary pattern (LBP) extracted from terahertz spectral images were utilized to establish the SVM, RF, and KNN models. HF-SF-KNN and HF-SF-LBP-KNN with the best performance achieved F1-score of 98.82%. This study presents a preliminary application of terahertz spectral and imaging technology for early-stage AVC detection and demonstrates its feasibility. Additionally, it provides a new way to detect AVC, which expands the application of ThzSI technology in tree disease detection in orchards and lays the foundation for further research.

Rapid Detection of Oil Content in Camellia oleifera Kernels Based on Hyperspectral Imaging and Machine Learning

The oil content (OC) of kernels is one of the primary targets in the breeding of Camellia oleifera. However, the OC determination is labor-consuming and time-costing using traditional methods. In this study, a rapid and efficient OC detecting method was developed based on hyperspectral imaging (HSI). The OCs of 220C. oleifera clones were first determined using the Soxtec extraction method and hyperspectral images of all samples were obtained. Five spectral preprocessing methods and two dimensionality reduction methods was performed to eliminate hyperspectral noise. Based on the preprocessed spectral and OC data, OC predictive models were developed. The optimal OC prediction model was developed based on the characteristic wavelengths selected by competitive adaptive reweighted sampling from the preprocessed data by Savitzky–Golay smoothing and the first derivative method. The determination coefficient of this model was 0.9383, with a root mean squared error prediction of 1.7921% and residual predictive deviation of 4.0271. The further validation of this model by the other samples demonstrated it’s robustness and accuracy. The results reveal the potential of HSI in the rapid OC detection in C. oleifera. This will provide reference and guidance for the phenotype collection of C. oleifera.

Towards a Spectral Library of Medicinal and Aromatic Plant species (MAPs): Plant Discrimination and Wavelength Selection

The recognition and identification of Medicinal and Aromatic Plants species (MAPs) is a challenge for researchers and professionals in the field. To address this issue, this study aimed to examine the potential of using hyperspectral remote sensing, chemometrics and Machine Learning in discriminating between MAP species. Specifically, we tested the applicability of UV-Near-infrared Spectroscopy (UV-NIR), PCA and Partial Least-Squares Discriminant Analysis (PLS-DA) as tools for discrimination. The spectral signature data were obtained by UV-near infrared spectroscopy, covering a range of 325–1075 nm. Principal component analysis (PCA) was applied to reduce data dimensionality in order to minimize errors in PLS-DA model. This model was used to build a discriminant model based on the wavelengths obtained from PCA. The results show that it is effective to combine UV-NIR spectral features and PLS-DA to establish a discriminant model for accurately classifying the MAP species. The key wavelengths most involved in the discrimination were found in the NIR region and especially in ranges 516–548 and 628–1070 nm.

Identifying Crop Distress and Stress-Induced Plant Diseases Using Hyper Spectral Image Analysis

The purpose of this study is to determine whether hyper spectral images can be used to identify plant diseases and crop pressure from aerial photographs. With extensive research on the prevalent methods used closer to the problem, this study offers a potent strategy for identifying crop distress and illnesses using this efficient imaging technique. To identify the spectral fingerprints of common indications and symptoms of plant diseases and crop strains, this study evaluates the available hyper spectral photo datasets. After that, the data are examined using two learning algorithms—the highly randomized trees and the Random Woodland set of rules—to create predictions that are entirely dependent on the results that are discovered. In the end, a benchmarked set of test statistics is used to assess the prediction accuracy. The results of this study show that hyper spectral photo evaluation has a strong and promising utility for crop stress and disease identification. Hyper spectral light evaluation is a method for identifying plant diseases caused by strain on crops. By gathering and evaluating high-dimensional spectral reflection data from satellite or aircraft structures, details regarding the physiological homes of flowers can be identified. The health of plants, illnesses brought on by stress, and agricultural productivity predictions can all be made using these facts. Hyper spectral recordings can also be used to create actions that reduce agricultural losses and enhance the health of plants that are prone to disease.

Uncertainty of evaluation of spectral mismatch correction factor

Abstract Addressing the effect of partially correlated components of spectral irradiance data on spectral integrals poses a substantial challenge. This study employs Monte Carlo methods to conduct an uncertainty analysis of spectral integrals, with a focus on the uncertainty of the spectral mismatch correction factor. The investigation encompasses the primary types of uncertainty contributors of spectral irradiance, based on carefully calibrated FEL lamp measurements. A novel approach is introduced to estimate the impacts of partial correlations among spectral irradiance values at different wavelengths. The findings reveal that uncertainty components arising from partial correlations significantly outweigh those associated with full spectral correlation or complete lack of correlation at different wavelengths. This insight advances our understanding of uncertainty analysis of spectral integrals and underscores the importance of accounting for partial correlations in accurate measurements.

Remote sensing-based green and blue agricultural water footprint estimation at the river basin scale

Since the development of Water Footprint environmental indicator, significant research on blue and green crop water use and the respective water footprint estimations has been published. Such research is commonly approached using different methodologies that leverage tabulated values for crop development characterisation, while studies based on remote sensing data are less abundant, despite crop monitoring using remote sensing-based vegetation indices having demonstrated great capabilities and operability. To help fill this gap, we present a methodology that uses a remote sensing vegetation index time series from Sentinel-2 satellite near infra-red and red spectral bands data to derive basal crop coefficient time series to subsequently be used under the Remote Sensing-based Soil Water Balance approach that follows the globally operative FAO56 procedure. It provides pixel-based temporal and spatially distributed estimations of net irrigation requirements and adjusted crop evapotranspiration, with the aim being to divide up the latter and estimate the remote sensing-based green and blue crop water use and the subsequent green and blue water footprint. This is all done under the Agricultural Water Footprint Assessment framework for a growing crop or tree. This methodology was applied over a large, crop-diverse Spanish river basin district (Júcar) and across two different climatological years (humid vs. dry). Its feasibility was demonstrated by the acceptable behaviour of the remote sensing-based blue crop water use estimation for different herbaceous and woody crops, against the official dataset for irrigation water accounting at two water management scales (of a relative mean absolute error of 15.4 % in the case of the largest water user association and of 17.1 % in the case of the river basin water authorities’ own estimations). The proposed approach, which we call Remote Sensing-based Agricultural Water Accounting and Footprint, aims to provide reliable and accurate spatially and temporally distributed thematic cartography about the remote sensing-based blue and green crop water use and water footprint. This information is essential for water managers with the goal of generating transparent and complementary information to incorporate into their own working scales.

Rational design, synthesis and anticancer screening of 1,2,4-oxadiazole incorporated thieno[2,3-d]thiazole-isoxazole-pyridine derivatives

We have design and synthesized a new series of 1,2,4-oxadiazole incorporated thieno[2,3-d]thiazole-isoxazole-pyridine analogues (13a-j) and were confirmed by 1H NMR, 13C NMR and mass spectral data. Further, the newly synthesized compounds (13a–j) were evaluated for their preliminary anticancer activity against a panel of four human cancer cell lines such as MCF-7 (breast cancer), A549 (lung cancer), Colo-205 (colon cancer) & A2780 (ovarian cancer) by using of MTT method, the known chemotherapeutic agent as etoposide used as positive control. Most of the tested compounds were displayed good to moderate activities on all cell lines. The IC50 values showed compound ranges from 0.01 ± 0.009 µM to 8.41 ± 5.48 µM. Where positive control showed values range from 3.34 ± 0.152 µM to 0.17 ± 0.034 µM. Among them, compound 13a, 13b, 13c, 13d, 13e and 13f were showed more potent activity than etoposide. Predominantly, The compound 13a showed potent anticancer activity against MCF-7, A549, Colo-205, and A2780 cancer cell lines with IC50 values of 0.02 ± 0.007 µM, 0.01 ± 0.009 µM, 0.13 ± 0.052 µM, and 0.11 ± 0.083 µM.

Aggressiveness evaluation of borderline serous ovarian tumors by analysis of Psammoma bodies present in cancer tissues using micro-FTIR spectroscopy

Borderline ovarian tumor is a type of tumor with generally low malignant potential. However, these tumors pose diagnostic challenges with benign and malignant epithelial ovarian tumors because the clinical symptoms are similar and investigation procedures for specific diagnosis are still debated. In addition, a small number of borderlines transform into high-grade serous ovarian carcinoma with a poor prognosis. Therefore, tools improving a better characterization of high-risk subtypes of borderline tumors to enable understanding of possible unfavorable evolution are essential for patients’ management. Psammoma bodies (PBs) are microcalcifications found both in serous epithelial ovarian cancer and serous borderline tumors with possible correlation with disease progressionIn this work, the chemical composition of PBs found in the tissues of borderline, high-grade and low-grade ovarian tumors was evaluated using micro-FTIR spectroscopy. Applying principal component analysis to spectral data, it was observed that among the borderline tumors analyzed (1-bl,2-bland3-bl), the PBs of3-blshowed a different chemical content from that of the PBs of the high-grade and low-grade tumors, while the PBs of1-bland2-blappeared to have similar chemical content to the PBs of high- and low-grade tumors. The discriminating wavenumbers were found to be those related to carbonate CO32− (1454, 1413 cm−1and 872 cm−1) and phosphate PO43−(1018 cm−1and 960 cm−1) present in microcalcifications. A higher ratio between peak intensities at 1413 cm−1and 1018 cm−1(I1413/I1018) was observed in the PBs of3-blcompared with those of high- and low-grade ovarian carcinoma patients, which correlates with higher CO32− content. On the other hand, the PBs of1-bland2-blshowed a CO32−level close to that of the PBs of high- and low-grade patients. Several studies on microcalcifications in breast carcinoma have reported that increased carbonate content is related to decreased tumor aggressiveness. Therefore, case bl-3 appeared to be the less aggressive. The results obtained from FTIR analysis were in agreement with histopathological tumor classification and molecular analysis for BRAFV600E. The FTIR technique could become a reliable tool for identifying borderline low- and high-risk tumors.

Raman spectroscopy of large extracellular vesicles derived from human microvascular endothelial cells to detect benzo[a]pyrene exposure.

Extracellular vesicles (EVs) have shown great potential as biomarkers since they reflect the physio-pathological status of the producing cell. In the context of cytotoxicity, it has been found that exposing cells to toxicants leads to changes in protein expression and the cargo of the EVs they produce. Here, we studied large extracellular vesicles (lEVs) derived from human microvascular endothelial cells (HMEC-1) to detect the modifications induced by cell exposure to benzo[a]pyrene (B[a]P). We used a custom CaF2-based biochip which allowed hyphenated techniques of investigation: surface plasmon resonance imaging (SPRi) to monitor the adsorption of objects, atomic force microscopy (AFM) to characterise EVs' size and morphology, and Raman spectroscopy to detect molecular modifications. Results obtained on EVs by Raman microscopy and tip-enhanced Raman spectroscopy (TERS) showed significant differences induced by B[a]P in the high wavenumber region of Raman spectra (2800 to 3000cm-1), corresponding mainly to lipid modifications. Two types of spectra were detected in the control sample. A support vector machine (SVM) model was trained on the pre-processed spectral data to differentiate between EVs from cells exposed or not to B[a]P at the spectrum level; this model could achieve a sensitivity of 88% and a specificity of 99.5%. Thus, this experimental setup facilitated the distinction between EVs originating from two cell culture conditions and enabled the discrimination of EV subsets within one cell culture condition.

Development and validation of a new method by MIR-FTIR and chemometrics for the early diagnosis of leprosy and evaluation of the treatment effect

ObjectiveDevelop a new method for diagnosing leprosy and monitoring the pharmacological treatment effect of patients. Material and methodsPlasma samples from patients diagnosed with leprosy (n = 211) who had not yet received any pharmacological treatment were collected at a basic health unit in Brazil. After treatment, samples were collected from the same patients (n = 125). Plasma samples from healthy volunteers were also collected (n = 179) and used as a control group. All samples were analyzed by Fourier transform mid-infrared spectrophotometry (MIR-FTIR). The spectral data of the samples were subjected to chemometric analysis. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to predict diagnosis and monitor pharmacological treatment. ResultsThe PCA model successfully distinguished among three sample classes: healthy individuals, pre-treatment leprosy patients, and post-treatment leprosy patients. The PLS-DA algorithm accurately classified healthy, treated, and diseased samples, facilitating both reliable diagnosis and treatment monitoring for leprosy. The model achieved a sensitivity of 97% to 100%, specificity of 100%, and accuracy ranging from 99% to 100%. Furthermore, when tested on plasma samples from patients with other conditions—renal failure (n=1032), hypertriglyceridemia (n=100), hypercholesterolemia (n=100), and mixed dyslipidemia (n=100)—the model correctly classified these as negative for leprosy, with diagnostic specificity between 93% and 96%. ConclusionThe MIR-FTIR technique combined with PLS-DA analysis proved to be a highly effective tool for screening leprosy patients and monitoring treatment outcomes. Given its low cost, this method could be easily implemented in laboratories across emerging and low-income countries.