Partial Least Squares Discriminant Analysis Research Articles

Accurate felt-tip pen brands classification based on a convolutional neural network using data augmentation.

Ink analysis played an important role in document examination, but the limited dataset made it difficult for many algorithms to distinguish inks accurately. This article aimed to evaluate the feasibility of two data augmentation (DA) methods, Gaussian noise data augmentation (GNDA) and extended multiplicative signal augmentation (EMSA), for the classification of felt-tip pen ink brands. Four brands of felt-tip pens were analyzed using FT-IR spectroscopy. Five classification models were used, convolutional neural network (CNN), K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and partial least squares discriminant analysis (PLS-DA). The results showed that the datasets generated by GNDA and EMSA are similar to the original datasets and have some diversity. The EMSA method had optimal classification results when combined with CNN, with classification accuracy (ACC), precision (PRE), recall (REC) and F1 score reaching 99.86%, 99.87%, 99.86%, 99.86%, and 99.86%, compared with GNDA-CNN method (ACC = 80.90%, PRE = 87.34%, REC = 81.62%, F1 score = 79.23%). This study shows that when raw spectral data is small, DA methods can be combined with neural network models to identify ink brands effectively.

Impact of Drying on Phytonutritional Compounds, In Vitro Antioxidant Activity and Cytotoxicity of Spiny Saltbush (Rhagodia spinescens)

The Spiny saltbush (Rhagodia spinscens) is a halophyte species with the potential to provide natural ingredients used in food and pharmaceutical industries. In food and pharmaceutical applications, drying is necessary to maintain shelf-life, which reduces phytonutrient content. In this study, changes in the nutritional composition, phenolic and carotenoid profiles of radical antioxidant scavenging activity [(2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)(ABTS)], antioxidant power [ferric reducing antioxidant ability assay (FRAP)], and cytotoxicity of freeze- and oven-dried (55 °C for 24 h) spiny saltbush were determined. Sodium (4.72 g/100 g dry weight (DW), potassium (6.86 g/100 g DW), calcium (4.06 g/100 g DW), zinc (372 mg/kg DW) and protein content were higher in oven-dried samples than freeze-dried samples. Ultra-performance liquid chromatography–mass spectrometry analysis detected 18 metabolites in saltbush extracts. Partial Least-Squares Discriminant Analysis, Hierarchical Cluster Analysis, and Variable Importance in Projection discriminated between freeze-dried and oven-dried samples. Freeze-dried samples retained more individual metabolites than oven-dried samples, while oven-dried samples had higher antioxidant activity (ABTS and FRAP), lutein, trans-β carotene, and cis-β-carotene. Correlation analysis identified potential antioxidant candidates between phenolic and carotenoid compounds. Neither freeze-dried nor oven-dried spiny saltbush samples showed cytotoxicity. The study uncovered changes in phytonutritional compounds after the oven and freeze-drying spiny saltbush, a potential salt alternative and functional ingredient for the food industry.

Nondestructive Identification of Internal Potato Defects Using Visible and Short-Wavelength Near-Infrared Spectral Analysis

Potatoes are a staple food crop consumed worldwide, with their significance extending from household kitchens to large-scale food processing industries. Their market value and quality are often compromised by various internal defects such as pythium, bruising, internal browning, hollow heart, gangrene, blackheart, internal sprouting, and dry rot. This study aimed to classify internal-based defects and investigate the quantification of internal defective areas in potatoes using visible and short-wavelength near-infrared spectroscopy. The acquisition of the spectral data of potato tubers was performed using a spectrometer with a wavelength range of 400–1100 nm. The classification of internal-based defects was performed using partial least squares discriminant analysis (PLS-DA), while the quantification of the internal defective area was based on partial least squares regression (PLSR). The PLS-DA double cross-validation accuracy for the distinction between non-defective and all internally defective potatoes was 90.78%. The double cross-validation classification accuracy achieved for pythium, bruising, and non-defective categories was 91.03%. The internal defective area model based on PLSR achieved a correlation coefficient of double cross-validation of 0.91 and a root mean square error of double cross-validation of 0.85 cm2. This study makes a valuable contribution to advancing nondestructive techniques for evaluating internal defects in potatoes.

Machine Learning Approaches for the Fusion of Near-Infrared, Mid-Infrared, and Raman Data to Identify Cartilage Degradation in Human Osteochondral Plugs.

Vibrational spectroscopy methods such as mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopies have been shown to have great potential for in vivo biomedical applications, such as arthroscopic evaluation of joint injuries and degeneration. Considering that these techniques provide complementary chemical information, in this study, we hypothesized that combining the MIR, NIR, and Raman data from human osteochondral samples can improve the detection of cartilage degradation. This study evaluated 272 osteochondral samples from 18 human knee joins, comprising both healthy and damaged tissue according to the reference Osteoarthritis Research Society International grading system. We established the one-block and multi-block classification models using partial least squares discriminant analysis (PLSDA), random forest, and support vector machine (SVM) algorithms. Feature modeling by principal component analysis was tested for the SVM (PCA-SVM) models. The best one-block models were built using MIR and Raman data, discriminating healthy cartilage from damaged with an accuracy of 77.5% for MIR and 77.8% for Raman using the PCA-SVM algorithm, whereas the NIR data did not perform as well achieving only 68.5% accuracy for the best model using PCA-SVM. The multi-block approach allowed an improvement with an accuracy of 81.4% for the best model by PCA-SVM. Fusing three blocks using MIR, NIR, and Raman by multi-block PLSDA significantly improved the performance of the single-block models to 79.1% correct classification. The significance was proven by statistical testing using analysis of variance. Thus, the study suggests the potential and the complementary value of the fusion of different spectroscopic techniques and provides valuable data analysis tools for the diagnostics of cartilage health.

Baseline Raman Spectral Fingerprints of Zebrafish Embryos and Larvae

As a highly sensitive vibrational technique, Raman spectroscopy (RS) can provide valuable chemical and molecular data useful to characterise animal cell types, tissues and organs. As a label-free, rapid detection method, RS has been considered a valuable asset in forensics, biology and medicine. The technique has been applied to zebrafish for various purposes, including physiological, biochemical or bioaccumulation analyses. The available data point out its potential for the early diagnosis of detrimental effects elicited by toxicant exposure. Nevertheless, no baseline spectra are available for zebrafish embryos and larvae that could allow for suitable planning of toxicological assessments, comparison with toxicant-elicited spectra or mechanistic understanding of biochemical and physiological responses to the exposures. With this in mind, this work carried out a baseline characterisation of Raman spectra of zebrafish embryos and larvae throughout early development. Raman spectra were recorded from the iris, forebrain, melanocytes, heart, muscle and swim bladder between 24 and 168 h post-fertilisation. A chemometrics approach, based on partial least-squares discriminant analysis (PLS-DA), was used to obtain a Raman characterisation of each tissue or organ. In total, 117 Raman bands were identified, of which 24 were well represented and, thus, retained in the data analysed. Only three bands were found to be common to all organs and tissues. The PLS-DA provided a tentative Raman spectral fingerprint typical of each tissue or organ, reflecting the ongoing developmental dynamics. The bands showed frequencies previously assigned to collagen, cholesterol, various essential amino acids, carbohydrates and nucleic acids.

Untargeted Lipidomic Profiling of Amniotic Fluid Reveals Dysregulated Lipid Metabolism in Healthy Normal-Weight Mothers with Fetal Macrosomia

Background: Alterations in maternal lipid metabolism have been elucidated by several studies in relation to macrosomia. However, the lipidome of the intrauterine compartment associated with macrosomia, particularly in early pregnancy, remains largely unknown. Objectives: (1) To compare the lipidomic profile of early 2nd trimester amniotic fluid (AF) of healthy mothers with normal body mass index who gave birth to large-for-gestational age (LGA) versus appropriate-for-gestational age (AGA) infants; and (2) to examine if insulin and glucose concentrations in AF were associated with the AF lipidomic profile. Methods: In this nested case–control study, bio-banked AF samples were collected from pregnant women undergoing routine amniocentesis at 12–22 weeks of gestation. A subsample of 15 LGA infants (cases) were contrasted with 15 AGA infants (controls). An untargeted lipidomics analysis using liquid chromatography quadrupole time-of-flight mass spectrometry was conducted. Univariate and multivariate statistical analyses (principal component analysis and partial least-squares discriminant analysis) were used to extract differentially abundant (DA) features with high variable importance in projection (VIP) scores. Results: LGA AF was characterized by elevations of 30 phosphatidic acid species. Among other DA features, sphingomyelin (SM 14:0;O2/20:1) had the highest VIP score and was markedly elevated in LGA AF. Neither insulin nor glucose was associated with 2nd trimester AF lipidomic profiles in these healthy, normal-weight mothers. Conclusion: These findings provide evidence of early dysregulated lipid metabolism in healthy, normal-weight mothers with LGA infants.

Metabolomic Analysis Reveals the Linkage between Sleep-Enhancing Effects and Metabolite Biomarkers and Pathways of Different Casein Hydrolysates in Chronic Unpredictable Mild Stressed Mice.

Casein hydrolysates have been proven to exert varying sleep-enhancing and anxiolytic effects due to their distinct release of potential peptides. However, their underlying sleep-enhancing mechanisms at the metabolic level remain unclear. This study aims to investigate the potential sleep-enhancing mechanism of casein hydrolysates through an integrated approach of untargeted and targeted metabolomics in CUMS-induced anxiety mice for the first time. The results showed seven potential biomarkers were identified and screened using orthogonal partial least-squares discriminant analysis, random forest model, and pathway analysis, including ornithine, l-proline, l-prolinamide, inhibitory neurotransmitters gamma-aminobutyric acid, 5-HIAA, fumaric acid, and 4-oxoglutaramate. Moreover, casein hydrolysates exerted sleep-enhancing effects through multiple metabolic pathways, mainly including the GABAergic system, tryptophan metabolism, and cAMP response signaling pathway, which was validated by targeted metabolomics and vital protein expressions. It was interesting that casein hydrolysates with diverse representative peptide compositions exhibited varying activity, which could be attributed to distinct alterations in metabolites via different pathways.

A Comparison of the Impacts of Different Drying Methods on the Volatile Organic Compounds in Ginseng

Ginseng (Panax ginseng C. A. Meyer) is a valuable plant resource which has been used for centuries as both food and traditional Chinese medicine. It is popular in health research and markets globally. Fresh ginseng has a high moisture content and is prone to mold and rot, reducing its nutritional value without proper preservation. Drying treatments are effective for maintaining the beneficial properties of ginseng post-harvest. In this study, we investigated the effects of natural air drying (ND), hot-air drying (HAD), vacuum drying (VD), microwave vacuum drying (MVD), and vacuum freeze drying (VFD) on volatile organic compounds (VOCs) in ginseng. The results showed that the MVD time was the shortest, followed by the VFD, VD, and HAD times, whereas the ND time was the longest, but the VFD is the most beneficial to the appearance and color retention of ginseng. A total of 72 VOCs were obtained and 68 VOCs were identified using the five drying methods based on gas chromatography–ion mobility spectrometry (GC-IMS) technology, including 23 aldehydes, 19 alkenes, 10 alcohols, 10 ketones, 4 esters, 1 furan, and 1 pyrazine, and the ND method was the best for retaining VOCs. GC-IMS fingerprints, principal component analysis (PCA), Euclidean distance analysis, partial least squares discriminant analysis (PLS-DA), and cluster analysis (CA) can distinguish ginseng from different drying methods. A total of 29 VOCs can be used as the main characteristic markers of different drying methods in ginseng. Overall, our findings provide scientific theoretical guidance for optimizing ginseng’s drying methods, aromatic health effects, and flavor quality research.

Metabolomic profiling of human feces and plasma from extrauterine growth restriction infants.

Extrauterine growth restriction (EUGR) affects a substantial proportion of preterm infants and may influence both short-term complications and long-term sequelae. While many preterm infants with EUGR are secondary to small for gestational age (SGA) or very low birth weight (VLBW), a subset of EUGR infants do not exhibit these conditions. The purpose of this study is to investigate the metabolic profiles and biomarkers of EUGR infants in the absence of SGA and VLBW. A total of 100 feces (n = 50) and plasma samples (n = 50) were collected from participants categorized as either EUGR (EUGR group) or non-EUGR (NonEUGR group) in the absence of SGA and VLBW. Metabolites were characterized via UPLC-MS/MS using the Discovery HD4® platform. Data normalization, partial least squares discriminant analysis (PLSDA), and KEGG enrichment analysis of metabolite profiles were performed using the MetaboAnalyst 6.0. The clinical characteristics of preterm infants differed significantly between the EUGR and NonEUGR groups at discharge, including length of stay, weight Z-score, weight, height Z-score, height, head circumference, and fat-free mass. The PLSDA model exhibited clustering within groups and separation between groups. A total of 58 and 71 differential metabolites were identified in feces and plasma samples, respectively. They were involved in pathways such as caffeine, galactose, glutathione, cysteine, and methionine metabolisms. In the feces sample, 1-palmitoyl-galactosylglycerol exhibited a significant negative correlation with the growth characteristics of preterm infants, while 1-palmitoyl-2-palmitoleoyl-GPC displayed the opposite pattern. In plasma samples, androsterone glucuronide displayed a significant positive correlation with the growth characteristics of preterm infants, whereas 2-methoxyhydroquinone sulfate generated an opposite pattern. Moreover, 2-oleoylglycerol and sphinganine-1-phosphate exhibited the highest area under the curve in feces and plasma samples, respectively, according to diagnostic ROC curves. Preterm infants with EUGR, in the absence of SGA and VLBW, exhibit specific clinical characteristics and metabolomic profiles. Sphinganine-1-phosphate and 2-oleoylglycerol may hold promise as diagnostic markers for EUGR in the absence of SGA and VLBW. The objective of this study is to identify the differential metabolites in preterm infants with extrauterine growth restriction (EUGR) in the absence of small for gestational age (SGA) or very low birth weight (VLBW). Preterm infants with EUGR without SGA and VLBW exhibit specific clinical characteristics and metabolomic profiles. Sphinganine-1-phosphate and 2-oleoylglycerol emerged as potential diagnostic biomarkers for EUGR. This study enhances our understanding of the metabolomic profile in preterm infants with EUGR without SGA or VLBW. Our findings will offer valuable evidence for improving nutritional management and shedding light on the associated pathophysiological mechanisms of EUGR.

Predicting fungal infection sensitivity of sepals in harvested tomatoes using imaging spectroscopy and partial least squares discriminant analysis

Tomatoes ( Solanum lycopersicum L.) are a widely grown and globally traded vegetable, essential for both local consumption and international trade. However, approximately 30% of harvested tomato yields are lost due to fungal decay during postharvest handling. Timely disease identification is crucial to prevent such losses, but certain tomato varieties exhibit higher susceptibility to fungal infections than others. Additionally, there are variations in susceptibility among individual sepals, with unknown underlying causes. Traditional methods for assessing fungal presence in plants have limitations, such as sample destruction and a focus on symptom detection rather than evaluating susceptibility to fungal infection. Hence, there is a demand need for an accurate, non-destructive method capable of predicting susceptibility to fungal infection. The use of hyperspectral imaging (HSI) with chemometrics presents a pioneering approach to address this need. In this study, three tomato cultivars (‘Brioso,’ ‘Cappricia,’ and ‘Provine’) were studied. Hyperspectral images were captured on day-1 of harvest, followed by controlled fungal growth conditions. Ground truth assessments were conducted by three experts on day-3 and day-4, averaging severity scores assigned per sepal. The methodology involved extracting spectra from HSI images and calibrating and validating models using partial least squares discriminant analysis (PLSDA), aiming to optimize model parameters for accurate predictions. The models were categorized into those developed using data from a single variety (intravariety) and those utilizing data from multiple varieties combined (global models). The best-performing intravariety model was established using the Cappricia variety, achieving a balanced accuracy of 0.84. Conversely, a global model combining Cappricia and Provine varieties achieved a balanced accuracy of 0.70. Overall, the results suggest that distinguishing between more and less susceptible sepals is feasible under controlled conditions.

Discriminate shiitake mushroom (Lentinus edodes) from different origins by lipidomics based on UHPLC-QE HR-AM/MS/MS

Shiitake mushroom (Lentinus edodes), as a prestigious edible mushroom and one of the most famous medicinal mushrooms, is cultivated in several regions of China. In this study, the total lipids of shiitake mushroom from different origins were characterized by ultra-high-performance liquid chromatography-quadrupole-Exactive-Orbitrap MS/MS (UHPLC-QE Orbitrap/MS/MS). Twenty lipids and 174 lipid molecular species were determined by non-targeted lipodomics analysis. The multivariate analysis showed that 19 lipids were screened as potential markers for distinguishing shiitake mushroom of different origins, for instance, phosphatidic acid (18:2/18:2) (PA 18:2/18:2) and phosphatidylglycerol (16:0/18:2) (PG 16:0/18:2). And the Partial least squares discriminant analysis (PLS-DA) model reconstructed was used to distinguish shiitake mushroom from different regions with a success rate of 100%. This work comprehensively provided the lipid information of shiitake mushroom and demonstrated the great potential of authenticity traceability of shiitake mushroom from different origins from the perspective of lipidomics.

An expert system for species discrimination and grade identification of fish maw

Fish maw, derived from dried swim bladders of fish, is valued for its nutritional and medicinal properties, which has led to an increased market demand. However, price variability based on species and grades has results in unethical practices such as counterfeiting and mislabeling, highlighting the need for reliable quality authentication. To address this, an expert system using MATLAB software has been developed. This system employs nuclear magnetic resonance (NMR) technology and pattern recognition methods to identify fish maw species and classify their grade. The study analyzed ten species across three grades of fish maw, identifying 43 nutritional components from NMR spectra, including sugars, amino acids, fatty acids, organic acids, and vitamins. Univariate statistical analysis was integrated with multivariate statistical analyses, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares (OPLS-DA) using SIMCA software, to develop models for species and grades identification. A four-dimensional volcano map was constructed to highlight characteristic components of various fish maws, resulting in an NMR database for common fish maws. The expert system’s accuracy was validated with new samples, achieving 92.6% for species identification and 90.0% for grade classification. This study provides a valuable tool for the quality evaluation of fish maw and a scientific basis for market regulation.

Untargeted metabolomics profiling for revealing water-soluble bioactive components and biological activities in edible bird’s nest

Edible bird’s nest (EBN) is a functional food renowned for its numerous health benefits. While its nutritional and therapeutic value is well-documented, the metabolites contributing to the bioactivities of EBN remain poorly understood. This study aimed to identify the metabolites present in EBN subjected to different treatments, including double-boiled EBN (EBNdb), EBN hydrolysate (EBNhydro), EBN fermented with Lactobacillus helveticus (EBNLH), Latilactobacillus curvatus (EBNLC), and Latilactobacillus sakei (EBNLS) using liquid chromatography-mass spectrometry (LC-MS) and correlate the identified bioactive metabolites with the bioactivities of EBN. It was found that the fermented EBNs exhibited the highest number of metabolites, with 76 tentatively identified, followed by EBNhydro (45) and EBNdb (37). Citric acid (1.97–4.48 g/kg) was present in all treated EBN samples, while L(+)-lactic acid (3.03–8.07 g/kg) and adipic acid (2.33–3.18 g/kg) were only found in fermented EBNs. Among the treated EBN samples, EBNLC demonstrated the significantly highest (p ≤ 0.05) antioxidative (22.34 ± 0.41 % 1,1-diphenyl-2-picrylhydrazil radical scavenging activity), antihypertensive (5.46 ± 0.26 % angiotensin-converting enzyme inhibitory activity), and antihyperglycemic activities (6.48 ± 0.34 % α-amylase inhibitory activity). A total of 18 metabolites, including citric acid, 4-sphingenin, N-acetylcitrulline, 4-aminophenol, L(+)-lactic acid, 2-oxoadipate, sildenafil, formylglycinamidin-RP, 11β,17α,21-α-5β-pregnane-3,20-dione, 2-ketobutyric acid, homoserine, benzaldehyde, 1-pyrroline4-hydroxy-2-carboxylate, nortriptyline, 1-methylguanine, 3-hydroxy-trimethyllysine, 3-phenylpropionate, and reserphine were predicted as bioactive metabolites using the partial least squares discriminant analysis (PLS-DA). This study provides valuable insights into the metabolites present in EBN and serves as fundamental data for future investigations into the bioactive compounds responsible for its specific health benefits, potentially leading to the development of enhanced EBN-based functional foods.

Identification of lu’an Guapian at different picking periods by using excitation–emission matrix fluorescence spectroscopy coupled with chemometrics

Lu’an Guapian (LAGP) is a renowned green tea, with its price significantly higher when picked before the Qingming Festival compared to after, posing risks of confusion and counterfeiting. This study proposed using an excitation-emission matrix (EEM) fluorescence method combined with chemometrics for rapid identification of tea picked before and after Qingming Festival. Firstly, the differences among the EEM fingerprints of different tea samples were analyzed using the alternating trilinear decomposition (ATLD) algorithm. To determine the differences between LAGP before and after Qingming Festival, the total contents of ten main components in tea were detected, and their effects on the EEM fluorescence fingerprint of tea were analyzed using correlation heatmaps. Finally, two chemometric algorithms, partial least squares discriminant analysis (PLS-DA) and k-nearest neighbor (k-NN), were used to classify LAGP before and after Qingming Festival, achieving a classification accuracy of 100% for the training set, test set, and prediction set. To further explore the potential of this method, LAGP was further classified in detail according to four detailed picking periods, achieving an accuracy of over 83%. The same chemometric algorithm was used to classify the data based on the high-performance liquid chromatography (HPLC) method, yielding results comparable to those of the EEM-based method, though slightly inferior. Variable importance projection (VIP) analysis shows that catechin analogs are the main contributors to the classification of LAGP. The results demonstrated the EEM method’s significant potential in identifying the picking time of green tea.

Combination of plasma-based lipidomics and machine learning provides a useful diagnostic tool for ovarian cancer

Ovarian cancer (OC), the second leading cause of death among gynecologic cancers, is often diagnosed at an advanced stage due to its asymptomatic nature at early stages. This study aimed to explore the diagnostic potential of plasma-based lipidomics combined with machine learning (ML) in OC. Non-targeted lipidomics analysis was conducted on plasma samples from participants with epithelial ovarian cancer (EOC), benign ovarian tumor (BOT), and healthy control (HC). The samples were randomly divided into a train set and a test set. Differential lipids between groups were selected using two-tailed Student’s t-test and partial least squares discriminant analysis (PLS-DA). Both single lipid-based receiver operating characteristic (ROC) model, and multiple lipid-based ML model, were constructed to investigate the diagnostic value of the differential lipids. The results showed several lipids with significant diagnostic potential. ST 27:2;O achieved the highest prediction accuracy of 0.92 in distinguishing EOC from HC. DG 42:2 had the highest prediction accuracy of 0.96 in diagnosing BOT from HC. Cer d18:1/18:0 had the highest prediction accuracy of 0.65 in differentiating EOC from BOT. Furthermore, multiple lipid-based ML models illustrated better diagnostic performance. K-nearest neighbors (k-NN), partial least squares (PLS), and random forest (RF) models achieved the highest prediction accuracy of 0.96 in discriminating EOC from HC. The support vector machine (SVM) model reached the highest prediction accuracy both in distinguishing BOT from HC, and in differentiating EOC from BOT, with accuracies of 1.00 and 0.74, respectively. In conclusion, this study revealed that the combination of plasma-based lipidomics and ML algorithms is an effective method for diagnosing OC.

Rapid qualitative and quantitative detection for adulteration of Atractylodis Rhizoma using hyperspectral imaging combined with chemometric methods

In the field of traditional Chinese medicine, Atractylodis Rhizoma (AR) is commonly used for various diseases due to its excellent ability to dry dampness and strengthen the spleen, especially popular in East Asia. The aim of this study is to proposed Hyperspectral Imaging (HSI) in combination with chemometric methods for the rapid qualitative and quantitative detection of AR adulteration with other types of powder. Partial Least Squares Discriminant Analysis (PLS-DA) was used to construct the classification models the best, with the First-order Derivative (F-D) preprocessing method. The accuracy values of the test sets for classification models were above 99%. Furthermore, Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and BP Neural Network (BPNN) were used to quantitatively analyze the adulteration level. On the whole, the BPNN model has a relatively stable effect. The R-square (R2) values of different models were all greater than 0.97, the Root Mean Square Error (RMSE) values were all less than 0.0300, and the Relative Percentage Difference (RPD) values were over 6.00. After applying three characteristic wavelength selection algorithms, namely Iterative Retained Information Variable (IRIV), Successive Projections Algorithm (SPA), and Variable Iterative Space Shrinkage Approach (VISSA) algorithms, the classification accuracy values remained over 99.00% while the quantification models’ RPD values were over 4.00. These results demonstrate the reliability of using hyperspectral imaging combined with chemometrics methods for the adulteration problems in AR.

Analysis of volatile compounds in Xiangjiao baijiu from different storage containers and years based on HS-GC-IMS and DI-GC–MS

The volatile compounds in 16 different storage containers and years of Xiangjiao Baijiu (XJBJ) were compared and analyzed via direct injection (DI) combined with gas chromatography–mass spectrometry (GC–MS) and headspace extraction (HS) coupled with gas chromatography-ion mobility spectrometry (HS-GC-IMS) for the first time. Through HS-GC-IMS analysis, it was found that the succession rules of 14 compounds such as furfural during aging process. A total of 60 compounds were identified using DI-GC–MS. Twenty-five of these compounds were further quantified, and 18 compounds had odor activity values (OAVs) > 1, which were important contributor to aroma of XJBJ. Among them, those with OAVs >1000 included ethyl hexanoate, ethyl octanoate, ethyl butanoate, and ethyl pentanoate. Combining the results of quantitative, OAVs and partial least squares-discriminant analysis (PLS-DA) revealed that 10 compounds such as ethyl octanoate were the important compounds that lead to the differences between different storage types of XJBJ.

Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections

The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinical manifestations. There is a need for inexpensive tools that require minimal expertise and are accessible for routine laboratories. Contrastingly, current conventional diagnostic methods are often found only in specialized environments. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy offers a nondestructive and user-friendly approach in the analysis of a wide range of samples. In this paper, we determined whether the technique coupled with machine learning can detect and differentiate lymphoma within lymphoid tissue samples. Tissue sections from 295 individuals diagnosed with lymphoma and 389 individuals without the disease were analyzed using ATR-FTIR spectroscopy. The resulting spectral dataset was split using a 70:30 train-test split. Partial least Squares Discriminant Analysis (PLS-DA) models were trained to distinguish non-malignant lymphoid tissue from lymphoma samples and to differentiate between subtypes. On the training set (n = 478), significant spectral differences were mainly identified in the 1800–900 cm–1 region, attributed to fundamental biochemical constituents like proteins, lipids, carbohydrates, and nucleic acids. On the independent test set (n = 206), the trained PLS-DA model achieved a promising AUC of 0.882 (95% CI: 0.881–0.884) in the differentiation between lymphoma and non-malignant lymphoid tissue. In addition, comparative analyses revealed spectral distinctions and notable clustering between the different lymphoma subtypes. This study provides valuable insights into the application of ATR-FTIR spectroscopy and machine learning in the field of lymphoma diagnosis as a non-destructive, rapid and inexpensive tool with the potential to be easily implemented in non-specialized laboratories.

Evaluating MIR and NIR Spectroscopy Coupled with Multivariate Analysis for Detection and Quantification of Additives in Tobacco Products.

The detection and quantification of additives in tobacco products are critical for ensuring consumer safety and compliance with regulatory standards. Traditional analytical techniques, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and others, although effective, suffer from drawbacks, including complex sample preparation, high costs, lengthy analysis times, and the requirement for skilled operators. This study addresses these challenges by evaluating the efficacy of mid-infrared (MIR) spectroscopy and near-IR (NIR) spectroscopy, coupled with multivariate analysis, as potential solutions for the detection and quantification of additives in tobacco products. So, a representative set of tobacco products was selected and spiked with the targeted additives, namely caffeine, menthol, glycerol, and cocoa. Multivariate analysis of MIR and NIR spectra consisted of principal component analysis (PCA), hierarchical clustering analysis (HCA), partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) to classify samples based on targeted additives. Based on the unsupervised techniques (PCA and HCA), a distinction could be made between spiked and non-spiked samples for all four targeted additives based on both MIR and NIR spectral data. During supervised analysis, SIMCA achieved 87-100% classification accuracy for the different additives and for both spectroscopic techniques. PLS-DA models showed classification rates of 80% to 100%, also demonstrating robust performance. Regression studies, using PLS, showed that it is possible to effectively estimate the concentration levels of the targeted molecules. The results also highlight the necessity of optimizing data pretreatment for accurate quantification of the target additives. Overall, NIR spectroscopy combined with SIMCA provided the most accurate and robust classification models for all target molecules, indicating that it is the most effective single technique for this type of analysis. MIR, on the other hand, showed the overall best performance for quantitative estimation.

Elucidation of the Effect of Solar Light on the Near-Infrared Excitation Raman Spectroscopy-Based Analysis of Fabric Dyes.

Colored textiles are valuable physical evidence often found at crime scenes. Analysis of the chemical structure of textiles could be used to establish a connection between fabric found at a crime scene and suspect cloths. High-performance liquid chromatography (HPLC) and mass spectroscopy coupled HPLC are traditionally used for the identification of dyes in fabric. However, these techniques are invasive and destructive. A growing body of evidence indicates that near-infrared excitation (λ = 830 nm) Raman spectroscopy (NIeRS) could be used to probe the chemical signature of such colorants. At the same time, it remains unclear whether environmental factors, such as solar light could lower the accuracy of NIeRS-based identification of dyes in textiles. In this study, we exposed cotton fabric colored with six different dyes to light and investigated the extent to which colorants fade during seven weeks using NIeRS. We found a decrease in the intensities of all vibrational bands in the acquired spectra as the time of the exposition of fabric to light increased. Nevertheless, utilization of partial least-squared discriminant analysis (PLS-DA) enabled identification of the colorants at all eight weeks. These results indicate that the effect of light exposure should be strongly considered by forensic experts upon the NIeRS-based analysis of colored fabric.