Partial Least Squares Discriminant Analysis Model Research Articles

Changes in the small-molecule fingerprints of rice planted near an industrial explosion site in Taiwan.

A fire and explosion accident at a petrochemical complex sparked concerns over the rice health and production in nearby paddy fields. To unveil the potential effects, this study investigated small molecule changes in rice harvested in nearby counties using non-target analysis. Rice grains were harvested three, eight, 15, and 20months after the accident from a total of ten townships. Small-molecule (m/z 70-1100) data in brown rice (n = 27) were acquired using high-resolution mass spectrometry (HRMS). Partial least squares discriminant analysis (PLS-DA) models were constructed to illustrate the temporal and spatial trends of rice's small-molecule fingerprints, and markers of production locations were identified. The small-molecule fingerprint in the rice directly exposed to the accident and harvested three months after the explosion differed significantly from those planted after the accident (PLS-DA model Q2 = 0.943, Q2/R2Y = 0.962), probably indicating the exclusion of long-term effects. Besides, in the rice directly exposed to the accident, the rice collected from near the explosion site (< 15km) exhibited reduced jasmonic acid and increased imidacloprid levels (log2 fold change: -1.53 and 5.46, respectively), compared to that from farther locations. The result would suggest compromised disease defence in rice grown under the stress of explosion. In addition, lipid and amino acid metabolism perturbations are deemed relevant to plant development.

Efficient generation of HPLC and FTIR data for quality assessment using time series generation model: a case study on Tibetan medicine Shilajit.

The scarcity and preciousness of plateau characteristic medicinal plants pose a significant challenge in obtaining sufficient quantities of experimental samples for quality evaluation. Insufficient sample sizes often lead to ambiguous and questionable quality assessments and suboptimal performance in pattern recognition. Shilajit, a popular Tibetan medicine, is harvested from high altitudes above 2000m, making it difficult to obtain. Additionally, the complex geographical environment results in low uniformity of Shilajit quality. To address these challenges, this study employed a deep learning model, time vector quantization variational auto- encoder (TimeVQVAE), to generate data matrices based on chromatographic and spectral for different grades of Shilajit, thereby increasing in the amount of data. Partial least squares discriminant analysis (PLS-DA) was used to identify three grades of Shilajit samples based on original, generated, and combined data. Compared with the originally generated high performance liquid chromatography (HPLC) and Fourier transform infrared spectroscopy (FTIR) data, the data generated by TimeVQVAE effectively preserved the chemical profile. In the test set, the average matrices for HPLC, FTIR, and combined data increased by 32.2%, 15.9%, and 23.0%, respectively. On the real test data, the PLS-DA model's classification accuracy initially reached a maximum of 0.7905. However, after incorporating TimeVQVAE-generated data, the accuracy significantly improved, reaching 0.9442 in the test set. Additionally, the PLS-DA model trained with the fused data showed enhanced stability. This study offers a novel and effective approach for researching medicinal materials with small sample sizes, and addresses the limitations of improving model performance through data augmentation strategies.

Beetroot juice intake positively influenced gut microbiota and inflammation but failed to improve functional outcomes in adults with long COVID: A pilot randomized controlled trial

Long-term effects of coronavirus disease 2019 (long COVID) develop in a substantial number of people following an acute COVID-19 episode. Red beetroot juice may have positive effects on multiple pathways involved in long COVID. The aim of this pilot study was to explore the impact of beetroot juice supplementation on physical function, gut microbiota, and systemic inflammation in adults with long COVID. A single-center, double-blind, placebo-controlled randomized trial was conducted to test the effects of 14 days of beetroot juice supplementation, rich in nitrates and betalains, on functional and biological outcomes in adults aged between 20 and 60 years with long COVID. Participants were randomized 1:1 to receive either daily oral supplementation with 200mL beetroot juice (∼600mg nitrate) or placebo (∼60mg nitrate) for 14 days. The primary endpoint was the change from baseline to day 14 in a fatigue resistance test. Secondary outcomes included the distance walked on the 6-min walk test, handgrip strength, and flow-mediated dilation. Secondary endpoints also included changes from baseline in circulating inflammatory mediators and metagenomic and fecal water metabolomic profiles. Partial least squares discriminant analysis (PLS-DA) models were built to evaluate the differences in biological variables associated with the interventions. Thirty-one participants were randomized in the study. Twenty-five of them (median (interquartile range) age 40 (10), 14 [56%] women), received either beetroot juice (15) or placebo (10) and completed the study. At 14 days, fatigue resistance significantly improved from baseline (mean difference [standard error]:+21.8 [3.7] s; p<0.001) with no significant differences between intervention groups. A significant increase from baseline in the distance walked on the 6-min walk test was observed (mean difference [standard error]:+30.0 [9.4] m; p=0.03), which was not different between groups. Flow-mediated dilation did not differ between participants who received beetroot juice and those on placebo. PLS-DA models allowed correct classification of participants with 92.2±4.4% accuracy. Those who ingested red beetroot juice had a greater abundance of bacteria with well-known beneficial effects, including Akkermansia, Oscillospira, Prevotella, Roseburia, Ruminococcaceae, and Turicibacter, compared with placebo. Participants allocated to beetroot juice supplementation were also characterized by significantly higher levels of fecal nicotinate, trimethylamine, and markers of beetroot juice intake (e.g., 5,6-dihydroxyindole). Finally, higher levels of interferon gamma and macrophage inflammatory protein-1β were found in participants who consumed beetroot juice. Beetroot juice supplementation for two weeks did not to induce significant improvements in functional outcomes in adults with long COVID compared with placebo. Beneficial effects were observed in both gut microbiota composition (i.e., increase in probiotic species) and inflammatory mediators. Trial was registered under ClinicalTrials.gov. Identifier no. NCT06535165.

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.

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.

Tuning multispectral fluorescence quantum dot–based identification of short-length amyloid β peptides by applying Cu(II) ions

Currently available methods for detecting amyloid β (Aβ) derivatives are mainly dedicated to determining the long forms Aβ1-42 and Aβ1-40. At the same time, the number of physiologically occurring Aβ analogs is much higher, including those truncated at the N- and C-termini. Their identification using standard methods is challenging due to the structural similarity of various Aβ analogs, but could highly benefit from both biomarkers discovery and pathophysiological studies of Alzheimer’s disease. Therefore a “chemical tongue” sensing strategy was employed for the detection of seven Aβ peptide derivatives: Aβ1-16, Aβ4-16, Aβ4-9, Aβ5-16, Aβ5-12, Aβ5-9, Aβ12-16. The proposed sensing system is based on competitive interactions between quantum dots, Cu(II) ions, and Aβ peptides, providing unique fluorescence fingerprints useful for the identification of analytes. After carefully evaluating the Aβ sample preparation protocol, perfect determination of all studied Aβ peptides was achieved using partial least square–discriminant analysis (PLS-DA). The developed PLS-DA models are characterized by excellent accuracy, sensitivity, precision, and specificity of analyte determination, emphasizing the potential of the proposed sensing strategy.Graphical abstract

Early Metabolomic Profiling as a Predictor of Renal Function Six Months After Kidney Transplantation.

Kidney transplantation is the therapy of choice for patients with advanced chronic kidney disease; however, predicting graft outcomes remains a significant challenge. Early identification of reliable biomarkers could enhance post-transplant management and improve long-term outcomes. This study aimed to identify metabolomic biomarkers within the first week after kidney transplantation that predict renal function at six months. We conducted a prospective study involving 50 adult patients who received deceased donor kidney transplants. Plasma samples collected one week after transplant were analyzed using liquid chromatography-mass spectrometry in a semi-targeted metabolomic approach. A Partial Least Squares-Discriminant Analysis (PLS-DA) model identified metabolites associated with serum creatinine > 1.5 mg/dL at six months. Metabolites were selected based on a Variable Importance in Projection (VIP) score > 1.5, which was used to optimize model performance. The PLS-DA model demonstrated strong predictive performance with an area under the curve (AUC) of 0.958. The metabolites negatively associated with serum creatinine > 1.5 mg/dL were 3-methylindole, guaiacol, histidine, 3-indolepropionic acid, and α-lipoic acid. Conversely, the metabolites positively associated with worse kidney graft outcomes included homocarnosine, 5-methylcytosine, xanthosine, choline, phenylalanine, kynurenic acid, and L-kynurenine. Early metabolomic profiling after transplantation shows promise in predicting renal function. Identifying metabolites with antioxidant and anti-inflammatory properties, as well as those that are harmful and could be targeted therapeutically, underscores their potential clinical significance. The link between several metabolites and the tryptophan pathway suggests that further specific evaluation of this pathway is warranted. These biomarkers can enhance patient management and graft survival.

Unrevealing the Halyomorpha halys Damage Fingerprint on Hazelnut Metabolome by Multiomic Platforms and AI-Aided Strategies.

The brown marmorated stink bug (Halyomorpha halys) poses a significant threat to hazelnut crops by affecting kernel development and causing quality defects, reducing the market value. While previous studies have identified bitter-tasting compounds in affected kernels, the impact of stink bug feeding on the hazelnut metabolome, particularly concerning aroma precursors, remains underexplored. This study aims to map the nonvolatile metabolome and volatilome of hazelnut samples obtained by caging H. halys on different cultivars in two locations to identify markers for diagnosing stink bug damage. Using a multiomic approach involving headspace solid-phase microextraction (HS-SPME), comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOF MS), and liquid chromatography-high-resolution mass spectrometry (LC-HRMS), both raw and roasted hazelnuts are analyzed, with artificial intelligence (AI) and machine learning tools employed to explore data correlations. The study finds that the hazelnut metabolome and volatilome exhibit high chemical complexity with significant classes of compounds such as aldehydes, ketones, alcohols, and terpenes identified in both raw and roasted hazelnuts. Multivariate analysis indicates that the orchard location significantly impacts the metabolome, followed by damage type, with cultivar differences being less pronounced. Partial least-squares discriminant analysis (PLS-DA) models achieve high predictive accuracy for orchard location (99%) and damage type (≈80%), with the roasted volatilome showing the highest predictive accuracy. Correlation matrices reveal significant relationships between raw hazelnut metabolites and aroma compounds in roasted samples, suggesting potential markers for stink bug damage that could guide the quality assessment and mitigation strategies. Data fusion techniques further enhance classification performance, particularly in predicting damage type, underscoring the potential of integrating multiple data sets for comprehensive quality assessment.

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.

Detection of serum alterations in polysubstance use patients by FT-Raman spectroscopy

Substance use disorders pose significant health risks and treatment challenges due to the diverse interactions between substances and their impact on physical and mental health. The chemical effects of multiple substance use on bodily fluids are not yet fully understood. Therefore, this study aimed to investigate the chemical changes induced by a combination of substances compared to a control group. Analysis of FT-Raman spectra revealed structural alterations in the amide III, I, and C = O functional groups of lipids in subjects treated with opioids, alcohol and cannabis (polysubstance group). These changes were evident in the form of peak shifts compared to the control group. Additionally, an imbalance in the amide-lipid ratio was observed, indicating perturbations in serum protein and lipid levels. Furthermore, a 2D plot of two-track two-dimensional correlation spectra (2T2D-COS) demonstrated a shift towards dominance of lipid vibrations in the polysubstance use groups, contrasting with the predominance of the amide fraction in the control group. This observation suggests distinct molecular changes induced by multiple substance use, potentially contributing to the pathophysiology of substance use disorders. Principal Component Analysis (PCA) was utilized to visualize the data structure and identify outliers. Subsequently, Partial Least Squares Discriminant Analysis (PLS-DA) was employed to classify the polysubstance use and control groups. The PLS-DA model demonstrated high classification accuracy, achieving 100.00 % in the training dataset and 94.74 % in the test dataset. Furthermore, receiver operating characteristic (ROC) analysis yielded perfect AUC values of 1.00 for both the training and test sets, underscoring the robustness of the classification model.This study highlights the quantitative and qualitative changes in serum protein and lipid levels induced by polysubstance use groups, as evidenced by FT-Raman spectroscopy. The findings underscore the importance of understanding the chemical effects of polysubstance use on bodily fluids for improved diagnosis and treatment of substance use disorders. Moreover, the successful classification of spectral data using machine learning techniques emphasizes the potential of these approaches in clinical applications for substance abuse monitoring and management.

Classification of Kudoa thyrsites infected and uninfected fish using a handheld near-infrared spectrophotometer, SIMCA and PLS-DA.

Kudoa thyrsites infection of marine fish typically results in myoliquefaction, which is only apparent 24 to 56 h post-mortem. The traditional methods for the detection of K. thyrsites infected fish are time-consuming and destructive, reducing its marketability. This poses a challenge for the fish industry to remove infected fish before it reaches the market or further processing activities. This study investigated the use of near-infrared (NIR) spectroscopy, in combination with soft independent modelling of class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA), for discriminating K. thyrsites infected fish from uninfected fish. Performance of the classification models was evaluated by calculating the sensitivity, specificity and precision. A total of 334 fish samples (200 sardine, 64 hake and 70 kingklip) were used for this study. Infection of K. thyrsites was determined with the use of qPCR assays. Ninety per cent (90%) of the sardine samples, 78% of the hake samples and 37% of the kingklip samples were infected. Class groups of infected and uninfected fish samples were created for the purpose of generating SIMCA and PLS-DA classification models for each species of fish, as well as for a species independent data set. Principal component analysis (PCA) of NIR spectra did not show any clustering for infected and uninfected samples. Calibration and test sample sets were generated for the purpose of building and testing the SIMCA and PLD-DA classification models. SIMCA and PLS-DA were unable to classify test samples correctly into the two classes. The number of misclassifications (NMC) was higher for the SIMCA models than for the PLS-DA models, with more than 60% incorrectly classified. SIMCA classified most of the test samples into both classes. The precision for PLS-DA were 89% for sardine, 81% for hake, 0% for kingklip and 87% for species independent models, however, most samples were classified at infected. The use of NIR spectroscopy and classification models such as SIMCA and PLS-DA showed limited use as a method to distinguish between K. thyrsites infected and uninfected fish samples. Textural and chemical changes during extended frozen storage of the fish samples may have masked the effects associated with K. thyrsites infection. Further studies are suggested where NIR spectroscopy is used in combination with texture analysis and image spectroscopy.

Near-infrared spectroscopy combined with support vector machine for the identification of Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) adulteration using wavelength selection algorithms

The frequent occurrence of adulterating Tartary buckwheat powder with crop flours in the market necessitates an urgent need for a simple analysis method to ensure the quality of Tartary buckwheat. This study employed near-infrared spectroscopy (NIRS) for the collection of spectral data from Tartary buckwheat samples adulterated with whole wheat, oat, soybean, barley, and sorghum flours. The competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were deployed to identify informative wavelengths. By integrating support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA), we constructed qualitative models to discern Tartary buckwheat adulteration. The PLS-DA model exhibited prediction accuracies between 89.78 % and 94.22 %, while the mean-centering (MC)-PLS-DA model showcased impressive predictive accuracy of 93.33 %. Notably, the feature-based Autoscales-CARS-CV-SVM model achieved more excellent identification accuracy. These findings exhibit the excellent potential of chemometrics as a powerful tool for detecting food product adulteration.

Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Analysis of Saliva as a Diagnostic Specimen for Rapid Classification of Oral Squamous Cell Carcinoma Using Chemometrics Methods

Background & Aim Recent advancements in analytical techniques have highlighted the potential of Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy as a quick, cost-effective, non-invasive, and efficient tool for cancer diagnosis. This study aims to evaluate the effectiveness of ATR-FTIR spectroscopy in combination with supervised machine learning classification models for diagnosing OSCC using saliva samples. Methods & Materials Eighty unstimulated whole saliva samples from OSCC patients and healthy controls were collected. The ATR-FTIR spectroscopy was performed and spectral data were used to classify healthy and OSCC groups. The data were analyzed using machine learning classification methods such as Partial Least Squares-Discriminant Analysis (PLS-DA) and Support Vector Machine Classification (SVM-C). The classification performance of the models was evaluated by computing sensitivity, specificity, precision, and accuracy. Results The samples were classified into two classes based on their spectral data. The obtained results demonstrate a high level of accuracy in the prediction sets of the PLS-DA and SVM-C models, with accuracy values of 0.960 and 0.962, respectively. The OSCC group sensitivity values for both PLS-DA and SVM-C models was 1.00, respectively. Conclusion The study indicates that ATR-FTIR spectroscopy, combined with chemometrics, is a potential method for the non-invasive diagnosis of OSCC using saliva samples. This method achieved high accuracy and the findings of this study suggest that ATR-FTIR spectroscopy could be further developed for clinical applications in OSCC diagnosis.

Assessing the Metabolic Variations of Invasive and Noninvasive Unilateral Retinoblastoma Patients.

Retinoblastoma (Rb) is a pediatric eye cancer which if diagnosed at later stages can lead to Rb invasion into the choroid, optic nerve, sclera, or beyond, with the potential of undergoing metastasis. Cancer cells, including Rb cells, reprogram their metabolic circuits for their own survival and progression, which provides a great opportunity to monitor the extent of Rb progression based on metabolic differences. Henceforth, the present study aims to map the metabolic variations in patients with invasive (primarily enucleated eyes with high-risk histopathological features) and noninvasive (eyes salvaged with treatment) unilateral retinoblastoma (Rb) using nuclear magnetic resonance (NMR) based serum metabolomics. Quantification of differential metabolites in the serum obtained from 9 patients with invasive and 4 with noninvasive unilateral Rb along with 6 controls (no retinal pathology) was carried out using 1H NMR spectroscopy. A total of 71 metabolites, such as organic acids, amino acids, carbohydrates, and others, were identified in the serum obtained from 9 patients with invasive and 4 with noninvasive unilateral Rb. Partial least-squares discriminant analysis (PLS-DA) models depicted distinct grouping of invasive and noninvasive Rb patients and controls. Differential metabolic fingerprints were observed for invasive and noninvasive Rb patients based on their biostatistical analyses with respect to controls. Remarkable perturbation was observed among various metabolites such as 4-aminobutyrate, 2-phosphoglycerate, O-phosphocholine, proline, Sn-glycero-3-phosphocholine (Sn-GPC), and O-phosphoethanolamine in noninvasive and invasive Rb patients with most of the effects being heightened in the latter group. Metabolic changes unique to invasive and noninvasive Rb patients were also observed. Multivariate receiver operating characteristics (ROC) analysis unveiled the highest accuracy and potency of ROC models 2 and 5 to distinguish the noninvasive and invasive Rb from controls, respectively. Metabolites identified in the serum of patients with invasive and noninvasive Rb may aid in advancing our knowledge about Rb tumor biology. Differential aberrant metabolic variations in patients with invasive Rb compared to those with noninvasive Rb may guide the decision of enucleation versus globe salvage.

Differentiating Pond-Intensive, Paddy-Ecologically, and Free-Range Cultured Crayfish (Procambarus clarkii) Using Stable Isotope and Multi-Element Analysis Coupled with Chemometrics.

The farming pattern of crayfish significantly impacts their quality, safety, and nutrition. Typically, green and ecologically friendly products command higher economic value and market competitiveness. Consequently, intensive farming methods are frequently employed in an attempt to replace these environmentally friendly products, leading to potential instances of commercial fraud. In this study, stable isotope and multi-element analysis were utilized in conjunction with multivariate modeling to differentiate between pond-intensive, paddy-ecologically, and free-range cultured crayfish. The four stable isotope ratios of carbon, nitrogen, hydrogen, and oxygen (δ13C, δ15N, δ2H, δ18O) and 20 elements from 88 crayfish samples and their feeds were determined for variance analysis and correlation analysis. To identify and differentiate three different farming pattern crayfish, unsupervised methods such as hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used, as well as supervised multivariate modeling, specifically partial least squares discriminant analysis (PLS-DA). The HCA and PCA exhibited limited effectiveness in classifying the farming pattern of crayfish, whereas the PLS-DA demonstrated a more robust performance with a predictive accuracy of 90.8%. Additionally, variables such as δ13C, δ15N, δ2H, Mn, and Co exhibited relatively higher contributions in the PLS-DA model, with a variable influence on projection (VIP) greater than 1. This study is the first attempt to use stable isotope and multi-element analysis to distinguish crayfish under three farming patterns. It holds promising potential as an effective strategy for crayfish authentication.

Detection of hypoglycaemia in type 1 diabetes through breath volatile organic compound profiling using gas chromatography-ion mobility spectrometry.

To evaluate the relationship between breath volatile organic compounds (VOCs) and glycaemic states in individuals with type 1 diabetes (T1D), focusing on identifying specific VOCs as biomarkers for hypoglycaemia to offer a non-invasive diabetes-monitoring method. Ten individuals with T1D underwent induced hypoglycaemia in a clinical setting. Breath samples, collected every 10-15 minutes, were analysed using gas chromatography-ion mobility spectrometry (GC-IMS). Correlation analysis and machine learning models, including Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine classifiers, were used to classify glycaemic states based on VOC profiles. Statistical analysis revealed moderate correlations between specific VOCs (e.g. isoprene, acetone) and venous blood glucose levels. Machine learning models showed high accuracy in classifying glycaemic states, with the best performance achieved by a two-class PLS-DA model showing an accuracy of 93%, sensitivity of 92% and specificity of 94%. Key biomarkers identified included isoprene, acetone, 2-butanone, methanol, ethanol, 2-propanol and 2-pentanone. This study shows the potential of breath VOCs to accurately classify glycaemic states in individuals with T1D. While key biomarkers such as isoprene, acetone and 2-butanone were identified, the analysis emphasizes the importance of using overall VOC patterns rather than individual compounds, which can be markers for multiple conditions. Machine learning models leveraging these patterns achieved high accuracy, sensitivity and specificity. These findings suggest that breath analysis using GC-IMS could be a viable non-invasive method for monitoring glycaemic states and managing diabetes.

Comparative chemometric modeling of fresh and dry cannabis inflorescences using FT-NIR spectroscopy: Quantification and classification insights.

Cannabis sativa L. inflorescences are rich in cannabinoids and terpenes. Traditional chemical analysis methods for cannabinoids and terpenes, such as liquid and gas chromatography (using UV or MS detectors), are expensive and time-consuming. This study explores the use of Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometric approaches for classifying cannabis chemovars and predicting cannabinoid and terpene concentrations for the first time in freshly harvested (wet) cannabis inflorescence. The study also compares the performance of FT-NIR spectroscopy on wet versus dry cannabis inflorescences. Spectral data from 187 samples across seven cannabis chemovars were analyzed using partial least squares-discriminant analysis (PLS-DA) and partial least squares-regression (PLS-R) models. The PLS-DA models effectively classified chemovars and major classes using only two latent variables (LVs) with minimal overfitting risk, with sensitivity, specificity, and accuracy values approaching 1. Despite the high water content in wet cannabis inflorescence, the PLS-R models demonstrated good to excellent predictive capabilities for nine cannabinoids and eight terpenes using FT-NIR spectra for the first time, achieving cross-validation and prediction R-squared values greater than 0.7, ratio of performance to interquartile range (RPIQ) exceeding 2, and a RMSECV/RMSEC ratio below 1.24. However, the low-cannabidiolic acid submodel and (-)-Δ9-trans-tetrahydrocannabinol model showed poor predictive performance. Some cannabinoid and terpene prediction models in wet cannabis inflorescence exhibited lower predictive capabilities compared with previously published models for dry cannabis inflorescence. These findings suggest that FT-NIR spectroscopy can be a viable rapid on-site analytical tool for growers during the inflorescence flowering stage.

Surface-enhanced Raman spectroscopy for the characterization of xylanases enzyme

Xylanases are essential hydrolytic enzymes which break down the plant cell wall polysaccharide, xylan composed of D-xylose monomers. Surface-enhanced Raman Spectroscopy (SERS) was utilized for the characterization of interaction of xylanases with xylan at varying concentrations. The study focuses on the application of SERS for the characterization of enzymatic activity of xylanases causing hydrolysis of Xylan substrate with increase in its concentration which is substrate for this enzyme in the range of 0.2% to 1.0%. SERS differentiating features are identified which can be associated with xylanases treated with different concentrations of xylan. SERS measurements were performed using silver nanoparticles as SERS substrate to amplify Raman signal intensity for the characterization of xylan treated with xylanases. Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) were applied to analyze the spectral data to analyze differentiation between the SERS spectra of different samples. Mean SERS spectra revealed significant differences in spectral features particularly related to carbohydrate skeletal mode and O-C-O and C–C–C ring deformations. PCA scatter plot effectively differentiates data sets, demonstrating SERS ability to distinguish treated xylanases samples and the PC-loadings plot highlights the variables responsible for differentiation. PLS-DA was employed as a quantitative classification model for treated xylanase enzymes with increasing concentrations of xylan. The values of sensitivity, specificity, and accuracy were found to be 0.98%, 0.99%, and 100% respectively. Moreover, the AUC value was found to be 0.9947 which signifies the excellent performance of PLS-DA model. SERS combined with multivariate techniques, effectively characterized and differentiated xylanase samples as a result of interaction with different concentrations of the Xylan substrate. The identified SERS features can help to characterize xylanases treated with various concentrations of xylan with promising applications in the bio-processing and biotechnology industries.

Suitability of different machine learning algorithms for the classification of the proportion of grassland-based forages at the herd level using mid-infrared spectral information from routine milk control

As the call for an international standard for milk from grassland-based production systems continues to grow, so too do the monitoring and evaluation policies surrounding this topic. Individual stipulations by countries and milk producers to market their milk under their own grass-fed labels include a compulsory number of grazing days per year, ranging from 120 d for certain labels to 180 d for others, a specified amount of herbage in the diet or a prescribed dietary proportion of grassland-based forages (GBF) fed and produced on farm. As these multifarious policy and label requirements are laborious and costly to monitor on farm, fast economical proxies would be advantageous to verify the proportion of GBF consumed by the cows in the final product. With this in mind, we employed readily available mid-infrared spectral data (n = 1132 spectra) from routine milk controls to develop binary classification models for 4 main feed groups from a primarily forage-based diet: Total GBF (≥50% n = 955, ≥ 75% n = 599, ≥ 85% n = 356), pasture (≥20% n = 451, ≥ 50% n = 284, ≥ 70% n = 152), fresh herbage (pasture + fresh herbage indoor feeding, ≥ 20% n = 517, ≥ 50% n = 325, ≥ 70% n = 182) and whole plant corn (fresh + conserved) (≥10% n = 646, ≥ 30% n = 187), the latter as a negative control. We compared 4 machine learning methods to assess which statistical model performs best at discriminating these classes. Three of these models have not yet been tested for herd-level dietary proportion classification and all 4 follow completely different approaches: least absolute shrinkage and selection operator (LASSO), partial least squares discriminant analysis (PLS-DA), random forest (RF) and support vector machines (SVM). Seasonality has been a missing element from previous dietary herbage proportion classification models. As grazing and fresh herbage indoor feeding are highly dependent on the season, we developed an indicator to incorporate seasonality in a consistent, unbiased manner into our models. We also tested 3 sets of covariates. The first set included only mid-infrared spectra derived data, the second included mid-infrared spectra derived data plus seasonality indices and the third included mid-infrared spectra derived data, seasonality indices and additional herd specific information (DIM, breed and parity). Of the 4 machine learning algorithms tested for the binary classification of GBF proportion at herd level, LASSO and PLS-DA performed best according to evaluation metrics; however, the RF and SVM models were not far behind the best performing model evaluation metrics in each feed category. Our best performing model, the LASSO model containing seasonality indices and herd specific information, classified total GBF ≥50% with an accuracy of 78.6%, precision of 85.1%, sensitivity of 90.6%, specificity 14.1% and F1 score (harmonic mean of precision and sensitivity) of 87.7%, this was very similar to the PLS-DA model. Our results suggest that in general LASSO and PLS-DA machine learning algorithms perform better for dietary GBF classification than RF or SVM algorithms.