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In this study, a novel pattern recognition method named boosting–partial least squares–discriminant analysis (Boosting-PLS-DA) was developed for the non-destructive and accurate origin identification of food and medicine homologous substances (FMHSs). Taking Gastrodia elata, Aurantii Fructus Immaturus, and Angelica dahurica as examples, spectra of FMHSs from different origins were obtained by portable near-infrared (NIR) spectroscopy without destroying the samples. The identification models were developed with Boosting-PLS-DA, compared with principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA) models. The model performances were evaluated using the validation set and an external validation set obtained one month later. The results showed that the Boosting-PLS-DA method can obtain the best results. For the analysis of Aurantii Fructus Immaturus and Angelica dahurica, 100% accuracies of the validation sets and external validation sets were obtained using Boosting-PLS-DA models. For the analysis of Gastrodia elata, Boosting-PLS-DA models showed significant improvements in external validation set accuracies compared to PLS-DA, reducing the risk of overfitting. Boosting-PLS-DA method combines the high robustness of ensemble learning with the strong discriminative capability of discriminant analysis. The generalizability will be further validated with a sufficiently large external validation set and more types of FMHSs.

Deterioration of water quality is a major problem world widely according to many international non-governmental organizations (NGO). As one of the European Union (EU) countries, Bulgaria is also obliged by EU legislation to maintain best practices in assessing surface water quality and the efficiency of wastewater treatment processes. For these reasons studies were undertaken to utilize ecotoxicological (Microtox®, Phytotoxkit FTM, Daphtoxkit FTM), instrumental (to determine pH, electrical conductivity (EC), chemical oxygen demand, total suspended solids (TSS), total nitrogen (N) and phosphorus (P), chlorides, sulphates, Cr, Co, Cu, Cd, Ba, V, Mn, Fe, Ni, Zn, Se, Pb), as well as advanced chemometric methods (partial least squares–discriminant analysis (PLS-DA)) in data evaluation to comprehensively assess wastewater treatment plants’ (WWTPs) effluents and surface waters quality around 21 major Bulgarian cities. The PLS-DA classification model for the physicochemical parameters gave excellent discrimination between WWTP effluents and surface waters with 93.65% correct predictions (with significant contribution of EC, TSS, P, N, Cl, Fe, Zn, and Se). The classification model based on ecotoxicological data identifies the plant test endpoints as having a greater impact on the classification model efficiency than bacterial, or crustaceans’ endpoints studied.

The medicinal use of bile‐derived materials dates back thousands of years. However, due to their complex origins and morphological similarities, adulteration of expensive bile types with cheaper alternatives remains prevalent in the market, significantly compromising quality control and regulatory oversight. To address this critical issue, this study employed ultra‐high‐performance liquid chromatography coupled with charged aerosol detection (UHPLC‐CAD) technology combined with chemometric approaches, including chromatographic fingerprint similarity analysis, hierarchical cluster analysis, principal component analysis, and orthogonal partial least squares‐discriminant analysis to systematically analyze 10 bile‐derived medicinal materials: biotransformed bear bile powder, bear bile powder, pig bile powder, ox bile powder, sheep bile powder, chicken bile powder, duck bile powder, goose bile powder, rabbit bile powder, and snake bile powder. Chromatographic fingerprint similarity analysis revealed significant differences among the medicinal materials from different sources. Hierarchical cluster analysis and principal component analysis successfully achieved accurate classification of the 10 types of bile‐derived medicinal materials, while orthogonal partial least squares‐discriminant analysis identified nine characteristic differential components with variable importance in projection >1: taurocholic acid, taurochenodeoxycholic acid, glycodeoxycholic acid, tauroursodeoxycholic acid, glycocholic acid, taurodeoxycholic acid, glycohyodeoxycholic acid, glycochenodeoxycholic acid, and chenodeoxycholic acid. This study established a UHPLC‐CAD method capable of simultaneously separating 17 bile acids, combined with multidimensional chemometric approaches, to conduct a comprehensive analysis of 10 bile‐derived medicinal materials. The research successfully identified characteristic compounds for each bile powder type, providing a reliable methodology for both the identification and quality control of bile‐derived medicinal materials.

Background Chronic inflammation, changes in body composition, and declining physical function are hallmarks of the ageing process. The aim of the present study was to provide a preliminary characterisation of the relationship among these age‐related phenomena via multivariate modelling. Methods Thirty‐five old adults (OAs) and 17 young adults (YAs) were enrolled. The volume of skeletal muscle, subcutaneous adipose tissue (SAT), and intermuscular adipose tissue (IMAT) of the thigh was quantified by three‐dimensional magnetic resonance imaging. Muscle strength was measured by knee extension strength testing. In OAs, physical performance was further assessed via the Short Physical Performance Battery (SPPB). Multi‐block partial least squares‐discriminant analysis (PLS‐DA) was employed to explore the relationship among inflammatory profiles and functional and imaging parameters. Double cross‐validation procedures were used to validate the predictive ability of the PLS‐DA model. Results The optimal complexity of the PLS‐DA model was found to be two latent variables. The proportion of correct classification was 92.3% in calibration (94.1% in YAs and 91.4% in OAs), 84.6% in internal validation (95.3% in YAs and 78.5% in OAs), and 82.6% in external validation (94% in YAs and 76.9% in OAs). Relative to YAs, OAs were characterised by smaller muscle volume, greater IMAT volume, lower muscle strength, and higher levels of myeloperoxidase, P‐selectin, soluble intercellular adhesion molecule 1, and vascular cell adhesion molecule 1. Compared with OAs with SPPB >8, those scoring ≤8 were characterised by smaller muscle volume, greater SAT volume, lower muscle strength, and higher levels of interleukin 1 beta, 6, 10, 12, 13, tumour necrosis factor alpha, and granulocyte‐macrophage colony‐stimulating factor. Conclusions Multi‐block PLS‐DA identified distinct patterns of relationships among circulating cytokines and functional and imaging parameters in persons of different ages and varying levels of physical performance. The longitudinal implementation of such an innovative strategy could allow for the tracking of health status over time, the early detection of deviations in health trajectories, and the monitoring of response to treatments.

The evaluation of Chinese dianhong black tea (CDBT) grades was an important indicator to ensure its quality. A handheld spectroscopy system combined with chemometrics was utilized to assess CDBT from eight grades. Both variables selection methods, namely genetic algorithm (GA) and successive projections algorithm (SPA), were employed to acquire the feature variables of each sample spectrum. A partial least‐squares discriminant analysis (PLS‐DA) and support vector machine (SVM) algorithms were applied for the establishment of the grading discrimination models based on near‐infrared spectroscopy (NIRS). Comparisons of the portable and benchtop NIRS systems were implemented to obtain the optimal discriminant models. Experimental results showed that GA‐SVM models by the handheld sensors yielded the best predictive performance with the correct discriminant rate (CDR) of 98.75% and 100% in the training set and prediction set, respectively. This study demonstrated that the handheld system combined with a suitable chemometric and feature information selection method could successfully be used for the rapid and efficient discrimination of CDBT rankings. It was promising to establish a specific economical portable NIRS sensor for in situ quality assurance of CDBT grades. The handheld near‐infrared spectroscopy (NIRS) system combined with a suitable chemometric and feature information selection method could successfully be used for the rapid and efficient discrimination of Chinese dianhong black tea (CDBT) grades. It was promising to establish a specific economical portable NIRS sensor for in situ quality assurance of CDBT grades.

Background：Tuberculosis （TB） is a chronic wasting inflammatory disease characterized by multisystem involvement,which can cause metabolic derangements in afflicted patients.Metabolic signatures have been exploited in the study of several diseases.However,the serum that is successfully used in TB diagnosis on the basis of metabolic profiling is not by much.Methods：Orthogonal partial least-squares discriminant analysis was capable of distinguishing TB patients from both healthy subjects and patients with conditions other than TB.Therefore,TB-specific metabolic profiling was established.Clusters of potential biomarkers for differentiating TB active from non-TB diseases were identified using Mann-Whitney U-test.Multiple logistic regression analysis of metabolites was calculated to determine the suitable biomarker group that allows the efficient differentiation of patients with TB active from the control subjects.Results：From among 271 participants,12 metabolites were found to contribute to the distinction between the TB active group and the control groups.These metabolites were mainly involved in the metabolic pathways of the following three biomolecules：Fatty acids,amino acids,and lipids.The receiver operating characteristic curves of3D,7D,and 11D-phytanic acid,behenic acid,and threoninyl-γ-glutamate exhibited excellent efficiency with area under the curve （AUC） values of 0.904 （95% confidence interval [CI]：0.863-0.944）,0.93 （95% CI：0.893-0.966）,and 0.964 （95% CI：0.941-0.988）,respectively.The largest and smallest resulting AUCs were 0.964 and 0.720,indicating that these biomarkers may be involved in the disease mechanisms.The combination of lysophosphatidylcholine （18∶0）,behenic acid,threoninyl-γ-glutamate,and presqualene diphosphate was used to represent the most suitable biomarker group for the differentiation of patients with TB active from the control subjects,with an AUC value of 0.991.Conclusion：The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases.The metabolomics-based analysis provides specific insights into the biology of TB and may offer new avenues for TB diagnosis.

Intensive study of the metabolome during the Douchi fermentation can provide new knowledge for optimizing the fermentation process. In this work, the metabolic characterization throughout the fermentation of Mucor racemosus Douchi was investigated using gas chromatography with time‐of‐flight mass spectrometry. A total of 511 peaks were found, and 114 metabolites were identified. The fermentation process was clearly distinguished into two main phases by principal components analysis and orthogonal partial least squares‐discriminant analysis. All the samples in the score plots were within the 95% Hotelling T2 ellipse. Two separated clusters can be seen clearly in the score plot, which represents the two stages of fermentation: koji‐making (within 48 hr) and postfermentation (after 48 hr). Besides, clear separation and discrimination by both methods were found among different fermentation time within 15 days, while the discrimination cannot be found with more than 15 days of fermentation, indicating that the fermentation of Douchi was finished in 15 days. Due to the synergistic effect of protease and hydrolase accumulated in the early stage, proteins and other big molecular substances are rapidly hydrolyzed into a large number of small molecule components. However, the activity of enzymes decreased with the further fermentation, and some free amino acids were consumed in Maillard reaction. Therefore, there was no significant change in the content of small molecular substances after 15 days of fermentation. Furthermore, the levels of some metabolites such as alanine and lysine involved in the fermentation varied significantly throughout the processes. This study provides new insights for the metabolomics characteristics of Douchi fermentation. Metabolic profile characterization throughout the Douchi fermentation was studied. Clear discrimination of samples with different fermentation time can be found. Important significantly different metabolites in the fermentation were obtained.

Urea is added as an adulterant to give milk whiteness and increase its consistency for improving the solid not fat percentage, but the excessive amount of urea in milk causes overburden and kidney damages. Here, an innovative sensitive methodology based on near‐infrared spectroscopy coupled with multivariate analysis has been proposed for the robust detection and quantification of urea adulteration in fresh milk samples. In this study, 162 fresh milk samples were used, those consisting 20 nonadulterated samples (without urea) and 142 with urea adulterant. Eight different percentage levels of urea adulterant, that is, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, and 1.70%, were prepared, each of them prepared in triplicates. A Frontier NIR spectrophotometer (BSEN60825‐1:2007) by Perkin Elmer was used for scanning the absorption of each sample in the wavenumber range of 10,000–4,000 cm‐1, using 0.2 mm path length CaF2 sealed cell at resolution of 2 cm‐1. Principal components analysis (PCA), partial least‐squares discriminant analysis (PLS‐DA), and partial least‐squares regressions (PLSR) methods were applied for the multivariate analysis of the NIR spectral data collected. PCA was used to reduce the dimensionality of the spectral data and to explore the similarities and differences among the fresh milk samples and the adulterated ones. PLS‐DA also showed the discrimination between the nonadulterated and adulterated milk samples. The R‐square and root mean square error (RMSE) values obtained for the PLS‐DA model were 0.9680 and 0.08%, respectively. Furthermore, PLSR model was also built using the training set of NIR spectral data to make a regression model. For this PLSR model, leave‐one‐out cross‐validation procedure was used as an internal cross‐validation criteria and the R‐square and the root mean square error (RMSE) values for the PLSR model were found as 0.9800 and 0.56%, respectively. The PLSR model was then externally validated using a test set. The root means square error of prediction (RMSEP) obtained was 0.48%. The present proposed study was intended to contribute toward the development of a robust, sensitive, and reproducible method to detect and determine the urea adulterant concentration in fresh milk samples. Generalized flow chart of partial least‐squares regression analysis.

The field of plant phenotype is used to analyze the shape and physiological characteristics of crops in multiple dimensions. Imaging, using non-destructive optical characteristics of plants, analyzes growth characteristics through spectral data. Among these, fluorescence imaging technology is a method of evaluating the physiological characteristics of crops by inducing plant excitation using a specific light source. Through this, we investigate how fluorescence imaging responds sensitively to environmental stress in garlic and can provide important information on future stress management. In this study, near UV LED (405 nm) was used to induce the fluorescence phenomenon of garlic, and fluorescence images were obtained to classify and evaluate crops exposed to abiotic environmental stress. Physiological characteristics related to environmental stress were developed from fluorescence sample images using the Chlorophyll ratio method, and classification performance was evaluated by developing a classification model based on partial least squares discrimination analysis from the image spectrum for stress identification. The environmental stress classification performance identified from the Chlorophyll ratio was 14.9% in F673/F717, 25.6% in F685/F730, and 0.209% in F690/F735. The spectrum-developed PLS-DA showed classification accuracy of 39.6%, 56.2% and 70.7% in Smoothing, MSV, and SNV, respectively. Spectrum pretreatment-based PLS-DA showed higher discrimination performance than the existing image-based Chlorophyll ratio.

One of the most important problems associated with the rice industry is the authenticity, mainly the identification of varieties by providing a reliable, fast, yet accurate method. To overcome these limitations, the development of fast and non-destructive methodologies for different rice type classification is, nowadays, a huge challenge for producers. The near-infrared (NIR) spectroscopy associated to principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and support vector machines (SVM) for discrimination and classification of rice varieties (Indica and Japonica) were explored after different spectra processing steps such as multiplicative scatter correction (MSC), first derivative and second derivative. The SVM model developed after the MSC processing procedure, showed a significant fitting accuracy (97%), cross-validation (93%) and prediction (91%). These data support the robustness of the model for efficient rice types classification. In terms of spectral analysis, the major differences between both rice types are present at range 7476–7095 cm−1, 7046 cm−1 and 4264–4153 cm−1, which can be used for its discrimination. This study showed that NIR spectroscopy associated to PLS-DA and SVM techniques allowed an efficient discrimination of rice samples, being considered as a suitable strategy for a competent system for fully automated classification and sorting of rice types grouping with a high level of accuracy, representing a valuable approach for discrimination and anti-fraud procedure for food control as well as in terms of security issues of any product.