Search Results (62)

Understanding interspecific and environmental influences on secondary metabolite profiles can be critical in plant metabolomics. This study used a hierarchical orthogonal projections to latent structure discriminant analysis (OPLS-DA) to classify the foliar metabolomes of four naturally growing Mitragyna species in Thailand, M. speciosa, M. diversifolia, M. hirsuta, and M. rotundifolia. Using a recursive binary classification, interspecific and environmental influences were determined in multiple class separations, while identifying key metabolites driving these distinctions. Gas chromatography–mass spectrometry (GC-MS) annotated 409 metabolites, and through a progressive class differentiation using hierarchical OPLS-DA, M. speciosa exhibited a metabolome distinct from the other three species. However, the metabolomes of M. hirsuta and M. rotundifolia had a lot of overlap, while M. diversifolia displayed regional metabolic variation, emphasizing the role of environmental factors in shaping its chemical composition. Key metabolites, such as mitragynine, isorhynchophylline, squalene, and vanillic acid, among others, were identified as major discriminators across the hierarchical splits. Unlike conventional OPLS-DA, which struggles with multiclass datasets, the recursive approach identified class structures that were biologically relevant, without the need for manual pairwise modeling. The results aligned with prior morphological and genetic studies, validating the method’s robustness in capturing interspecific and environmental differences, which can be used in high-dimensional multiclass plant metabolomics. Full article

Elemental contents are effective fingerprints for Pu-erh tea’s geographical traceability, which is crucial for consumer protection and sustainable development. Region and processing methods are key factors influencing the tea’s elemental fingerprint. This study analyzed 28 elements in Pu-erh tea samples from three Yunnan production regions subjected to different processing stages in the year of 2023. The results show that significant regional differences were observed for 25 of the 28 elements. As, Li, Cu, Zn, and Cd contents vary significantly during processing. The contents of 27 elements (excluding Pb) are significantly influenced by the interaction between region and processing stage. Orthogonal partial least squares discriminant analysis (OPLS-DA) achieved good validation (Q² = 0.946) and identified 18 key factors, while the original and cross-validation correct classification rates were 100% and 98.6%, respectively. Crucially, the robustness of the model was confirmed with 100% accuracy through an independent validation set from tea samples in the harvest year of 2024. This study confirms that the elemental contents of Pu-erh tea are mainly influenced by region rather than processing stage, and elemental analysis can trace the geographical origin of Pu-erh effectively, even when mixed with a differently processed tea. Full article

(1) Background: This study used the cold-tolerant cultivar “Daye No. 3” (DY) and the cold-sensitive cultivar “Longdong” (LD) as plant materials to study the metabolic changes in plant hormones in alfalfa (Medicago sativa L.) under cold stress. (2) Methods: The targeted quantitative detection of phytohormones in alfalfa was carried out by liquid chromatography–tandem mass spectrometry (LC-MS/MS) technology. Principal component analysis (PCA), orthogonal signal correction, and partial least squares discriminant analysis (OPLS-DA) were used to investigate sample classification and screen differential metabolites. (3) Results: The results showed that 17 differential metabolites were detected. Seven metabolites showed common changes in the two cultivars after low-temperature stress induction. The levels of tryptamine, N-jasmonoylisoleucine, trans-zeatin riboside, isopentenyladenine riboside, cis-zeatin riboside, and gibberellin A7 were decreased, while N6-isopentenyladenine levels increased. In addition, compared with the LD variety, DY had more metabolite changes in response to low-temperature stress. Abscisic acid and trans-zeatin were elevated, whereas IAA-alanine, dihydrozeatin riboside, and indole-3-carboxaldehyde showed reduced concentrations. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that differential plant hormones were more active in plant hormone signal transduction, zeatin biosynthesis, and tryptophan metabolism pathways. In addition, a total of 12 metabolites in these three pathways showed common changes under cold stress. (4) This study identified significant metabolomic differences between two alfalfa genotypes under stress. It highlighted key pathways and provided new insights into the metabolic changes of alfalfa under cold-stress conditions. Full article

The integration of HS-SPME-GC/MS and UPLC-MS/MS techniques enabled the profiling of volatile organic compounds (VOCs) and amino acids (AAs) in 18 crabapple flower cultivars, facilitating the development of a novel VOC–AA model. Among the 51 identified VOCs, benzyl alcohol, benzaldehyde, and ethyl benzoate were predominant, categorizing cultivars into fruit-almond, fruit-sweet, and mixed types. The amino acids, namely glutamic acid (Glu), asparagine (Asn), aspartic acid (Asp), serine (Ser), and alanine (Ala) constituted 83.6% of the total AAs identified. Notably, specific amino acids showed positive correlations with key VOCs, suggesting a metabolic regulatory mechanism. The Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model, when combined with volatile organic compounds (VOCs) and amino acid profiles, enabled more effective aroma type classification, providing a robust foundation for further studies on aroma mechanisms and targeted breeding. Full article

As demand for plant-based beverages grows, analytical tools are needed to classify and understand their structural and compositional diversity. This study applied a multi-analytical approach to characterize 41 commercial almond-, oat-, rice- and soy-based beverages, evaluating attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, protein secondary structure proportions, colorimetry, and microscopic image texture analysis. A total of 26 variables, derived from ATR-FTIR and protein secondary structure assessment, were employed in multivariate models, using partial least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) to evaluate classification performance. The results indicated clear group separation, with soy and rice beverages forming distinct clusters while almond and oat samples showing partial overlap. Variable importance in projection (VIP) scores revealed that β-turn and α-helix protein structures, along with carbohydrate-associated spectral bands, were the key features for beverages’ classification. Textural features derived from microscopy images correlated with sugar and carbohydrate content and color parameters were also employed to describe beverages’ differences related to sugar content and visual appearance in terms of homogeneity. These findings demonstrate that combining ATR-FTIR spectral data with protein secondary structure data enables the effective classification of plant-based beverages, while microscopic image textural and color parameters offer additional extended product characterization. Full article

Polygonati Rhizoma, a renowned edible homologous material, encompasses an array of widely distributed species. Despite their morphological and medicinal similarities, their overlapping distribution and evolving varieties present challenges for their classification and identification. This study provides a comprehensive characterization of the physicochemical and antioxidant properties of polysaccharides extracted from three common species: P. sibiricum, P. cyrtonema, and P. kingianum. An analysis of their monosaccharide composition reveals distinct profiles, with P. kingianum polysaccharides (PKPs) demonstrating a significantly higher glucose content compared to P. sibiricum polysaccharides (PSPs) and P. cyrtonema polysaccharides (PCPs). Infrared (IR) spectroscopy and derivative spectral processing affirm both structural similarities and quantitative differences in functional groups among the species. Multivariate analyses, including HCA, PCA, and OPLS-DA, confidently classify the 12 batches of polysaccharides into three distinct groups (PSPs, PCPs, and PKPs), exhibiting strong model robustness (PCA: R²X = 0.951, Q² = 0.673; OPLS-DA: R²Y = 0.953, Q² = 0.922). Importantly, PKPs from number S11 show exceptional in vitro antioxidant activity (DPPH scavenging), which directly correlates with their high monosaccharide content and distinctive spectral features. These findings establish a robust foundation for the quality assessment of Polygonatum polysaccharides as potential natural antioxidants in functional foods, positioning PKPs as leading candidates for dietary supplement development. Full article

Wuyi Shuixian tea, a premium oolong tea known for its complex floral-fruity aroma, exhibits significant quality variations across different grades. This study systematically analyzed the aroma characteristics and key fragrant compounds of four grades (Grand Prize SA, First Prize SB, Outstanding Award SC, and Non-award SD) using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS), odor activity value (OAV) analysis, and multivariate statistical methods. A total of 159 volatile compounds were identified, with similar compound categories but distinct concentration gradients between grades. OAV-splitting analysis (based on OAV ≥ 1 as the threshold for aroma activity) identified β-ionone (fruity), octanal (fatty), and linalool (floral) as core aroma-active contributors, as their OAV values significantly exceeded 10 in awarded grades (SA, SB, SC), indicating dominant roles in sensory perception. Notably, linalool, a floral marker, showed a concentration gradient (SA > SB > SC) and was absent in SD, serving as a critical determinant of grade differentiation. Orthogonal partial least squares-discriminant analysis (OPLS-DA) further distinguished awarded grades (SA, SB, SC) by balanced fruity, floral, and woody notes, while SD lacked floral traits and exhibited burnt aromas. This classification was supported by hierarchical clustering analysis (HCA) of volatile profiles and principal component analysis (PCA). Electronic nose data validated these findings, showing strong correlations between sensor responses (W5S/W2W) and key compounds like hexanal and β-ionone. This study elucidates the molecular basis of aroma-driven quality grading in Wuyi Shuixian tea, providing a scientific framework for optimizing processing techniques and enhancing quality evaluation standards. The integration of chemical profiling with sensory attributes advances precision in tea industry practices, bridging traditional grading with objective analytical metrics. Full article

Flavor is one of the crucial factors that influences the quality and consumer acceptance of baijiu. In this study, we analyzed the volatile organic compound (VOC) profiles of five different quality grades of Nongxiangxing baijiu (NXB), fermented during the summer of 2024, using 2D gas chromatography time-of-flight mass spectrometry (GC×GC–TOF-MS). We employed machine-learning (ML)-based classification and prediction models to evaluate the flavor. For TW, the scores of the sensory evaluation for coordination and overall evaluation were the highest. TW contained the highest concentration of ethyl caproate; we detected 965 VOCs in total, including several pyrazine compounds with potential health benefits. Principal component analysis (PCA) combined with orthogonal partial least squares discriminant analysis (OPLS-DA) enabled us to distinguish different samples, with eight VOCs emerging as primary contributors to the aroma of the samples, possessing variable importance in projection (VIP) values > 1. Furthermore, we tested eight ML models; random forest (RF) demonstrated the best classification performance, effectively discriminating samples based on their VOC profiles. The key VOC contributors that showed quality-grade specificity included 1-butanol, 3-methyl-1-butanol, and ethyl 5-methylhexanoate. The results elucidate the flavor-based features of NXB and provide a valuable reference for discriminating and predicting baijiu flavors. Full article

Background/Objectives: Plant-derived secondary metabolites have long contributed to the discovery of novel therapeutic agents, especially in the treatment of parasitic and infectious diseases in developing countries. Metabolomics provides a systems-level approach to understanding plant biochemistry, enabling the discovery of secondary metabolites with pharmacological relevance. Taraxacum kok-saghyz (TKS), widely known for its rubber-producing capabilities, remains underexplored as a medicinal plant. Given the well-established therapeutic properties of Taraxacum officinale and the emerging pharmacological profiles of related species, this study investigates the metabolic composition of TKS roots and leaves to uncover bioactive compounds with antioxidant, anti-inflammatory, or hepatoprotective potential. Methods: Widely targeted metabolomics was conducted on 10-month-old field-grown Kultevar™ TKS plants using ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS). Samples were hand-harvested and preserved on dry ice to maintain biochemical integrity. Metabolite identification and classification were performed using the MWDB and KEGG databases. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to evaluate metabolic variation between tissues. Results: A total of 1813 metabolites were identified, including flavonoids, alkaloids, lipids, amino acids, and phenolic compounds. Differential analysis revealed 964 significantly altered metabolites—609 downregulated and 355 upregulated in roots relative to leaves. Multivariate analysis confirmed clear tissue-specific metabolic profiles. KEGG pathway enrichment highlighted the involvement of flavonoid biosynthesis, amino acid metabolism, and lipid metabolism pathways, suggesting bioactive potential. This study presents the first comprehensive metabolic profile of TKS, highlighting its potential value beyond rubber production. The detection of numerous therapeutic secondary metabolites supports its promise as a pharmaceutical and nutraceutical resource. Further functional validation of identified compounds is warranted. Full article

Background/Objectives: Salivary gland tumors (SGTs) are a rare and histologically heterogeneous group of neoplasms that are challenging to diagnose due to phenotypic heterogeneity and overlapping histomorphological markers. Accurate diagnosis is required for clinical management, particularly in unusual subtypes. The objective of this study was to ascertain whether attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, in combination with enzymatic deglycosylation, would be useful in SGT classification by detecting glycosylation-related metabolic variations. Methods: 155 tissue sections, consisting of 80 SGTs and 75 controls, were analyzed. ATR-FTIR spectroscopy was used to record the mid-infrared (MIR) spectra (4000–400 cm⁻¹) of enzymatically untreated and deglycosylated samples. Spectral data were preprocessed and analyzed by principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). Enzymatic deglycosylation focused on sialic acid and fucose residues with α2-3,6,8 neuraminidase, α1-2,4,6 fucosidase O, and α1-3,4 fucosidase. Results: Tumor and control samples were discriminated with an OPLS-DA model, achieving an accuracy of 81.9% (78.7% for controls and 85.0% for tumors), especially in the glycosylation-relevant spectral range (850–1250 cm⁻¹). Classification between benign and malignant tumors was more challenging, with an accuracy of 70.0% (72.5% for benign and 67.5% for malignant cases). Enzymatic deglycosylation resulted in detectable changes in the MIR spectra, confirming the contribution of glycosylation to tumor-specific signatures. Benign vs. malignant tumor discrimination was still poor and was not much enhanced in the sense of incorporating glycosylation-specific regions. Conclusions: ATR-FTIR spectroscopy coupled with enzymatic deglycosylation can distinguish tumor and control tissues based on glycan-associated spectral differences. Application of the technique to benign/malignant SGT discrimination is hampered by spectral overlap and tumor heterogeneity. Further research will be necessary to explore other clustering algorithms and larger and more homogeneous datasets for improved diagnostic accuracy. Full article

The untargeted metabolomics of Newhall navel oranges from three areas in China—Ganzhou, Fengjie, and Zigui—with geographical indication (GI) was measured using LC-MS/MS. Orthogonal partial least squares discriminant analysis was performed for sample classification and important metabolite identification. This approach identified the best markers of the geographical origin able to discriminate Fengjie, Ganzhou, and Zigui orange samples. For peeled samples, 2-isopropylmalic acid, succinic acid, citric acid, L-aspartic acid, L-glutamic γ-semialdehyde, D-β-phenylalanine, hesperetin, hydrocinnamic acid, 4-hydroxycinnamic acid, and dehydroascorbate were the markers used to discriminate the geographical origin. All these markers were overexpressed in the peeled samples from the Zigui area, followed by the Ganzhou area. As for unpeeled samples, L-glutamic γ-semialdehyde, isovitexin 2′-O-β-D-glucoside, 2-isopropylmalic acid, isovitexin, diosmetin, trans-2-hydroxycinnamate and trans-cinnamate, L-aspartic acid, hydrocinnamic acid, and β-carotene were used to discriminate their origin. The first seven markers in Zigui-planted whole samples showed the highest levels, and the last three markers were richest in Ganzhou-planted samples. According to the variation in the markers for discriminating the origins of the peeled or unpeeled Newhall navel oranges with GI and the highest value of titratable acidity in those from Zigui, the samples planted in Ganzhou have the best balance between taste and nutrition. This work confirms that the approach of untargeted metabolomics combined with OPLS-DA is an effective way for origin tracing and overall quality evaluation. Full article

The chemical composition and quality of honey are influenced by its botanical, geographic, and entomological origins, as well as climatic conditions. In this study, the physicochemical characteristics, microbial communities, and hydrocarbon compounds of honey produced by Apis mellifera, Apis cerana, Apis laboriosa, Apis dorsata, and Apis florea were elucidated. The physicochemical profile of the honey exhibited significant differences across species, including moisture content (18.27–23.66%), fructose (33.79–38.70%), maltose (1.10–1.93%), electrical conductivity (0.37–0.74 mS/cm), pH (3.36–3.72), diastase activity (4.50–29.97 diastase number), and color (37.90–102.47 mm). Microbial analysis revealed a significant abundance of lactic acid bacteria, particularly the Apilactobacillus genus in A. laboriosa honey and the Lactobacillus in A. florea honey, indicating significant probiotic potential. Chemometric methods, principal component analysis, hierarchical cluster analysis, and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to classify the honey samples based on the 12 beeswax-derived hydrocarbons. The OPLS-DA model demonstrated 100% accuracy in predicting the entomological origin of honey, indicating that specific hydrocarbons are reliable markers for honey classification. Full article

The Jingbai pear is one of the best pear species in China with high quality and nutrition values which are closely linked to its geographical origin. With the purpose of discriminating the PGI Mentougou Jingbai pear from three other producing regions, the stable isotope ratios and elemental profiles of the pears (n = 52) and the corresponding soils and groundwater were determined using isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry (ICP-MS), respectively. The results revealed that δ¹⁵N, δ¹⁸O_J, and Li were significantly different (p < 0.05) in samples from different regions, which indicated their potential to be used in the geographical origin classification of the Jingbai pear. The nitrogen isotopic values of the pear pulp were positively correlated with the δ¹⁵N value and nitrogen content of the corresponding soils, whilst the B, Na, K, Cr, and Cd contents of the pear pulps were positively correlated with their corresponding soils. Orthogonal partial least squares discriminant analysis (OPLS-DA) was performed in combination with analysis of the stable isotopes and elemental profiles, making it possible to distinguish the cultivation regions from each other with a high prediction accuracy (a correct classification rate of 92.3%). The results of this study highlight the potential of stable isotope ratios and elemental profiles to trace the geographical origin of pears at a small spatial scale. Full article

The diagnostic criteria for fibromyalgia (FM) have relied heavily on subjective reports of experienced symptoms coupled with examination-based evidence of diffuse tenderness due to the lack of reliable biomarkers. Rheumatic disorders that are common causes of chronic pain such as rheumatoid arthritis, systemic lupus erythematosus, osteoarthritis, and chronic low back pain are frequently found to be comorbid with FM. As a result, this can make the diagnosis of FM more challenging. We aim to develop a reliable classification algorithm using unique spectral profiles of portable FT-MIR that can be used as a real-time point-of-care device for the screening of FM. A novel volumetric absorptive microsampling (VAMS) technique ensured sample volume accuracies and minimized the variation introduced due to hematocrit-based bias. Blood samples from 337 subjects with different disorders (179 FM, 158 non-FM) collected with VAMS were analyzed. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. The OPLS-DA algorithm enabled the classification of the spectra into their corresponding classes with 84% accuracy, 83% sensitivity, and 85% specificity. The OPLS-DA regression plot indicated that spectral regions associated with amide bands and amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. Full article

Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm⁻¹ enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM. Full article