Orthogonal Partial Least Squares Discriminant Analysis Research Articles

Volatiles extracted from Melaleuca Rugulosa (Link) Craven leaves: comparative profiling, bioactivity screening, and metabolomic analysis.

Melaleuca species (family Myrtaceae) are characterized by their wide-ranging applications as antimicrobials and in skin-related conditions. Herein, we estimated the volatile profile and biological significance of M. rugulosa (Link) leaves for the first time supported by a dereplication protocol. Volatile components were extracted using hydrodistillation (HD), supercritical fluid (SF), and headspace (HS) techniques and identified using GC/MS. The variations among the three extracts were assessed using principal component analysis and orthogonal partial least square discriminant analysis (OPLS-DA). The extracted volatiles were tested for radical scavenging activity, anti-aging, and anti-hyperpigmentation potential. Finally, disc diffusion and broth microdilution assays were implemented to explore the antibacterial capacity against Streptococcus pyogenes, Staphylococcus aureus, Clostridium perfringens, and Pseudomonas aeruginosa. The yield of the SF technique (0.8%) was three times higher than HD. GC/MS analysis revealed that the oxygenated compounds are the most proponents in the three extracts being 95.93% (HD), 80.94% (HS), and 48.4% (SF). Moreover, eucalyptol (1,8-cineol) represents the major component in the HD-EO (89.60%) and HS (73.13%) volatiles, while dl-α-tocopherol (16.27%) and α-terpineol (11.89%) represent the highest percentage in SF extract. Regarding the bioactivity profile, the HD-EO and SF-extract showed antioxidant potential in terms of oxygen radical absorbance capacity, and β- carotene assays, while exerting weak activity towards DPPH. In addition, they displayed potent anti-elastase and moderate anti-collagenase activities. The HD-EO exhibited potent anti-tyrosinase activity, while the SF extract showed a moderate level compared to tested controls. OPLS-DA and dereplication studies predicted that the selective antibacterial activity of HD-EO to S. aureus was related to eucalyptol, while SF extract to C. perfringens was related to α-tocopherol. M. rugulosa leaves are considered a vital source of bioactive volatile components that are promoted for controlling skin aging and infection. However, further safety and clinical studies are recommended.

Mycotoxin Challenge in Dairy Cows: Assessment of the Efficacy of an Anti-Mycotoxin Agent by Adopting an In Vitro Rumen Simulation Method

To protect ruminants from the harmful effects of mycotoxins, anti-mycotoxin agents can be added to the dietary ration, thus guaranteeing animal health and production. Therefore, the objective of this study was to evaluate the in vitro ruminal initial sequestration (weak binding) and subsequent desorption (strong binding) of an anti-mycotoxin agent based on a mixture of adsorbing material, turmeric and milk thistle extracts and yeast-based components to adsorb or bio-convert aflatoxins (AF), fumonisins B1 and B2 (FB), trichothecene deoxynivalenol (DON), T-2 and HT-2 toxins, and zearalenone (ZEN). Two doses were tested: Dose 1 simulated 30 mg/cow/d, while Dose 2 simulated 90 mg/cow/d of the anti-mycotoxin agent. Each treatment involved three analytical replicates at each of three incubation times (1, 4, and 24 h post-incubation), with two independent experimental runs providing experimental replicates. Analytical methods, including UHPLC-HRMS and multivariate analyses, were used to both quantify mycotoxin concentrations and reveal dose-dependent reductions, with statistical validations indicating significant changes in mycotoxin levels across both dose and time. The results indicated that the anti-mycotoxin agent was able to highly bind AFB1, T2, and HT-2 toxins since its concentration was always under the limit of detection (<1 ppb). Regarding ZEN (weak binding mean: 94.6%; strong binding mean: 62.4%) and FBs (weak binding mean: 58.7%; strong binding mean: 32.3%), orthogonal contrasts indicated that the anti-mycotoxin agent was able to effectively bind these toxins using Dose 1 (p < 0.05). This finding suggests that Dose 1 may be sufficient to achieve the targeted effect and that a further increase does not significantly improve the outcome. Regarding DON, a strong linear relationship was observed between dose and adsorption. However, the complex interactions between the mycotoxin, the ruminal environment, and the anti-mycotoxin agent made it difficult to establish a clear dose–effect relationship (p > 0.10). UHPLC-HRMS analysis identified over 1500 mass features in rumen samples, which were further analyzed to assess the effects of the anti-mycotoxin agent. Hierarchical clustering analysis (HCA) revealed significant changes in the untargeted metabolomic profiles of samples treated with mycotoxins compared to control samples, particularly after 24 h with the anti-mycotoxin treatments. Clear differences were noted between strong binding and weak binding samples. Further analysis using orthogonal partial least squares discriminant analysis (OPLS-DA) highlighted distinct metabolomic profiles, with stronger predictive ability in the strong binding group (Q2 cumulative value of 0.57) compared to the weak binding group (0.30). The analysis identified 44 discriminant compounds in the strong binding model and 16 in the weak binding model. Seven compounds were common to both groups, while silibinin, known for its antioxidant and anti-inflammatory properties, was found among the unique compounds in the weak binding group. Overall, the findings suggest that both doses of the anti-mycotoxin agent significantly influenced the chemical profiles in the rumen, particularly enhancing the binding of mycotoxins, thereby supporting the role of phytogenic extracts in mitigating mycotoxin effects.

Enhancing Phenolic Profiles in ‘Cabernet Franc’ Grapes Through Chitooligosaccharide Treatments: Impacts on Phenolic Compounds Accumulation Across Developmental Stages

High-quality grape raw materials are fundamental for producing premium wine. Ensuring the quality of grape raw materials, particularly enhancing their phenolic profiles, significantly improves wine flavor. Therefore, this study focused on ‘Cabernet Franc’ grapes, where a 0.1% chitooligosaccharide (COS) solution was foliar sprayed during the green pea stage, the onset of veraison stage, and the mid-ripening stage to investigate the impact of exogenous COS treatment on the accumulation of phenolic compounds in grape berries. The results revealed that COS treatment during the green pea and the onset of veraison stages significantly increased the levels of total phenolic, total flavonoid, and total anthocyanin in grapes, with distinct effects on flavanols, phenolic acids, flavonols, and stilbenes, respectively. Eight key compounds most significantly influenced by the treatment were identified through orthogonal partial least squares discriminant analysis (OPLS-DA) and machine learning screening. Specifically, treatment during the green pea stage had a significant impact on total soluble solids, proanthocyanidin B1, catechin, and vanillic acid, while veraison treatment notably affected petunidin-3-O-(6″-O-p-coumaryl)-glucoside, cyanidin-3-O-(6″-O-p-coumaryl)-glucoside, cyanidin-3-O-glucoside and isorhamnetin. This study could provide valuable data references and theoretical support for applying COS in wine grapes and regulating high-quality raw materials.

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.

Comparative characterization of key compounds of Sauce-flavored rounded-Baijiu in northern and southern China and the potential possibility of similar quality of their combined products

The flavor characteristics and sensory attributes of northern Sauce-flavored rounded-Baijiu (NR) and southern Sauce-flavored rounded-Baijiu (SR) were systematically studied. A total of 56 major flavor compounds were quantitatively detected through Gas chromatography-flame ionization detection (GC-FID). Besides, 20 kinds of key aroma compounds and 7 significant differential compounds were identified by calculating the odor activity values (OAVs) and orthogonal partial least squares discriminant analysis (OPLS-DA) of flavor compounds in NR and SR. And then, the relationship between sensory attributes and key aroma compounds were investigated by network analysis. Furthermore, A group of combined-Baijiu (the ratios of the first to the fifth round of Sauce-flavored Baijiu in the north (NR1–5) were 5 % NR1, 35 % NR2, 30 % NR3, 20 % NR4, 10 % NR5) was closer to the style known for blending different proportions of SR into the combined-Baijiu. This study provided a guiding significance for the standardized production of sauce-flavored Baijiu.

An Integrated Approach to Identify the Q-Markers of Banxia-Houpo Decoction Based on Nontargeted Multicomponent Profiling, Network Pharmacology, and Chemometrics.

The inherent complexity of traditional Chinese medicine (TCM) poses significant challenges in directly correlating quality evaluation with clinical efficacy. Banxia-Houpo Decoction (BHD), a classical TCM formula, has demonstrated efficacy in treating globus hystericus. However, the intricate composition of BHD, which contains both volatile and non-volatile active components, complicates efforts to ensure its consistent quality and clinical effectiveness. The aim of this study was to introduce an integrated approach that combines non-targeted multicomponent analysis, network pharmacology, and multivariate chemometrics to identify quality markers for the effective quality control of BHD. First, a nontargeted high-definition MSE method based on ultraperformance liquid chromatography-quadrupole time-of-flight-mass spectrometry (UHPLC-QTOF-MS) was developed for the comprehensive multi-component characterization of BHD. Next, the quality markers of nonvolatile compounds in BHD were identified through network pharmacology analysis. Subsequently, volatile organic compounds (VOCs) in BHD samples were analyzed via headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS). Finally, the orthogonal partial least squares discriminant analysis (OPLS-DA) model was applied to screen for potential markers. Based on in-house library-driven automated peak annotation and comparison with 25 reference compounds, 128 components were identified for the first time. Additionally, honokiol, magnolol, magnoflorine, 6-gingerol, rosmarinic acid, and adenosine were preliminarily identified as potential quality markers for BHD through network pharmacology analysis. By employing two complementary techniques, HS-SPME-GC-MS and HS-GC-IMS, a total of 145 volatile compounds was identified in the BHD samples. Four potential differential VOCs in the BHD samples were further identified based on the variable importance in projection (VIP ≥ 1.5) using HS-GC-IMS combined with chemometric analysis. In conclusion, this study not only contributes to establishing quality standards for BHD but also offers new insights into quality assessment and identification in the development of classical formulations enriched with volatile components.

Study on Evaluation of Fruit Aroma of Plum Variety Resources Based on Headspace Solid-Phase Microextraction Combined with Gas Chromatography-Mass Spectrometry.

To explore the characteristics of and variations in aroma components across different plum varieties and maturity stages, this study employed headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS). This method was used to systematically analyze the aroma components of 12 early-maturing, 15 medium-maturing, and 11 late-maturing plum varieties. The variations in volatile components among these three germplasm types were then compared using multivariate statistical methods. The examination revealed that 138 aromatic components were meticulously identified and quantified, such as 26 aldehydes, 63 esters, 13 ketones, 30 alcohols, and six other compounds. Thirteen main aroma compounds including acetic acid hexyl ester, (Z)-3-hexen-1-ol acetate, hexanal, 1-hexanol, 3-hexenal, butanoic acid butyl ester, (E)-2-hexen-1-ol, hexanoic acid butyl ester, propanoic acid butyl ester, (E)-2-hexenal, L-.alpha.-terpineol, (Z)-2-hexen-1-ol acetate, and 1-butanol were considered dominant. The orthogonal partial least squares discriminant analysis (OPLS-DA) combined with variable importance projection (VIP) results showed that 24 differential aroma compounds were screened out from 38 varieties of plum fruits based on their differences in aroma components, which can be used to distinguish plum fruits at different ripening times. Twenty-four aroma-contributing compounds were identified based on their odor activity values (OAVs). Among these, 14 key aroma components with OAVs ≥ 10 were highlighted. In summary, the aroma compounds of early- and late-maturing plum germplasm exhibited rich diversity, with significant differences in aroma components between plums of varying maturity and germplasm. These differences can serve as indicators for identifying different plum germplasm.

Characteristics of volatile flavor development in aged longissimus lumborum post-ultrasound treatment: 4D proteomics combined with phosphoproteomics analysis

The present study aimed to evaluate the impact of ultrasonic treatment on the development of volatile flavor compounds in beef during postmortem aging and its potential mechanism. Results showed that ultrasound treatment may cause an increase in the total content of unsaturated fatty acids, which could lead to lipid oxidation and potentially result in changes in the flavor development. Additionally, it was also found that ultrasound exacerbated protein oxidation. A total of 141 volatile compounds were obtained by SPME–GC–MS analysis, and 18 differential aroma substances (P < 0.05, VIP > 1) were obtained by orthogonal partial least squares discrimination analysis (OPLS-DA). Five key volatile flavor compounds (hexanal, nonanal, octanal, pentanal, and 1-pentanol) originating from lipid oxidation were identified according to odor activity values (OAVs). The concentration of these compounds was significantly higher in the ultrasonic treatment group compared to the non-ultrasonic group that underwent a 3-day aging process. Nine common differentially expressed proteins (DEPs) were identified through the utilization of proteomics and phosphoproteomics analysis. KEGG pathways showed that selenocompound metabolism, tryptophan metabolism and cysteine and methionine metabolism led to flavor formation during wet aging of beef after ultrasound treatment. This study provided proteomic insights into the flavor of beef aged through sonication and suggested potential links between flavor development and biological processes.

Multi-omics analysis of metabolic differences in rape bee pollen fermented by single and mixed lactic acid bacterial strains

Bee pollen is a nutrient-rich granule, but its hard cell wall limits the absorption of its nutrients by the human body. Fermenting bee pollen with lactic acid bacteria breaks down its cell wall and enhances its metabolite profile, improving nutrient availability. However, the differences in metabolites and cell wall disruption effects between mixed and single-strain fermentation have not been clarified. This study aimed to analyze the metabolites of Lactobacillus plantarum comprehensively fermented bee pollen (LPFP), Lactobacillus acidophilus fermented bee pollen (LAFP), and L. plantarum and L. acidophilus mixed fermented bee pollen (LMFP) using targeted and untargeted metabolomics as well as lipidomics, to explore the key metabolic pathways of LMFP. The results showed that under scanning electron microscopy (SEM), the cell wall of LMFP was more thoroughly disrupted. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) and metabolic pathway enrichment analysis indicated that the degradation of flavonoids in LMFP was reduced, while the contents of total phenols, total flavonoids, rutin, and other polyphenols were significantly increased. Meanwhile, the metabolism of sucrose and amino acids in LMFP was enhanced, which might have contributed to the production of flavor compounds. Additionally, compared to LAFP and LPFP, triglycerides (TG) in LMFP increased by 8.14% and 9.85%, respectively, while the proportion of phosphatidylcholine (PC) decreased by 0.62% and 2.88%. Overall, this study presents a comprehensive metabolic profile of LAFP, LPFP, and LMFP, demonstrating how fermentation strategies affect cell wall disruption and metabolite diversity, laying a foundation for enhancing bee pollen's nutritional and functional properties.

Screening and Predictive Biomarkers for Down Syndrome Through Amniotic Fluid Metabolomics.

Down syndrome (DS) is a congenital disorder caused by the presence of an extra copy of all or part of chromosome 21. It is characterized by significant intellectual disability, distinct facial features, and growth and developmental challenges. The utilization of metabolomics to analyze specific metabolic markers in maternal amniotic fluid may provide innovative tools and screening methods for investigating the early pathophysiology of trisomy 21 at the functional level. Amniotic fluid samples were obtained via amniocentesis from 57 pregnancies with DS and 55 control pregnancies between 173/7 and 240/7weeks of gestation. The targeted metabolomics focused on 34 organic acids, 17 amino acids, and 5 acylcarnitine metabolites. The untargeted metabolomics analysis concentrated on lipid profiles and included 602 metabolites that met quality control standards. Principal Component Analysis, Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and false discovery rate (FDR) adjustments were applied. MetaboAnalystR 5.0 was used to perform the metabolic pathway analysis on the identified differential metabolites. Fifty differential metabolites, including L-glutamine, eight organic acids, and 41 lipids, were significantly altered in DS based on three criteria: VIP > 1 in the OPLS-DA model, FDR-adjusted p-value < 0.05, and |log2FC|>log2(1.5) from a volcano plot of all detected metabolites. An analysis of 212 differential metabolites, selected from both targeted and untargeted approaches (VIP > 1 in the OPLS-DA model and FDR-adjusted p-value < 0.05), revealed significant changes in nine metabolic pathways. Fourteen key metabolites were identified to establish a screening model for DS, achieving an area under the curve of 1.00. Our results underscore the potential of metabolomics approaches in identifying concise and reliable biomarker combinations that demonstrate promising screening performance in DS.

UHPLC-QQQ-MS/MS method for the simultaneous quantification of 18 amino acids in various meats.

Amino acids are an essential source of human protein, and their content and composition are the main factors determining food protein utilization rate. Determining amino acids is essential in the component analysis of food. Therefore, a groundbreaking technique was developed utilizing ultra-high performance liquid chromatography interfaced with a triple quadrupole mass spectrometer (UHPLC-QQQ-MS/MS) for concurrently quantifying 18 amino acids across various types of meat. According to the test results, it can be known that the average content of glutamate (2.03 × 104 ± 3.94 × 103 μg/g in pig feet) was the highest in all meat samples, and the content of aspartate (0.0945 ± 0.0950 μg/g in pork) was the lowest, which was not detected in some samples such as beef and lean meat. Orthogonal partial least-squares discrimination analysis (OPLS-DA) showed: (1) 13 amino acids (arginine, valine, serine, alanine, lysine, glycine, asparagine, methionine, proline, threonine, glutamate, phenylalanine, and leucine, VIP > 1) were used as characteristic amino acids between pork and pig feet; (2) serine, threonine, alanine, histidine, asparagine, and arginine (VIP > 1) were used as signature amino acids in different components of pork (lean meat, fat, and pigskin); (3) asparagine, glutamate, histidine, tyrosine, and valine (VIP > 1) were considered as signature amino acids in different types of meats (pork, mutton, beef, and chicken). This study provides a new UHPLC-QQQ-MS/MS method for the determination of amino acid content in meat and also provides data support for the comprehensive evaluation of the nutritional value of foods containing amino acids.

Metabolomics based on GC-MS combined with chemometrics for geographical discrimination of garlic (Allium sativum L.)

Ensuring the geographic origin of products helps protect consumers against fraud and mislabeling. This study investigates the metabolomic profiles of garlic (Allium sativum L.) samples from Brazil and China using gas chromatography-mass spectrometry (GC-MS). Multivariate analysis Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal PLS-DA (Ortho PLS-DA) revealed clear metabolic distinctions between the two sample groups with high classification accuracy. Brazilian garlic samples showed higher levels of sulfur compounds, such as allyl propyl sulfide, cysteine sulfonic acid, and S-carboxymethyl-L-cysteine, which contribute to its pungency and potential health benefits. Amino acids L-arginine, L-glutamic acid, and L-cysteine were also significantly increased in garlic from Brazil. In contrast, Chinese garlic exhibited higher intensities of sugars (e.g., D-fructose, D-mannose) and essential amino acids (L-tryptophan, L-valine, and L-leucine). These differences in metabolism are the result of distinct metabolic profiles. GC-MS-based untargeted metabolomic and chemometric analysis were able to differentiate the geographical origin of Brazilian and Chinese garlics and remain valuable to food authentication and quality control. Further studies are needed to confirm the biomarkers for origin verification here stated.

Statins have profound effects on the epicardial fat transcriptome

Abstract Background Epicardial adipose tissue (EAT) has direct contact with the myocardium, which enables a common microcirculation allowing bidirectional signaling between the tissues. It is possible that a change in the balance of protective and damaging mediators produced by EAT is a major driver for myocardial dysfunction. Notably, EAT volume and thickness associate with increased pro-inflammatory secretome and with coronary artery disease (CAD). Purpose This aim of this study was to analyze the total transcriptome in EAT and relate putative differences between patient groups to metabolic parameters as well as ongoing medications. Methods Between 2011 and 2016, 44 patients (mean age 68.0 years, 18.2% women) had a biopsy taken from the epicardial fat during coronoray by-pass surgery (CABG, n=36), or during elective aortic valvular replacement due to aortic stenosis (AVR, n=8). RNA from epicardial fat biopsis was extracted and sequenced. Differentially expressed genes were obtained by custom scripts that performed a negative binomial distribution test on effective counts. Multivariate regression analysis with orthogonal partial least-squares-discriminant analysis (OPLS-DA) was applied to extract and interpret the systematic variation in the resolved RNA sequencing profiles. To identify enrichment of biologically relevant functional gene sets, our data were entered in evidence-based network enrichment analysis (EviNet). Results The CABG surgery was performed approximately 7 days after an acute coronary event (75% acute MI), and 44% of them had previously known CAD. None of the AVR cases had significant CAD. At surgery, 97% of the CABG cases were prescribed statins, and 36% of them had statins alredy before the actual admission (long-term statin treatment). 63% of the AVR cases were on statins at admission. The OPLS analysis revealed only one medication with significant outcome, i.e., patients with long-term treatment with statins (n=18) versus patients without (n=26). Long-term statin treatment associated with down-regulated gene expression involved in beta-oxidation and triglyceride biosynthesis whereas gene expression involved in extracellular matrix organization were up-regulated. Gene expression involved in MHC class II presentation and interferon signalling were also reduced in patients with long-term treatment with statisn. A downregulation of genes in the interleukin- and complement cascade genes among CABG patient (versus AVR patients) without long-term statin treatment showed normal levels in statin pretreated patients. Conclusion Long-term statin treatment has a major influence on transcriptional regulation of genes involved in beta-oxidation, triglyceride biosynthesis, extracellular matrix composition, MHC class II presentation and interferon signalling in epicardial adipose tissue. These findings support important metabolic and immune effects of statins beyond the lipid-lowering function.

Authenticating the Geographical Origin of Jingbai Pear in Northern China by Multiple Stable Isotope and Elemental Analysis.

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, δ18OJ, 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.

Quality Evaluation of Kidney Tea Granules From Different Origins Based on TLC, HPLC Fingerprinting, and Quantitative Analysis Combined With Chemical Pattern Recognition.

Kidney tea is an essential herbal medicine. It is widely used to treat conditions such as urinary stones, gallstones, and rheumatoid arthritis. There is currently no standardized or widely accepted research strategy for evaluating the quality of kidney tea granules (KTGs) after granulation. In this study, we aim to establish a comprehensive strategy for evaluating the quality of KTGs produced from different sources of kidney tea. A TLC combined with HPLC method was established to identify the chemical components in KTGs, and HPLC method was used to determine the contents of rosmarinic acid of KTGs. In order to distinguish samples and identify differential components, similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted. TLC and HPLC detection confirmed three chemical components of KTGs, which are rosmarinic acid, caffeic acid, and lithospermic acid. HPLC fingerprint analysis revealed a total of seven common peaks in 15 batches of KTGs. Similarity analysis showed that the similarity of all 15 batches of KTGs was greater than 0.969. The peak areas of the seven common peaks were identified by chemical pattern recognition, and the results showed that most of the KTGs from different origins were clustered together, with small differences between them. The PCA and OPLS-DA results showed that two principal components can represent 82.638% of the common peaks of KTGs, among which peak 5 represents rosmarinic acid, which is the main differential biomarker of KTGs from different regions. Quantitative analysis of rosmarinic acid in KTG samples was performed using HPLC fingerprint conditions and the content of rosmarinic acid in 15 batches of KTGs samples was measured to be between 8.01-14.61 mg/g. This study combines TLC, HPLC, and chemometrics to establish a stable and reliable method that can quickly and effectively identify the components of KTGs, accurately quantify known components in KTGs, and provide reference for the quality evaluation of KTGs.

Decoding wheat contamination through self-assembled whole-cell biosensor combined with linear and non-linear machine learning algorithms

The contamination of mycotoxins is a serious problem around the world. It has detrimental effects on human beings and leads to tremendous economic loss. It is essential to develop a rapid and non-destructive method for contamination recognition particularly for early alarm. In this study, the whole-cell biosensor array was constructed and employed for rapid recognition of wheat contamination by combining with machine learning algorithms. Seven key VOCs were explored through univariate coupling to multivariate analysis of orthogonal partial least squares-discrimination analysis (OPLS-DA) models. The promoters of dnaK, katG, oxyR, soxS obtained from the stress-responsive of key VOCs were fused to the bacterial operon and fabricated on the whole-cell biosensor. The constructed whole-cell biosensor array was consisted with four kinds of sensors and 18 sensor unit. The bioluminescent intensity combined with linear machine learning algorithm of partial least squares discriminant analysis (PLS-DA) and non-linear algorithms of back propagating artificial neural network (BP-ANN) and least square support vector machine (LS-SVM) were employed to establish discrimination models for mold contamination especially for early warning. The Monte-Carlo strategy was performed to generate thirty subsets for modeling to give more reliable results. As a result, the whole-cell biosensor combined with non-linear algorithm of LS-SVM was practicable for detecting mold identification for wheat early-warning with the accuracy of 97.24%. Additionally, this study provides practical and effective methods not only for wheat quality guarantee and supervision but also for other foodstuffs.

Metabolomic signature of sperm in men with obesity-associated asthenozoospermia.

Obese men have a significantly increased risk of developing asthenozoospermia. Sperm motility is directly related to cellular energy supply and metabolic status. Sperm metabolomics research based on Gas chromatography-mass spectrometry (GC-MS) technology can provide useful information for the pathological mechanism, diagnosis, and treatment of obesity-associated asthenozoospermia. Sperm samples were obtained from a healthy control group (n = 49) and patients with obesity-associated asthenozoospermia (n = 40). After the analysis of sperm samples using GC-MS, various multivariate statistical methods such as principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) were conducted. A total of 56 metabolites were identified in the sperm samples. Among them, 19 differential metabolites were found between the two groups. Metabolites such as glutamic acid, fumaric acid, and cysteine were significantly downregulated in the sperm of patients with obesity-associated asthenozoospermia, while metabolites like palmitic acid, stearic acid, and alanine were significantly upregulated. The differential metabolites were enriched in D-glutamine and D-glutamate metabolism; proline, aspartate, and glutamate metabolism; glutathione metabolism and the other metabolic pathways. Obesity may influence the composition of metabolic products in sperm, and metabolomic analysis proves beneficial for the future diagnosis and treatment of obesity-associated asthenozoospermia.

Microencapsulation Efficiency of Carboxymethylcellulose, Gelatin, Maltodextrin, and Acacia for Aroma Preservation in Jasmine Instant Tea.

Enhancing the sensory appeal of jasmine instant tea, particularly its aroma, poses a significant challenge due to the loss of volatile organic compounds during conventional processing. This study introduces a novel approach to address this issue through the application of microencapsulation techniques, aimed at preserving these key aromatic elements. Our investigation focused on the encapsulating agents gelatin, acacia gum, carboxymethylcellulose (CMC), and maltodextrin, chosen for their compatibility with the volatile organic compounds of tea. A statistical analysis was conducted on the analytical results through comprehensive analytical techniques like Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and Variable Importance in Projection (VIP) analysis for microcapsule characterization. The statistical analysis revealed gelatin to be a particularly effective encapsulating medium, preserving an aroma profile more akin to fresh tea. The statistical analysis confirmed the reliability of these findings, highlighting the potential of microencapsulation in refining the quality of jasmine instant tea products. The results of this research suggest that microencapsulation could be instrumental in improving the sensory quality and shelf life of instant tea products, offering new opportunities for product enhancement in the beverage industry.

Rapid Identification of Medicinal Polygonatum Species and Predictive of Polysaccharides Using ATR-FTIR Spectroscopy Combined With Multivariate Analysis.

Medicinal Polygonatum species is a widely used traditional Chinese medicine with high nutritional value, known for its anti-fatigue properties, enhancement of immunity, delays aging, improves sleep, and other health benefits. However, the efficacy of different species varies, making the quality control of medicinal Polygonatum species increasingly important. Polysaccharides are important in medicinal Polygonatum species because of their potential functional properties, such as antioxidation, hypoglycemia, protection of intestinal health, and minimal toxicological effects on human health, as well as high polysaccharide levels. This study developed a qualitative medicinal Polygonatum species model and a polysaccharides predictive model based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) combined with a multivariate analysis approach. ATR-FTIR spectral information of 334 medicinal Polygonatum species samples was collected and the spectral information of different modes was analyzed. The ATR-FTIR spectral differences of three medicinal Polygonatum species were studied by multivariate analysis combined with four spectral preprocessing and three variable selection methods. For the prediction of polysaccharides in Polygonatum kingianum Collett & Hemsl. (PK), we initially determined the actual content of 110 PK polysaccharide samples using the anthrone-sulfuric acid method, then established partial least squares regression (PLSR) and kernel PLSR models in conjunction with attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy. In the visualization analysis, the orthogonal partial least squares-discriminant analysis (OPLS-DA) model based on second-order derivative (SD) preprocessing was most suitable for medicinal Polygonatum species species binary classification, spectral differences between Polygonatum cyrtonema Hua (PC) and other species are evident; in the hard modeling, SD preprocessing improves the accuracy of non-deep learning models for the classification of three medicinal Polygonatum species. In contrast, residual neural network (ResNet) models were the best choice for species identification without preprocessing and variable selection. In addition, the partial least squares regression (PLSR) model and Kernel-PLSR model can quickly predict PK polysaccharides content, among them, the Kernel-PLSR model with SD pretreatment has the best prediction performance, residual prediction deviation (RPD) = 7.2870, Rp = 0.9905. In this study, we employed ATR-FTIR spectroscopy and various treatments to discern different medicinal Polygonatum species. We also evaluated the effects of preprocessing methods and variable selection on the prediction of PK polysaccharides by PLSR and Kernel-PLSR models. Among them, the ResNet model can achieve 100% correct classification of medicinal Polygonatum species without complex spectral preprocessing. Furthermore, the Kernel-PLSR model based on SD-ATR-FTIR spectra had the best performance in polysaccharides prediction. In summary, by integrating ATR-FTIR spectroscopy with multivariate analysis, this research accomplished the classification of medicinal Polygonatum species and the prediction of polysaccharides. The methodology offers the benefits of speed, environmental sustainability, and precision, highlighting its significant potential for practical applications. In future research, on the one hand, it can be further investigated using a portable infrared spectrometer, and on the other hand, infrared spectroscopy can also be applied to the prediction of other chemical components of medicinal Polygonatum species.

Identification of Cervi cornu pantotrichum and its mixed varieties by UHPLC-QTOF-MS and UHPLC-TQS-MS/MS

Sika deer (Cervus nippon Temminck) and red deer (Cervus elaphus Linnaeus) are the two creatural sources of velvet antlers, which are recorded in the Chinese Pharmacopoeia (2020 edition). However, the counterfeits-moose (Alces alces Linnaeus) antlers are selling as genuine products in the market. In order to strengthen antler quality control and market supervision, a UHPLC-QTOF-MS coupled with chemometrics analysis and UHPLC-TQS-MS were developed and applied to distinguish the Velvet Antler of moose from that of sika deer and red deer. Firstly, the QTOF-MS quantized data obtained from samples treated with trypsin were subjected to principal component analysis (PCA) to classify these three kinds of antlers. Then, the potential differential marker peptides were found with orthogonal partial least-squares discriminant analysis (OPLS-DA). Finally, validation of these differential marker peptides was assessed using UHPLC-QTOF-MS, and the results showed that the specific ion pairs (m/z 724.8 → 518.1, m/z 724.8 → 938.3, m/z 732.8 → 526.3, m/z 732.8 → 784.3) were detected in moose deer antler, not in velvet antler of sika deer (Cervus nippon Temminck) and red deer (Cervus elaphus Linnaeus). The methods in the paper and specific ion pairs could be successfully applied to distinguish moose antlers from sika deer antlers and red deer antlers.