Orthogonal Partial Least Squares Research Articles

Untargeted metabolomics using liquid chromatography-high resolution mass spectrometry and chemometrics for analysis of non-halal meats adulteration in beef meat.

The adulteration of raw beef (BMr) with dog meat (DMr) and pork (PMr) becomes a serious problem because it is associated with halal status, quality, and safety of meats. This research aimed to develop an effective authentication method to detect non-halal meats (dog meat and pork) in beef using metabolomics approach. Liquid chromatography-high resolution mass spectrometry (LC-HRMS) using untargeted approach combined with chemometrics was applied for analysis non-halal meats in BMr. The untargeted metabolomics approach successfully identified various metabolites in BMr DMr, PMr, and their mixtures. The discrimination and classification between authentic BMr and those adulterated with DMr and PMr were successfully determined using partial least square-discriminant analysis (PLS-DA) with high accuracy. All BMr samples containing non-halal meats could be differentiated from authentic BMr. A number of discriminating metabolites with potential as biomarkers to discriminate BMr in the mixtures with DMr and PMr could be identified from the analysis of variable importance for projection value. Partial least square (PLS) and orthogonal PLS (OPLS) regression using discriminating metabolites showed high accuracy (R2>0.990) and high precision (both RMSEC and RMSEE <5%) in predicting the concentration of DMr and PMr present in beef indicating that the discriminating metabolites were good predictors. The developed untargeted LC-HRMS metabolomics and chemometrics successfully identified non-halal meats adulteration (DMr and PMr) in beef with high sensitivity up to 0.1% (w/w). A combination of LC-HRMS untargeted metabolomic and chemometrics promises to be an effective analytical technique for halal authenticity testing of meats. This method could be further standardized and proposed as a method for halal authentication of meats.

Influences of molecular composition in dissolved organic matter on its reactivity with chlorine radicals (Cl• and Cl2•−)

Dissolved organic matter (DOM) is a major scavenger of chlorine radicals (e.g., Cl• and Cl2•−) in water. However, there is limited literature addressing the impact of DOM molecular composition on its reactivity towards chlorine radicals. This work focused on discovering the relationships between DOM molecular composition and its reactivity towards Cl• and Cl2•−. The molecular composition of 23 DOM isolates from different sources was characterized using high-resolution mass spectrometry, and their rate constants with Cl• (kDOM-Cl•) and Cl2•− (kDOM-Cl2•−) were determined using laser flash photolysis and competition kinetics. Orthogonal partial least squares (OPLS) analysis model was then used to identify the most correlated and effective predictors of kDOM-Cl• and kDOM-Cl2•−. The kDOM-Cl• positively correlated to higher unsaturation and aromaticity while negatively correlated to the proportion of several N- and S-containing formulas. The kDOM-Cl2•− values showed positive correlation with unsaturation and aromaticity while showing negative correlation with the proportion of CHOPwa formulas. These observations suggest that while unsaturation and aromaticity serve as useful indicators of reactivity with chlorine radicals, non-oxygen heteroatom-containing formulas within the DOM isolates play a pivotal role in governing their interactions with chlorine radicals.

Classification of industrial tapioca starch hydrolysis products based on their Brix and dextrose equivalent values using near-infrared spectroscopy.

Industrial starch hydrolysis allows the production of syrups with varying functionality depending on their Brix value and dextrose equivalent (DE). As the current methods for evaluating these products are labor-intensive and time-consuming, the objective of this study was to investigate the potential of near-infrared (NIR) spectroscopy for classifying the different tapioca starch hydrolysis products. NIR spectra of samples of seven products (n = 410) were recorded in transflectance mode in the 12 000-4000 cm-1 range. Next, orthogonal partial least squares (OPLS) regression models were built to predict the Brix and DE values of the different samples. To classify the different starch hydrolysis products, support vector machines (SVM) were trained using either the raw spectra or latent variables (LVs) obtained from the OPLS models. The best classification accuracy was obtained by the SVM classifier based on the LVs from the OPLS model for DE prediction, resulting in 95% correct classification over all classes. These results show the potential of NIR spectroscopy for classifying tapioca starch hydrolysis products with respect to their functional properties related to the Brix and DE values. © 2024 Society of Chemical Industry.

Greenness-sustainability metrics for assessment smart-chemometric spectrophotometric strategy for evaluation of the combination of six gastric proton-pump inhibitors with two selected impurities

Green analytical approaches are employed for the determination of active pharmaceutical ingredients, in conjunction with their impurities. Smart chemometric spectrophotometric techniques, including orthogonal partial least square (OPLS), variable selection such as genetic algorithm (GA-OPLS), and interval selection (i-OPLS), were utilized. These chemometric models were implemented for assessing six proton-pump inhibitors Omeprazole, Esomeprazole, Lansoprazole, Pantoprazole, Rabeprazole, and Dexlansoprazole along with two selected official impurities, namely 4-Desmethoxy omeprazole impurity and Rabeprazole-impurity B. Experimental design was implemented to separate impurities, in the process of multivariate calibration, a five-level eight-factor calibration design consisting of 25 samples was selected. This design was deliberately selected to guarantee that the components were mutually orthogonal to assess the model's performance and reliability, a separate validation set of 15 samples was constructed. The best-performing of the proposed techniques were identified by considering the least favorable values of the Correlation Coefficient (R ≥ 0.9995), the Root Mean Square Error of Prediction (RMSEP) values between (0.0102–0.5622), and the Relative Error of Prediction (REP) values between (0.2961–1.1917). The proposed and reported methods' greenness-sustainability was quantitatively evaluated, and a comparative study of the greenness profile was established through a spider chart, the National Environmental Method Index tool, advanced and modified NEMI along with the Hexagon tool, and the whiteness qualities of the presented approaches were assessed by implementing the recently adopted Red-Green-Blue paradigm and White Analytical Chemistry tool. These approaches are well-suited for use in quality control laboratories due to their observed acceptance, long-term sustainability, simplicity, and affordability.

Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy.

Jet fuel is the primary fuel used in the aviation industry, and its quality has a direct impact on the safety and operational efficiency of aircraft. The accurate quantitative detection and analysis of various physicochemical property indicators are important for improving and ensuring the quality of jet fuel in the domestic market. This study used near-infrared (NIR) spectroscopy to establish a suitable model for the simultaneous and rapid detection of multiple physicochemical properties in jet fuel. Using more than 40 different sources of jet fuel, a rapid detection model was established by optimizing the spectral processing methods. The measurement models were separately built using the partial least-squares (PLS) and orthogonal PLS algorithms, and the model parameters were optimized. The results show that after the Savitzky-Golay second derivative preprocessing, the PLS model built using the feature spectra selected by the uninformative variable elimination wavelength algorithm achieved the best measurement performance. Compared with the PLS model without preprocessing, the range of the resulting accuracy improvement was at least 15.01%. Under the optimal model parameters, the calibration set regression coefficient (Rc2) of the 11 jet fuel property index models ranged from 0.9102 to 0.9763, with the root-mean-square error of calibration values up to 0.8468 °C (for flash points). The regression coefficient (Rp2) of the validation set ranged from 0.8239 to 0.9557, with the root-mean-square error of prediction values up to 1.1354 °C (for flash points). The ratios of prediction to deviation (RPD) values were all in the range of 1.9-3.0, indicating high accuracy and reliability of the model. The rapid NIR analysis method established in this study enables the simultaneous and rapid detection of multiple physicochemical properties of jet fuel, thereby providing effective technical support for ensuring the quality of jet fuel in the market.

Metabolomics combined with chemometric analysis to identify α-glucosidase inhibitors in Phaleria macrocarpa fruit extracts and its molecular docking simulation

Phaleria macrocarpa is a medicinal plant widely available in Malaysia. The plant parts have been traditionally used as an antidiabetic remedy. This study aimed to identify the putative inhibitors of the α-glucosidase enzyme from P. macrocarpa using a gas chromatography-mass spectrometry (GC–MS)-based metabolomics approach and further subjected them to in silico molecular docking analysis to elucidate the possible mechanism of action. This study assessed the inhibitory potential of P. macrocarpa fruit extracts (aqueous, 20 %, 40 %, 60 %, 80 %, and 100 % ethanol) against the α-glucosidase enzyme using GC–MS and chemometric analysis. Orthogonal partial least squares (OPLS) combined with GC–MS analysis were applied to correlate the inhibition of enzyme activity to the metabolites profile of P. macrocarpa. All the potential inhibitors identified were further docked to the yeast (Saccharomyces cerevisiae) protein crystal structure (PDB3A4A). The generated score scatter plot of OPLS showed a recognizable separation of all the extracts into six different clusters. GC–MS, incorporated with multivariate data analysis techniques, was used to identify significant chemical markers. Methyl α-d-glucopyranoside, squalene, palmitic acid, myo-inositol and isoquinoline metabolites were identified as putative inhibitors against α-glucosidase activity from P. macrocarpa. In conclusion, the GC–MS-based metabolomics approach identified potential chemical markers of P. macrocarpa that could be utilized in the development of herbal based medicine.

Plasma metabolomics identifies S-adenosylmethionine as a biomarker and potential therapeutic target for vascular aging in older adult males

Brachial-ankle pulse wave velocity (baPWV) is a noninvasive index of vascular aging. However, the metabolic profile underlying vascular aging has not yet been fully elucidated. The current study aimed to identify circulating markers of vascular aging as assessed by baPWV and to elucidate its mechanism from a metabolomic perspective in older adults. A total of 60 and 61 Chinese male participants aged ≥80 years were recruited to the metabolome and validation cohorts, respectively. The baPWV of participants was measured using an automatic waveform analyzer. Plasma metabolic profile was investigated using ultra-performance liquid chromatography coupled with triple quadrupole linear ion trap tandem mass spectrometry. Orthogonal partial least squares (OPLS) regression modeling established the association between metabolic profile and baPWV to determine important metabolites predictive of vascular aging. Additionally, an enzyme-linked immunosorbent assay was employed to validate the metabolites in plasma and culture media of vascular smooth muscle cells in vitro. OPLS modeling identified 14 and 22 metabolites inversely and positively associated with baPWV, respectively. These 36 biomarkers were significantly enriched in seven metabolite sets, especially in cysteine and methionine metabolism (p <0.05). Notably, among metabolites involved in cysteine and methionine metabolism, S-adenosylmethionine (SAM) level was inversely related to baPWV, with a significant correlation coefficient in the OPLS model (p <0.05). Furthermore, the relationship between SAM and vascular aging was reconfirmed in an independent cohort and at the cellular level in vitro. SAM was independently associated with baPWV after adjustments for clinical covariates (β = −0.448, p <0.001) in the validation cohort. In summary, plasma metabolomics identified an inverse correlation between SAM and baPWV in older males. SAM has the potential to be a novel biomarker and therapeutic target for vascular aging.

Correlations between microbes with volatile compounds and physicochemical indicators of Cabernet Sauvignon wines fermented with different starters

The use of native yeasts is becoming an effective way of improving wine regional character aromas. In this study, three native Saccharomyces cerevisiae (FML, FMS and JCT) and one commercial S. cerevisiae F15 were used to ferment Cabernet Sauvignon wine, and the differences of microorganisms, volatile compounds and physicochemical indicators were evaluated. The result showed that 20 bacterial genera and 10 fungal genera were identified as dominant genera (relative abundance >1%) among them. A total of 74 volatile compounds were detected, most of which originated from the fermentation process (12 alcohols, 27 esters, 5 acids and 4 ketones). FMS was found providing higher content of aromas (13396.76 μg/L), especially ethyl acetate (1566.04 μg/L), which enhanced the pineapple flavor characteristics of wine. In addition, Spearman's correlation and orthogonal partial least squares (OPLS) analysis indicated that FMS had more core microorganisms (21) compared to FML (13), JCT (11) and F15 (16). Some genera in FMS that were closely correlated with volatile compounds, such as Citrobacter, Promicromonospora, Turicibacter, and Saccharomyces, contribute to the formation of esters and alcohols. Our findings provided beneficial reference for the industrial application of indigenous yeast and the development of distinctive Cabernet Sauvignon wines.

Real-time and non-destructive control of the freshness and viability of live mussels through portable near-infrared spectroscopy

This study introduces the application of a novel, rapid, and non-destructive method employing portable near-infrared (NIR) spectroscopy to assess the freshness and viability of live mussels throughout their shelf-life. NIR spectra ranging from 908 to 1676 nm were collected from 150 Mediterranean mussels (Mytilus galloprovincialis, L.) over a 10-day storage period. Simultaneously, measurements of weight loss, intravalvular liquid pH, and nonprotein nitrogen levels were taken. A predictive model was established by correlating the multivariate information derived from NIR spectral data with days of storage under refrigerated conditions using orthogonal partial least squares regression (OPLSR) analysis. While physical and chemical parameters (except for gradual weight loss due to water leakage) showed no distinct trends throughout storage, the fourth derivative-preprocessed NIR spectra enabled the construction of an OPLSR model that, following cross-validation, exhibited a correlation coefficient of 0.86 and, following external validation with new mussel samples, an average accuracy (root mean square error of prediction) of just 1.3 days. The predictive power of the model was primarily attributed to specific NIR wavelengths associated with key chemical features, including unsaturated fatty acids, nitrogen compounds, water content, glycerol, and ATP-related compounds that, collectively, constituted a distinctive fingerprint for forecasting mussel storage duration. The performance of the tool, along with its environmentally friendly, non-invasive, real-time, and cost-effective characteristics, align with industry control procedures and inspection needs, allowing effective freshness and viability assessment of live mussels across the food supply chain.

HS-SPME-GC-MS Combined with Orthogonal Partial Least Squares Identification to Analyze the Effect of LPL on Yak Milk's Flavor under Different Storage Temperatures and Times.

Flavor is a crucial parameter for assessing the sensory quality of yak milk. However, there is limited information regarding the factors influencing its taste. In this study, the effects of endogenous lipoprotein lipase (LPL) on the volatile flavor components of yak milk under storage conditions of 4 °C, 18 °C and 65 °C were analyzed via headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) combined with orthogonal partial least-squares (OPSL) discrimination, and the reasons for the changes in yak milk flavors were investigated. Combined with the difference in the changes in volatile flavor substance before and after the action of LPL, LPL was found to have a significant effect on the flavor of fresh yak milk. Fresh milk was best kept at 4 °C for 24 h and pasteurized for more than 24 h. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed to characterize the volatile components in yak milk under various treatment conditions. Twelve substances with significant influence on yak milk flavor were identified by measuring their VIP values. Notably, 2-nonanone, heptanal, and ethyl caprylate exhibited OAV values greater than 1, indicating their significant contribution to the flavor of yak milk. Conversely, 4-octanone and 2-heptanone displayed OAV values between 0.1 and 1, showing their important role in modifying the flavor of yak milk. These findings can serve as monitoring indicators for assessing the freshness of yak milk.

Integrated characterization of filler tobacco leaves: HS–SPME–GC–MS, E-nose, and microbiome analysis across different origins

This study delves into the aroma characteristics and microbial composition of filler tobacco leaves (FTLs) sourced from six distinct cigar-growing regions within Yunnan, China, following standardized fermentation. An integrated approach using gas chromatography-mass spectrometry (GC–MS), electronic nose (E-nose), and microbiome analysis was employed for comprehensive profiling. Results derived from Linear Discriminant Analysis (LDA) using E-nose data confirmed the presence of notable variability in flavor substance profiles among the FTLs from six regions. Additionally, GC–MS was used to discern disparities in volatile organic compound (VOC) distribution across FTLs from these regions, identifying 92, 81, 79, 58, 69, and 92 VOCs within each respective sample set. Significantly, 24 VOCs emerged as pivotal determinants contributing to the heterogeneity of flavor profiles among FTLs from diverse origins, as indicated by Variable Importance for the Projection (VIP) analysis. Furthermore, distinctions in free amino acid content and chemical constituents were observed across FTLs. Of noteworthy significance, solanone, isophorone, durene, (-)-alpha-terpineol, and 2,3'-bipyridine exhibited the strongest correlations with microbiome data, with fungal microorganisms exerting a more pronounced influence on metabolites, as elucidated through two-way orthogonal partial least-squares (O2PLS) modeling. These findings provide a theoretical and technical basis for accurately evaluating the synchronization of FTLs in aromas and fermentation processes, and they will enhance the quality of fermented FTLs and foster the growth of the domestic cigar tobacco industry ultimately.Graphical

Metabolomics of Red Wines Aged Traditionally, with Chips or Staves.

Traditionally and alternatively aged wines' odour activity values (OAVs) are investigated to differentiate and highlight the differences between the selected methods. An analysis of the volatile aroma compounds of wines derived from ageing in barrels, oak chips, and staves was performed using stir bar sorptive extraction chromatography-mass spectroscopy (SBSE-GC-MS). The results showed that alcohols, esters, and oak compounds were the main contributors to aroma, and their OAVs were higher in the stave samples after 3 months than in the samples from the other two systems of ageing. Furthermore, wines aged with staves have stronger fruity, spiced, and woody aromas, while samples aged in barrels present more chemistry-driven, floral, caramelly, and creamy aromas. The staves-medium plus toast (SMPT at 3 months > 225) and chips-medium plus toast (CMPT at 3 months > 170) showed the highest levels of aromatic series, suggesting that alternative systems provided more powerful aromas than traditional systems, such as barrels-medium plus toast (BMPT at 3 months > 150). A principal component analysis (PCA), orthogonal partial least squares (OPLS) analysis, and cluster analysis allowed for a clear differentiation to be made between red wines according to ageing systems and ageing times. The odour activity values fingerprint in winemaking is a feasible approach to characterise and distinguish wines. Moreover, OAVs provide important information on the effects of production methods on wine quality and aroma profile.

Metabolomic analyses uncover an inhibitory effect of niclosamide on mitochondrial membrane potential in cholangiocarcinoma cells.

Niclosamide is an oral anthelminthic drug that has been used for treating tapeworm infections. Its mechanism involves the disturbance of mitochondrial membrane potential that in turn inhibits oxidative phosphorylation leading to ATP depletion. To date, niclosamide has been validated as the potent anti-cancer agent against several cancers. However, the molecular mechanisms underlying the effects of niclosamide on the liver fluke Opisthorchis viverrini (Ov)-associated cholangiocarcinoma (CCA) cell functions remain to be elucidated. The aims of this study were to investigate the effects of niclosamide on CCA cell proliferation and on metabolic phenoconversion through the alteration of metabolites associated with mitochondrial function in CCA cell lines. The inhibitory effect of niclosamide on CCA cells was determined using SRB assay. A mitochondrial membrane potential using tetramethylrhodamine, ethyl ester-mitochondrial membrane potential (TMRE-MMP) assay was conducted. Liquid chromatography-mass spectrometry-based metabolomics was employed to investigate the global metabolic changes upon niclosamide treatment. ATP levels were measured using CellTiter-Glo® luminescent cell viability assay. NAD metabolism was examined by the NAD+/NADH ratio. Niclosamide strongly inhibited CCA cell growth and reduced the MMP of CCA cells. An orthogonal partial-least square regression analysis revealed that the effects of niclosamide on suppressing cell viability and MMP of CCA cells were significantly associated with an increase in niacinamide, a precursor in NAD synthesis that may disrupt the electron transport system leading to suppression of NAD+/NADH ratio and ATP depletion. Our findings unravel the mode of action of niclosamide in the energy depletion that could potentially serve as the promising therapeutic strategy for CCA treatment.

Deep learning and targeted metabolomics-based monitoring of chewing insects in tea plants and screening defense compounds.

Tea is an important cash crop that is often consumed by chewing pests, resulting in reduced yields and economic losses. It is important to establish a method to quickly identify the degree of damage to tea plants caused by leaf-eating insects and screen green control compounds. This study was performed through the combination of deep learning and targeted metabolomics, in vitro feeding experiment, enzymic analysis and transient genetic transformation. A small target damage detection model based on YOLOv5 with Transformer Prediction Head (TPH-YOLOv5) algorithm for the tea canopy level was established. Orthogonal partial least squares (OPLS) was used to analyze the correlation between the degree of damage and the phenolic metabolites. A potential defensive compound, (-)-epicatechin-3-O-caffeoate (EC-CA), was screened. In vitro feeding experiments showed that compared with EC and epicatechin gallate, Ectropis grisescens exhibited more significant antifeeding against EC-CA. In vitro enzymatic experiments showed that the hydroxycinnamoyl transferase (CsHCTs) recombinant protein has substrate promiscuity and can catalyze the synthesis of EC-CA. Transient overexpression of CsHCTs in tea leaves effectively reduced the degree of damage to tea leaves. This study provides important reference values and application prospects for the effective monitoring of pests in tea gardens and screening of green chemical control substances.

Greenness and whiteness evaluated triple divisor-continuous wavelet transform spectrophotometric and advanced chemometric methods for the simultaneous determination of four photoinitiators in saliva and intravenous injection solution

Migration of photoinitiators from packaging materials and dental products in saliva presents an extensive real damage, particularly that emerges from packed food and dental products in saliva or from polyethylene packaging of the intravenous solutions which is extremely dangerous to human health and to the environment. In line with this, environmentally evaluated, advanced and reliable triple divisor combined with continuous wavelet transform (CWT) spectrophotometric method and nine chemometric methods were established and validated for the concurrent determination of four photoinitiators: Benzophenone, 4-Hydroxyacetophenone, Benzoin, and Diphenyliodonium hexafluorophosphate in saliva and in intravenous injection solutions. To our knowledge, this is the first spectrophotometric method for the analysis of the cited photoinitiators as well as the primary white method for the examination of those photoinitiators. Also, in this paper, the triple divisor method is combined with continuous wavelet transform for the first time. Triple divisor–CWT peak amplitudes were defined at 260.0, 275.8, 262.2 and 231.2 nm over a linear dynamic range of 0.50–13.00, 0.80–10.00, 1.5–30.00 and 1.00–28.00 μg/mL for Benzophenone, 4-Hydroxyacetophenone, Benzoin and Diphenyliodonium hexafluorophosphate, respectively. The nine chemometric methods incorporate the application of principle component regression (PCR), orthogonal partial least square (OPLS), ridge regression (RR) and artificial neural networks (ANN) methods along with different variable selections including genetic algorithm (GA-PCR, GA-OPLS and GA-RR) and interval selections (Interval-PCR and Interval-OPLS). Regarding the adopted chemometrics models, a multivariate multifactor experimental design based on the calibration curve range of the studied photoinitiators was considered to construct a calibration set consisting of eighteen mixtures while using seven mixtures for the validation set. The proposed methods were demonstrated to be sensitive, accurate, precise and reproducible throughout the validation process. The acquired results revealed that the proposed spectrophotometric and chemometric approaches could be effectively applied for the regular analysis of the cited photoinitiators in biological fluids as saliva and in intravenous solutions. The greenness of the proposed methods was assessed using two green metric tools enclosing Analytical Eco-scale & Analytical Greenness Assessment (AGREE), and a White Analytical Chemistry tool (WAC).

Experimental study on changes in metabolic mechanism of papillary thyroid carcinoma complicated with Hashimoto’s thyroiditis

BackgroundWhether the mechanism of thyroid papillary carcinoma (PTC) is the same in patients with a Hashimoto's thyroiditis (HT) background as compared with patients with a normal background remains a highly debated and controversial issue. In this study, we aimed to analyze the differences and similarities of the metabolic mechanism of PTC in normal and HT background, and to explore the relationship between HT and PTC. MethodsThe ultra performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF/MS) technology was used to analyze 61 PTC patient tissues (31 HT background and 30 normal tissue (NC) background). Potential biomarkers were screened from principal component analysis (PCA) to orthogonal partial least square (OPLS) discriminant analysis. HMDB was searched to identify potential differential metabolites and final metabolic pathway analysis was performed by MetaboAnalyst 5.0. We analyzed the differential metabolites diagnostic accuracy through receiver operating characteristic (ROC) curves analysis. ResultsSeven different metabolites were screened from HT group and NC group, including arginine, glutamic acid, cysteine, citric acid, malic acid, uracil and taurine. Logistic regression model combined with ROC analysis of these 7 biomarkers had good discriminability for PTC (area under operating characteristic curve of HT group and NC group were 0.867 and 0.973, respectively). The HT group had specific metabolic pathways, including aminoacyl-tRNA biosynthesis, glycine, serine and threonine metabolism. ConclusionsThe metabolic profiles of the NC and HT groups had important similarities and differences in PTC. The correlation of PTC with HT may be related to aminoacyl-tRNA biosynthesis, serine and threonine metabolism.

Exploring the link between microbial community structure and flavour compounds of traditional fermented yak milk in Gannan region

Traditional fermented yak milk has immense cultural and nutritional significance for Tibetan herders. In this study, we investigated the microbial community structure and flavour compounds in traditional fermented yak milk from three distinct regions of Gannan to explore the relationship between microbial composition and flavour compounds. The findings revealed significant variations in flavour compounds and the bacterial microbiota among the samples from the three regions. The dominant species identified in fermented yak milk were Streptococcus salivarius subsp. Thermophilus, Lactobacillus helveticus, and Kluyveromyces marxianus. Bidirectional orthogonal partial least-squares (O2PLS) analysis highlighted five bacterial genera and three fungal genera as contributors to the production of flavour-related compounds. Furthermore, regression analysis revealed that the Lactobacillus and Streptococcus genera were associated with the production of 2,3-pentanedione as well as eight predicted KEGG pathways. These findings provide valuable insights into the intricate relationship between flavour compounds and microbiota in traditional fermented yak milk.

Associations of High-Density Lipoprotein Functionality with Coronary Plaque Characteristics in Diabetic Patients with Coronary Artery Disease: Integrated Backscatter Intravascular Ultrasound Analysis.

High-density lipoprotein (HDL) functionality has been reported to be associated with coronary artery disease (CAD). However, little is known about the impact of HDL functionality on coronary atherosclerosis. Thirty-eight type 2 diabetic patients with CAD who underwent percutaneous coronary intervention were examined. Coronary atheroma burden and plaque composition of the culprit lesions were assessed using conventional gray-scale and integrated backscatter intravascular ultrasound. HDL-mediated cholesterol efflux capacity (HDL-CEC) and HDL antioxidant capacity, estimated as HDL inflammatory index (HII), were examined. The associations between HDL functionality and coronary plaques were analyzed using multivariate data analysis, including principal components analysis and orthogonal partial least squares (OPLS) models. Percent atheroma volume was correlated with HDL-CEC (r = 0.34, p = 0.04) but not with HII (p = 0.65). The OPLS model demonstrated that the percentage lipid volume was significantly associated with HDL functionality [coefficient (95% confidence interval); HDL-CEC: -0.26 (-0.49, -0.04); HII: 0.34 (0.08, 2.60), respectively]. HII exhibited the highest variable importance in projection score, indicating the greatest contribution. HDL functionality was associated with coronary plaque composition, a key component of plaque vulnerability. Our findings highlight the potential importance of HDL functionality for coronary plaque stabilization.

Aroma characteristics of flaxseed milk via GC–MS-O and odor activity value calculation: Imparts and selection of different flaxseed varieties

Currently, the effect of varieties on the flavor and stability of flaxseed milk remains unknown. Therefore, this study was conducted to investigate the effects of different varieties on the stability, sensory, and aromas of flaxseed milk. 51 volatile compounds were identified in flaxseed milk using Stir Bar Sorptive Extraction-Gas Chromatography-Olfactometry-Mass Spectrometry (SBSE-GC-O-MS). Among them, 1-octen-3-ol, 2-methoxy-4-vinylphenol, and 2,3,5-trimethylpyrazine contributed higher relative odor active values (ROAV), resulted in the fruity, roasted, sweet, and cucumber in flaxseed milk. Isovaleraldehyde (green notes) was not detected in XHZM. However, other compounds such as 1-nonanol (floral), γ-nonanolactone and γ-octanoic lactone (coconut milk) had higher concentrations, causing a better sensory evaluation. Additionally, its stability was relatively good. The orthogonal partial least-squares regression (OPLS) model and VIP values showed that eight compounds were responsible for the sensory differences from different varietals. The study provided references to selection and understanding flavor composition basis of flaxseed milk.

Analysis of Pork in Beef Sausages Using LC-Orbitrap HRMS Untargeted Metabolomics Combined with Chemometrics for Halal Authentication Study.

Beef sausage (BS) is one of the most favored meat products due to its nutrition and good taste. However, for economic purposes, BS is often adulterated with pork by unethical players. Pork consumption is strictly prohibited for religions including Islam and Judaism. Therefore, advanced detection methods are highly required to warrant the halal authenticity of BS. This research aimed to develop a liquid chromatography-high-resolution mass spectrometry (LC-HRMS) method to determine the halal authenticity of BS using an untargeted metabolomics approach. LC-HRMS was capable of detecting various metabolites in BS and BS containing pork. The presence of pork in BS could be differentiated using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) with high accuracy. PLS-DA perfectly classified authentic BS and BS containing pork in all concentration levels of pork with R2X = (0.821), R2Y(= 0.984), and Q2 = (0.795). The level of pork in BS was successfully predicted through partial least squares (PLS) and orthogonal PLS (OPLS) chemometrics. Both models gave high R2 (>0.99) actual and predicted values as well as few errors, indicating good accuracy and precision. Identification of discriminating metabolites' potential as biomarker candidates through variable importance for projections (VIP) value revealed metabolites of 2-arachidonyl-sn-glycero-3-phosphoethanolamine, 3-hydroxyoctanoylcarnitine, 8Z,11Z,14Z-eicosatrienoic acid, D-(+)-galactose, oleamide, 3-hydroxyhexadecanoylcarnitine, arachidonic acid, and α-eleostearic acid as good indicators to detect pork. It can be concluded that LC-HRMS metabolomics combined with PCA, PLS-DA, PLS, and OPLS was successfully used to detect pork adulteration in beef sausages. The results imply that LC-HRMS untargeted metabolomics in combination with chemometrics is a promising alternative as an analytical technique to detect pork in sausage products. Further analysis of larger samples is required to warrant the reproducibility.