Partial Least Squares Discriminant Analysis Research Articles

TLR4 as a Potential Target of Me-PFOSA-AcOH Leading to Cardiovascular Diseases: Evidence from NHANES 2013-2018 and Molecular Docking.

Concerns have been raised regarding the effects of perfluoroalkyl substance (PFAS) exposure on cardiovascular diseases (CVD), but clear evidence linking PFAS exposure to CVD is lacking, and the mechanism remains unclear. To study the association between PFASs and CVD in U.S. population, and to reveal the mechanism of PFASs' effects on CVD. To assess the relationships between individual blood serum PFAS levels and the risk of total CVD or its subtypes, multivariable logistic regression analysis and partial least squares discriminant analysis (PLS-DA) were conducted on all participants or subgroups among 3391 adults from the National Health and Nutrition Examination Survey (NHANES). The SuperPred and GeneCards databases were utilized to identify potential targets related to PFAS and CVD, respectively. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of intersection genes were performed using Metascape. Protein interaction networks were generated, and core targets were identified with STRING. Molecular docking was achieved using Autodock Vina 1.1.2. There was a positive association between Me-PFOSA-AcOH and CVD (OR = 1.28, p = 0.022), especially coronary heart disease (CHD) (OR = 1.47, p = 0.007) and heart attack (OR = 1.58, p < 0.001) after adjusting for all potential covariates. Me-PFOSA-AcOH contributed the most to distinguishing between individuals in terms of CVD and non-CVD. Significant moderating effects for Me-PFOSA-AcOH were observed in the subgroup analysis stratified by sex, ethnicity, education level, PIR, BMI, smoking status, physical activity, and hypertension (p < 0.05). The potential intersection targets were mainly enriched in CVD-related pathways, including the inflammatory response, neuroactive ligand-receptor interaction, MAPK signaling pathway, and arachidonic acid metabolism. TLR4 was identified as the core target for the effects of Me-PFOSA-AcOH on CVD. Molecular docking results revealed that the binding energy of Me-PFOSA-AcOH to the TLR4-MD-2 complex was -7.2 kcal/mol, suggesting that Me-PFOSA-AcOH binds well to the TLR4-MD-2 complex. Me-PFOSA-AcOH exposure was significantly associated with CVD. Network toxicology and molecular docking uncovered novel molecular targets, such as TLR4, and identified the inflammatory and metabolic mechanisms underlying Me-PFOSA-AcOH-induced CVD.

Geographical origin identification of cinnamon using HPLC-DAD fingerprints and chemometrics

The commercial value of cinnamon depends largely on its geographical origin. This study proposed high-performance liquid chromatography-diode array detection (HPLC-DAD) fingerprints combined with chemometrics to identify geographical origins of cinnamons. Alternating trilinear decomposition (ATLD) algorithm was applied to extract meaningful chemical information from the HPLC fingerprint data. Then, non-targeted HPLC fingerprints and chemical component information were used as chemical descriptors to distinguish geographical origins using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA)/N-way partial least squares-discriminant analysis (NPLS-DA). Satisfactory classification results were acquired for each discriminant model and the correct classification rates (CCRs) of training and test sets were 100%. Moreover, four characteristic variables were screened by variable importance in projection (VIP) method, three of which were reasonably identified as cinnamic acid, cinnamaldehyde and 2-methoxycinnamaldehyde, which can be regarded as potential markers to distinguish cinnamon from different geographical origins. The results indicated that HPLC-DAD fingerprints combined with chemometrics can be a promising means to identify the geographical origins and screen potential markers of cinnamon samples, especially in the absence of high-cost and sophisticated analytical instrumentals (e.g., high-resolution mass spectrometry and nuclear magnetic resonance spectroscopy). This work can help to prevent cinnamon fraud practices and maintain the normal order of the cinnamon market.

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

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

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

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

Polyphenolic profiling of coffee beverages by Liquid Chromatography-High-Resolution mass Spectrometry for classification and characterization

The importance of monitoring the presence of bioactive compounds as food attributes for sample classification and characterization is increasing. In this study, targeted Liquid Chromatography coupled with High-Resolution Mass Spectrometry (LC-HRMS) was employed to analyze the chemical profile of polyphenolic compounds as the source of information for the characterization and classification of 306 commercial coffee samples. Coffee holds a distinguished position as one of the most widely popular beverages globally but also one of the most easily adulterated. Regrettably, in recent times, instances of coffee adulteration have been on the rise. Consequently, implementing rigorous quality control measures for coffee becomes imperative to guarantee its quality. The results obtained in this work confirm that the proposed chemical profiles serve as excellent descriptors for sample characterization and classification through the implementation of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), achieving classification rates higher than 83.3% in PLS-DA validation. Moreover, the proposed LC-HRMS polyphenolic approach was employed to identify and measure adulteration levels in coffee samples using partial least squares (PLS) regression with prediction errors below 7.8%.

Non-Targeted Metabolomics of Serum Reveals Biomarkers Associated with Body Weight in Wumeng Black-Bone Chickens.

Growth performance is an important economic trait of broilers but the related serum metabolomics remains unclear. In this study, we utilized non-targeted metabolomics using ultra-high-performance liquid phase tandem mass spectrometry (UHPLC-MS/MS) to establish metabolite profiling in the serum of Chinese Wumeng black-bone chickens. The biomarker metabolites in serum associated with growth performance of chickens were identified by comparing the serum metabolome differences between chickens that significantly differed in their weights at 160 days of age when fed identical diets. A total of 766 metabolites were identified including 13 differential metabolite classes such as lipids and lipid-like molecules, organic acids and their derivatives, and organoheterocyclic compounds. The results of difference analysis using a partial least squares discriminant analysis (PLS-DA) model indicated that the low-body-weight group could be differentiated based on inflammatory markers including prostaglandin a2, kynurenic acid and fatty acid esters of hydroxy fatty acids (FAHFA), and inflammation-related metabolic pathways including tryptophan and arachidonic acid metabolism. In contrast, the sera of high-body-weight chickens were enriched for riboflavin and 2-isopropylmalic acid and for metabolic pathways including riboflavin metabolism, acetyl group transfer into mitochondria, and the tricarboxylic acid (TCA) cycle. These results provide new insights into the practical application of improving the growth performance of local chickens.

Authentication of porcine, bovine, and fish gelatins based on quantitative profile of amino acid and chemometric analysis

Gelatin is a biological macromolecule derived from the partial hydrolysis of collagen protein. This paper describes a sensitive and rapid method for the detection of gelatin sources based on the composition of amino acids. Hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry (HILIC-MS/MS) and chemometric tools such as principle component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used for the analysis and spectral classification, respectively. Twenty amino acids are identified and quantified with the limit-of-detection (LOD) of 0.03–2.62 μg/mL and limit-of-quantification (LOQ) of 0.10–7.93 μg/mL, a precision of 0.19–11.21% and 0.03–13.62% (%RSD) for intra and inter-day, respectively, and a total recovery of 85.1–107.6%. Porcine gelatin showed correlation with glycine, proline, hydroxyproline, tyrosine glutamine, and glutamic acid; Bovine gelatin was correlated with lysine, leucine, and isoleucine histidine, phenylalanine, and alanine, and fish gelatin showed correlation with methionine, threonine, serine arginine and cysteine in PCA analysis. Verification of the developed method was confirmed by using different commercial and laboratory prepared gelatin products containing gelatin and the samples were successfully categorized into their respective sources.

Untargeted authentication of fruit juices based on electrochemical fingerprints combined with chemometrics. Adulteration of orange juice as case of study

This work presents a novel strategy for authentication of fruit juices. The methodology is based on an electrochemical method combined with chemometrics. In addition, the case of orange juice adulteration with grapefruit juices was studied using this methodology. The electrochemical fingerprint of different juices showed the influence from different polyphenols according to the type of fruit. First, Principal Component Analysis (PCA) was able to differentiate clusters of different juices, being the higher group distance within apple, orange, and grape juices samples. On the other hand, partial least-squares – discriminant analysis (PLS-DA) and PCA-linear discriminant analysis (LDA) were used as classification methods, obtaining better results for orange and apple models. Furthermore, good results were obtained for the authentication of orange juices, compared to other juices, with an error rate of 0.04 for cross-validation. In the case of adulteration of orange juice was detected using PLS-DA at an adulteration level as low as 1%. Regression vectors for all models highlighted identifiable potential values that could be related to the main polyphenols in each type of fruit. This electrochemical method is rapid, low-cost, and compatible with on-site analysis compared to other laborious analysis described in the literature.

Reliability of Serum-Derived Connectome Indicators in Identifying Cirrhosis.

Patients with cirrhosis face a heightened risk of complications, underscoring the importance of identification. We have developed a Connectome strategy that combines metabolites with peptide spectral matching (PSM) in proteomics to integrate metabolomics and proteomics, identifying specific metabolites bound to blood proteins in cirrhosis using open search proteomics methods. Analysis methods including Partial Least Squares Discriminant Analysis (PLS-DA), Uniform Manifold Approximation and Projection (UMAP), and hierarchical clustering were used to distinguish significant differences among the Cirrhosis group, Chronic Hepatitis B (CHB) group, and Healthy group. In this study, we identified 81 cirrhosis-associated connectomes and established an effective model distinctly distinguishing cirrhosis from chronic hepatitis B and healthy samples, confirmed by PLS-DA, hierarchical clustering analysis, and UMAP analysis, and further validated using six new cirrhosis samples. We established a Unified Indicator for Identifying cirrhosis, including tyrosine, Unnamed_189.2, thiazolidine, etc., which not only enables accurate identification of cirrhosis groups but was also further validated using six new cirrhosis samples and extensively supported by other cirrhosis research data (PXD035024). Our study reveals that characteristic cirrhosis connectomes can reliably distinguish cirrhosis from CHB and healthy groups. The established unified cirrhotic indicator facilitates the identification of cirrhosis cases in both this study and additional research data.

Classification of Pesticide Residues in Sorghum Based on Hyperspectral and Gradient Boosting Decision Trees

ABSTRACTTo address the challenges posed by chemical methods for detecting pesticide residues in sorghum, such as complicated sample preparation and prolonged detection periods, this study presents a rapid and nondestructive detection approach based on hyperspectral imaging (HSI) technology. A group of sorghum without pesticide residues and three groups uniformly sprayed with pesticides were used in this study. Firstly, support vector machine (SVM) classification models were built using spectral data preprocessed with Savitzky–Golay (SG), discrete wavelet transform (DWT), and standard normal variate (SNV) methods, respectively, and SNV was determined to be the best preprocessing method. Secondly, the gradient boosting decision tree (GBDT) algorithm, principal component analysis (PCA), and the successive projections algorithm (SPA) were respectively used to extract feature wavelengths. Pesticide residue identification models based on full and feature wavelengths were then respectively established using backpropagation neural network (BPNN), SVM, and partial least squares discriminant analysis (PLS‐DA). The results show that the BPNN model developed using the feature wavelengths obtained from GBDT was the best for identification of pesticide residues, with an accuracy of 97.8% for both the training and testing sets. Finally, visualization of pesticide residue species in sorghum was achieved using the optimal model. This study demonstrates that utilizing HSI in conjunction with the GBDT‐BPNN model is an effective, rapid, and nondestructive method for identifying pesticide residues in sorghum.

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

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

1H NMR chemometrics and mechanistic insights on the thermal degradation of coconut oil

AbstractThe re‐use and prolonged heating of cooking oils is a common practice in preparing deep‐fried foods. However, this could be unsafe and pose a health threat to consumers. This work investigates the thermal degradation of edible coconut oil under prolonged heating using chemometrics analysis of proton nuclear magnetic resonance (1H NMR) data. Coconut oil samples were heated continuously without food matrix at three different temperatures (150, 175, and 200°C) for 12 h and the 1H NMR spectra were collected every hour. NMR data revealed the formation of aldehyde oxidation products. Partial least squares discriminant analysis (PLS‐DA) discriminated the samples based on temperature and heating time. The Variable Importance Projection revealed the discriminating peaks highlighting the spectral feature due to hydroperoxides and aldehydes in the oil degradation. A probe into the 1H NMR of the degradation products of saturated coconut oil confirmed the formation of a ketone as evidenced by a CH2 triplet in the chemical shift region of protons alpha to a ketone. The results showed that the thermal degradation of coconut oil was influenced by temperature, time, moisture, and oxygen in the air which can contribute to the advancement in the control and assessment of coconut oil quality.

Volatile organic compound profile for the search of rejection markers in protein baits used as Vespa velutina control method

The invasive species Vespa velutina nigrithorax (Lepeletier 1836), commonly known as the Asian hornet, constitutes an economic and environmental threat, as it is a predator of fruits and insects, especially Apis mellifera (Linnaeus, 1758) (honey bees).Different methods have been developed for its control. Among of them, biocidal protein baits are included, which have a limited time of use. Oxidation of their components, enzymatic activity and microorganisms lead to the formation of spoilage compounds.The aim of this work was to determine the optimal conditions for storing and preserving the baits without losing efficacy. Furthermore, a search of rejection markers will be carried out based on field studies results in the apiary on the acceptance and rejection of the baits by hornets under different storage conditions.Volatile organic compound profiles of these baits were obtained by solid phase microextraction/gas chromatography/mass spectrometry (SPME-GC–MS). The data were processed using different statistical and chemometric tools.The identification of potential rejection markers belonging to the families of ketones, esters, alcohols and amines was carried out by partial least squares discriminant analysis (PLS-DA). The four markers with the highest VIP scores were 2-butanone, 2-heptanone, 1-butanol, 3-methyl and 1-butanol, 3-methyl, acetate.

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

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

Online detection of moldy apple core based on diameter and SSC features

Moldy core is a common internal disease in apples, with current visible/near-infrared (Vis/NIR) spectroscopy-based detection methods often suffering from inaccuracies due to variations in fruit diameter and soluble solids content (SSC). To address this issue, an online detection system was developed that integrates spectral corrections for both diameter and SSC information. The spectra were first corrected using a hyperbolic sine function based on fruit diameter, followed by a second correction utilizing the spectral features of SSC through the Variational Mode Decomposition (VMD) algorithm. This two-step correction process effectively mitigated the influence of diameter and SSC variations on the spectral data. A Partial Least Squares Discriminant Analysis (PLS-DA) model was subsequently constructed using the corrected spectra and moldy core labels. Compared to the uncorrected model, the improved model exhibited substantial enhancements in accuracy, recall, and precision, achieving 94.44%, 92.59%, and 96.15%, respectively. Additionally, online validation with independent samples demonstrated an accuracy of 88.33%, highlighting the system's stability and robustness for efficient online detection of apple moldy core.

Non-Invasive Endometrial Cancer Screening through Urinary Fluorescent Metabolome Profile Monitoring and Machine Learning Algorithms.

Endometrial cancer is becoming increasingly common, highlighting the need for improved diagnostic methods that are both effective and non-invasive. This study investigates the use of urinary fluorescence spectroscopy as a potential diagnostic tool for endometrial cancer. Urine samples were collected from endometrial cancer patients (n = 77), patients with benign uterine tumors (n = 23), and control gynecological patients attending regular checkups or follow-ups (n = 96). These samples were analyzed using synchronous fluorescence spectroscopy to measure the total fluorescent metabolome profile, and specific fluorescence ratios were created to differentiate between control, benign, and malignant samples. These spectral markers demonstrated potential clinical applicability with AUC as high as 80%. Partial Least Squares Discriminant Analysis (PLS-DA) was employed to reduce data dimensionality and enhance class separation. Additionally, machine learning models, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD), were utilized to distinguish between controls and endometrial cancer patients. PLS-DA achieved an overall accuracy of 79% and an AUC of 90%. These promising results indicate that urinary fluorescence spectroscopy, combined with advanced machine learning models, has the potential to revolutionize endometrial cancer diagnostics, offering a rapid, accurate, and non-invasive alternative to current methods.

Acupuncture at the Zusanli acupoint can reduce the inflammatory response in AIA mice by regulating the arachidonic acid and pentose phosphate pathways

BackgroundRheumatoid arthritis (RA) is an autoimmune disease characterized by synovitis, which can lead to joint deformity. Acupuncture treatment stimulates specific acupoints to adjust qi and blood function, relieving joint inflammation and pain. MethodsUltra-high performance liquid chromatography-mass spectrometry (UPLC-QTOF-MS) was utilized for non-targeted metabolomics analysis of plasma samples from the blank group, Adjuvant-Induced Arthritis (AIA) model mice model mice group, and acupuncture group. Metabolite hierarchical clustering analysis, multivariate statistical analysis, standardized processing, principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and other methods were employed to identify targeted metabolites affected by acupuncture treatment in AIA mice. The related metabolic pathways were analyzed using KEGG pathway. ResultsHistopathological results demonstrated that acupuncture at Zusanli point (ST 36) significantly improved the inflammatory response in AIA mice. The PCA score plot indicated relatively close sample clustering within each group with significant differences observed between the four groups, confirming successful establishment of the AIA animal model with metabolic disorders occurring. Acupuncture treatment effectively corrected these metabolic disorders. Plasma metabolomics identified a total of 10 differential metabolites primarily associated with arachidonic acid and pentose phosphate metabolic pathways. ConclusionsAcupuncture at ST36 can significantly improve the inflammatory response in AIA mice through modulation of arachidonic acid and pentose phosphate metabolic pathways.

PSLBI-11 Relevance of mitochondrial-mediated apoptotic processes in color and oxidative stabilities of five different beef muscles

Abstract Fresh meat color stands as one of the most critical quality indicators influencing consumer perception of meat freshness and purchase decision. Variation in color and color stability among different muscle types exists due to differences in chemical compositions, including lipids and metabolites, and other redox stability-related biochemical properties. Understanding biochemical mechanisms governing color stability in different muscles will offer insights for the industry to develop practical strategies aimed at reducing food waste attributed to discoloration. The objective of the current study was to identify metabolites and lipid compounds potentially linked to oxidative stabilities and relevant biochemical properties across different bovine muscles. Five different muscles [longissimus lumborum (LL), gluteus medius (GM), biceps femoris (BF), semitendinosus (ST), and infraspinatus (IF)] were collected from 16 beef carcasses (USDA Choice) at 2-d postmortem. Multiple cuts were made from each muscle for meat color measurements including instrumental and sensory color evaluations during display, and biochemical attributes, including total reducing activity (TRA), mitochondria-swelling, mitochondrial-lipid oxidation, cytochrome c redox ability (CytC). For the lipidome and metabolome analyses, beef samples were extracted and profiled using non-targeted UPLC-MS analyses. Data analysis was conducted with Metaboanalyst 6.0. The correlation analysis was conduct with Pearson’s correlation and considered the significance level at P &amp;lt; 0.05. In brief, LL and ST maintained greater color stability, redox stability, and lipid/protein oxidative stabilities compared with BF and GM, with IF showing the least (P &amp;lt; 0.05). Also, IF had the greatest levels of mitochondrial-swelling, mitochondrial-lipid oxidation, and oxidized CytC compared with LL (P &amp;lt; 0.05). The ANOVA results found 1,410 significant features in the metabolome analysis and 531 in the lipidome analysis of beef samples. Utilizing partial least square-discriminant analysis (PLS-DA), scatter plots grouped the five muscle types into three distinct clusters: LL and ST, BF and GM, and IF. Pro-apoptotic factors such as phosphatidylserine and lysophosphatidylserine were greater in IF compared with LL and ST (P &amp;lt; 0.05). From the metabolome results, anti-apoptotic factors like creatine and glutathione were increased in LL and ST compared with IF, whereas pro-apoptotic spermidine and adenosine monophosphate (AMP) were greatest in IF (P &amp;lt; 0.05). CIE a* showed a positive correlation with phospholipids and mitochondria-swelling (P &amp;lt; 0.05). Chroma exhibited a positive correlation with TRA, creatine, cyclic AMP, and GMP (P &amp;lt; 0.05). The results suggest that variations in meat color stability among the five beef muscles were likely attributed to differences in the abundance of lipids and metabolites associated with redox and oxidative stabilities, as well as mitochondrial-mediated apoptotic pathways. Specifically, the varying abundance of pro-/anti-apoptotic factors across muscles indicates potential mechanistic biochemical linkages between the apoptosis and meat color. Further research is warranted to investigate the effects of extended aging periods on changes in apoptotic features and color stability of beef muscles.

PSV-2 Rumen fluid metabolomics of dairy cows fed combinations of essential oil blend, onion peel extract and prebiotics using the RUSITEC system

Abstract Dairy products have a crucial role in the diets of over 6 billion people worldwide, providing essential nutrients such as energy, high-quality protein, and micronutrients. With dairy cows accounting for nearly 81% of global milk production, strategies to increase milk production sustainably are vital, especially with the U.S. being the second-largest producer. Essential oil blends (EOB), onion peel extract (OPE), and prebiotics such as mannan oligosaccharides (MOS) and galacto-oligosaccharides (GOS) offer promising avenues to enhance bovine milk production. These additives have the potential to modify rumen ecology and fermentation activity, while also mitigating greenhouse gas emissions in dairy cows. Therefore, this study investigated the effects of combining EOB, OPE, GOS, and/or MOS on specific rumen metabolites and metabolic pathways using the rumen simulation technique (RUSITEC) system. Rumen-cannulated non-lactating Holstein Friesian cows (n = 3) were used as rumen fluid donors. Total mixed ration (TMR) consisted of corn silage (60%), alfalfa hay (20%), and concentrate (20%). Using a completely randomized design, 16 fermentation vessels were divided into 4 groups with 4 replicates each over a 9-d period. The treatments included a control group (CON; TMR only), GEO (TMR+GOS+EOB+OPE), MEO (TMR+MOS+EOB+OPE), and OLEO (TMR+OLG+EOB+OPE). The EOB comprised of garlic, lemongrass, cumin, lavender, and nutmeg (4:2:2:1:1), while OLG contained GOS and MOS in equal proportions. EOB was added at 10 µL/g, and OPE, GOS, MOS, and OLG were included at 30 mg/g TMR. Rumen effluents were collected over 5 d for quantitative untargeted metabolome analysis. Analysis of rumen effluents using liquid chromatography-mass spectrometry and MetaboAnalyst 5.0 revealed 495 detected and identified metabolites. Volcano plot analysis showed no significant differences (P ≥ 0.05) in metabolite abundance between the CON and MEO, GEO, or OLEO treatments. Partial least squares-discriminant analysis (PLS-DA) also indicated no clear separation between the CON and treatment groups, suggesting no significant alteration in the metabolome. Compared with CON, the relative concentrations of dopamine 4-O-glucuronide, proline, and histamine were less while pyrocatechol was greater in MEO group. Isomer 1 of 2-hydroxyhepta-2,4-dienedioic acid, histamine, and dopamine 4-O-glucuronide were less while 2-aminomuconic acid and isomer 1 of hulupinic acid were greater in the GEO group. Histamine was less and hydroxypyridine was greater in the OLEO group. Phosphonate and phosphinate metabolism tended to be upregulated in OLEO. Meanwhile, metabolic pathways related to amino acids, vitamins, nitrogen metabolism, and primary bile acid biosynthesis remained unaltered by MEO, GEO and OLEO treatments. In summary, the study demonstrated that the inclusion of MEO, GEO, and OLEO had no adverse effects on rumen metabolites. Future research should focus on combinations of nutraceuticals that could positively impact nutrient metabolism and production performance of dairy cows.

512 Divergence of the sow vaginal microbiome based on fertility status

Abstract The need for alternative biomarkers of reproductive potential in the pork industry is apparent as the current selection approach for sow fertility is not keeping up with the improvement of other production-related traits. To date, the vaginal microbiome has been overlooked as a source of potential biomarkers of sow fertility status. We aimed to elucidate a possible divergence of the vaginal microbiome between sows of differing fertility status and identify possible microbial biomarkers that classify the fertility potential of a sow. Microbial DNA was extracted from vaginal microbiome samples originating from highly reproductive sows (HRP, n = 41) with the number of piglets born alive ≥ 13 or infertile sows (INF, n = 22), that failed to become pregnant after two rounds of consecutive artificial insemination. Our 16S rRNA sequencing results revealed significant (P &amp;lt; 0.05) beta diversity divergence at the family and genus taxonomic levels between HRP and INF vaginal communities. INF vaginal communities displayed significantly greater (P &amp;lt; 0.05) alpha diversity at both the family and genus levels and had a significantly (P &amp;lt; 0.05) greater number of unique OTUs identified at the genus level. The composition of the vaginal microbiome diverges between HRP and INF sows, with INF sows experiencing a disruption of their communities via the colonization of opportunistic pathogens reducing the abundance of normal microbiota seen in HRP. The genera Streptococcus, Lachnospiraceae XPB1014 group, Staphylococcus and Treponema were selected to test as potential biomarkers due to their univariate significance, contribution to partial least squares discriminant analysis (PLS-DA) and selection in a recursive feature elimination model (RFE). The resultant support-vector machine model was diagnostic (achieving an ROC-AUC = 0.86), supporting the selected biomarkers as diagnostic markers for sow fertility status. Overall, there was an apparent shift from a normal vaginal microbiota in HRP sows to a dysbiotic vaginal microbiota in INF sows dominated by potentially pathogenic, more anaerobic and short-chain fatty acid (SCFA)-producing microbiota that may play damaging and pro-inflammatory roles in the vaginal canal. Lactobacillus, known to be the dominant genus in a majority of healthy human vaginal microbiomes, was not the dominating genus in either INF or HRP vaginal microbiomes, suggesting the uniqueness of pig vaginal microbiomes. This also emphasizes the need for pig-specific vaginal microbiome research. Investigations regarding the application of the potential vaginal microbial biomarkers identified in this study to a large, novel population of sow and gilt vaginal microbiome samples to test their on-farm accuracy are underway. In addition, the mechanistic and physiologic role of SCFAs and the bacteria that produce them in the sow vaginal microbiome should be further investigated to understand how they may influence or be a product of fertility status.