Identifying Putative Biomarkers of Foodborne Pathogens Using a Metabolomic Approach.

Metabolomics is the study of low molecular weight biochemical molecules (typically <1500 Da) in a defined biological organism or system. In case of food systems, the term "food metabolomics" is often used. Food metabolomics has been widely explored and applied in various fields including food analysis, food intake, food traceability, and food safety. Food safety applications focusing on the identification of pathogen-specific biomarkers have been promising. This chapter describes a nontargeted metabolite profiling workflow using gas chromatography coupled with mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for the extraction of polar metabolites from media, the analysis of the extracts using GC-MS, and finally chemometric data analysis using univariate and multivariate statistical tools to identify potential pathogen-specific biomarkers.

Qualitative discrimination and quantitative prediction of salt in aqueous solution based on near-infrared spectroscopy.

Freshwater resources have been gradually salinized in recent years, dramatically impacting the ecosystem and human health. Therefore, it is necessary to detect the salinity of freshwater resources. However, traditional detection methods make it difficult to check the type and concentration of salt quickly and accurately in solution. This paper uses a portable near-infrared spectrometer to qualitatively discriminate and quantitatively predict the salt in the solution. The study was carried out by adding ten salts of NaCl, KCl, MgCl2, CaCl2, Na2CO3, K2CO3, CaCO3, Na2SO4, K2SO4, MgSO4 to 2 mL of deionized water to prepare a single salt solution (0.02 %-1.00 %) totaling 100 sets. It was found that the Support vector machine (SVM) model was only effective in discriminating the class of salt anions in the solution. The Partial least squares-discriminant analysis (PLS-DA) model, on the other hand, can effectively discriminate the classes of salt in solution, and the accuracies of the optimal model prediction set and the interactive validation set are 98.86 % and 99.66 %, respectively. Furthermore, the Partial least squares regression (PLSR) models can accurately predict the concentration of NaCl, KCl, MgCl2, CaCl2, Na2CO3, K2CO3, CaCO3, Na2SO4, K2SO4, MgSO4 salt solutions. The coefficients of determination R2 of their model interactive validation sets were 0.99, 0.99, 0.99, 0.97, 0.99, 0.99, 0.98, 0.99, 0.98, and 0.98, respectively. This study shows that NIRS can achieve rapid and accurate qualitative and quantitative detection of salts in solution, which provides technical support for the utilization of safe water resources.

Comparative Investigation of Aroma-Active Volatiles in ("Ruixue", "Liangzhi", "Crystal Fuji," and "Guifei") Apples by Application of Gas Chromatography-Mass Spectrometry-Olfactometry (GC-MS-O) and Two-Dimensional Gas Chromatography-Quadrupole Mass Spectrometry (GC × GC-qMS) Coupled with Sensory Molecular Science.

Aroma dramatically impacts the overall flavor profiles and consumer acceptance; therefore, it is necessary to conduct a comprehensive analysis of the aroma characteristics of apples. In this study, the aroma differences among four popular apple varieties ("Ruixue", "Liangzhi", "Crystal Fuji," and "Guifei") were compared using two extraction methods (headspace-solid phase microextraction, and solvent-assisted flavor evaporation) coupled with gas chromatography-mass spectrometry-olfactometry (GC-MS-O) and two-dimensional gas chromatography-quadrupole mass spectrometry (GC × GC-qMS). A total of 82 odorants were identified via GC-MS-O, and 143 volatiles were identified by GC× GC-qMS. Among them, 41 key aroma-active compounds (butanal, ethyl acetate, 3-methylbutanal, methyl butanoate, 2-methylpropyl acetate, ethyl 2-methylbutanoate, ethyl 3-methylbutanoate, butyl acetate, hexanal, 2-methylbutyl acetate, 1-butanol, 2-methylpropyl butanoate, 3-methylbutyl acetate, (E)-2-hexenal, butyl butanoate, butyl 2-methylbutanoate, hexyl acetate, hexyl butanoate, hexyl, 2-methylbutanoate, 1-octen-3-ol, 3-methylthiopropanol, 1,3-octanediol, linalyl acetate, and so on) with high odor activity values (OAVs) and AI value (odor activity values ≥1 or aroma intensity ≥3) were identified. Partial least-squares-discriminant analysis showed that Ruixue exhibited a high "fruity" note, Guifei and Crystal Fuji had the greatest "wood," "floral," and "sweet" notes, while Liangzhi presented a significant "green" note. This study provided flavor chemistry support for the apple quality control and production.

Attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) analysis of human nails: Implications for age determination in forensics.

A person's age estimation from biological evidence is a crucial aspect of forensic investigations, aiding in victim identification and criminal profiling. In this study, we present a novel approach of utilizing Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) spectroscopy to predict the age of donors based on nail samples. A diverse dataset comprising nails from donors spanning different age groups was analyzed using ATR FT-IR, with subsequent multivariate analysis techniques used for age prediction. The developed partial least squares regression (PLS-R) model demonstrated promising accuracy in age estimation, with a root mean square error of prediction (RMSEP) equal to 11.1 during external validation. Additionally, a partial least squares discriminant analysis (PLS-DA) classification model achieved high accuracy of 88% in classifying donors into younger and older age groups during external validation. This proof-of-concept study highlights the potential of ATR FT-IR spectroscopy as a non-destructive and efficient tool for age estimation in forensic investigations, offering a new approach to forensic analysis with practical implications.

Comparison between Traditional and Novel NMR Methods for the Analysis of Sicilian Monovarietal Extra Virgin Olive Oils: Metabolic Profile Is Influenced by Micro-Pedoclimatic Zones.

Nuclear magnetic resonance (NMR) metabolomic analysis was applied to investigate the differences within nineteen Sicilian Nocellara del Belice monovarietal extra virgin olive oils (EVOOs), grown in two zones that are different in altitude and soil composition. Several classes of endogenous olive oil metabolites were quantified through a nuclear magnetic resonance (NMR) three-experiment protocol coupled with a yet-developed data-processing called MARA-NMR (Multiple Assignment Recovered Analysis by Nuclear Magnetic Resonance). This method, taking around one-hour of experimental time per sample, faces the possible quantification of different class of compounds at different concentration ranges, which would require at least three alternative traditional methods. NMR results were compared with the data of traditional analytical methods to quantify free fatty acidity (FFA), fatty acid methyl esters (FAMEs), and total phenol content. The presented NMR methodology is compared with traditional analytical practices, and its consistency is also tested through slightly different data treatment. Despite the rich literature about the NMR of EVOOs, the paper points out that there are still several advances potentially improving this general analysis and overcoming the other cumbersome and multi-device analytical strategies. Monovarietal EVOO's composition is mainly affected by pedoclimatic conditions, in turn relying upon the nutritional properties, quality, and authenticity. Data collection, analysis, and statistical processing are discussed, touching on the important issues related to the climate changes in Sicily and to the specific influence of pedoclimatic conditions.

Classification of Mass Spectral Data to Assist in the Identification of Novel Synthetic Cannabinoids.

Detection and characterization of newly synthesized cannabinoids (NSCs) is challenging due to the lack of availability of reference standards and chemical data. In this study, a binary classification system was developed and validated using partial least square discriminant analysis (PLS-DA) by utilizing readily available mass spectral data of known drugs to assist in the identification of previously unknown NCSs. First, a binary classification model was developed to discriminate cannabinoids and cannabinoid-related compounds from other drug classes. Then, a classification model was developed to discriminate classical (THC-related) from synthetic cannabinoids. Additional models were developed based on the most abundant functional groups including core groups such as indole, indazole, azaindole, and naphthoylpyrrole, as well as head and tail groups including 4-fluorobenzyl (FUB) and 5-Fluoropentyl (5-F). The predictive ability of these models was tested via both cross-validation and external validation. The results show that all models developed are highly accurate. Additionally, latent variables (LVs) of each model provide useful mass to charge (m/z) for discrimination between classes, which further facilitates the identification of different functional groups of previously unknown drug molecules.

Detection of serum alterations in polysubstance use patients by FT-Raman spectroscopy.

Substance use disorders pose significant health risks and treatment challenges due to the diverse interactions between substances and their impact on physical and mental health. The chemical effects of multiple substance use on bodily fluids are not yet fully understood. Therefore, this study aimed to investigate the chemical changes induced by a combination of substances compared to a control group. Analysis of FT-Raman spectra revealed structural alterations in the amide III, I, and C = O functional groups of lipids in subjects treated with opioids, alcohol and cannabis (polysubstance group). These changes were evident in the form of peak shifts compared to the control group. Additionally, an imbalance in the amide-lipid ratio was observed, indicating perturbations in serum protein and lipid levels. Furthermore, a 2D plot of two-track two-dimensional correlation spectra (2T2D-COS) demonstrated a shift towards dominance of lipid vibrations in the polysubstance use groups, contrasting with the predominance of the amide fraction in the control group. This observation suggests distinct molecular changes induced by multiple substance use, potentially contributing to the pathophysiology of substance use disorders. Principal Component Analysis (PCA) was utilized to visualize the data structure and identify outliers. Subsequently, Partial Least Squares Discriminant Analysis (PLS-DA) was employed to classify the polysubstance use and control groups. The PLS-DA model demonstrated high classification accuracy, achieving 100.00 % in the training dataset and 94.74 % in the test dataset. Furthermore, receiver operating characteristic (ROC) analysis yielded perfect AUC values of 1.00 for both the training and test sets, underscoring the robustness of the classification model. This study highlights the quantitative and qualitative changes in serum protein and lipid levels induced by polysubstance use groups, as evidenced by FT-Raman spectroscopy. The findings underscore the importance of understanding the chemical effects of polysubstance use on bodily fluids for improved diagnosis and treatment of substance use disorders. Moreover, the successful classification of spectral data using machine learning techniques emphasizes the potential of these approaches in clinical applications for substance abuse monitoring and management.

Infrared spectroscopy as a new approach for early fabry disease screening: a pilot study.

BACKGROUND: Fabry disease (FD) is a rare X-linked lysosomal storage disorder marked by alpha-galactosidase-A (α-Gal A) deficiency, caused by pathogenic mutations in the GLA gene, resulting in the accumulation of glycosphingolipids within lysosomes. The current screening test relies on measuring α-Gal A activity. However, this approach is limited to males. Infrared (IR) spectroscopy is a technique that can generate fingerprint spectra of a biofluid's molecular composition and has been successfully applied to screen numerous diseases. Herein, we investigate the discriminating vibration profile of plasma chemical bonds in patients with FD using attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy. RESULTS: The Fabry disease group (n = 47) and the healthy control group (n = 52) recruited were age-matched (39.2 ± 16.9 and 36.7 ± 10.9 years, respectively), and females were predominant in both groups (59.6% and 65.4%, respectively). All patients had the classic phenotype (100%), and no late-onset phenotype was detected. A generated partial least squares discriminant analysis (PLS-DA) classification model, independent of gender, allowed differentiation of samples from FD vs. control groups, reaching 100% sensitivity, specificity and accuracy. CONCLUSION: ATR-FTIR spectroscopy harnessed to pattern recognition algorithms can distinguish between FD patients and healthy control participants, offering the potential of a fast and inexpensive screening test.

Plasma metabolomics profiles and breast cancer risk.

BACKGROUND: Breast cancer (BC) is the most common cancer in women and incidence rates are increasing; metabolomics may be a promising approach for identifying the drivers of the increasing trends that cannot be explained by changes in known BC risk factors. METHODS: We conducted a nested case-control study (median followup 6.3 years) within the New York site of the Breast Cancer Family Registry (BCFR) (n = 40 cases and 70 age-matched controls). We conducted a metabolome-wide association study using untargeted metabolomics coupling hydrophilic interaction liquid chromatography (HILIC) and C18 chromatography with high-resolution mass spectrometry (LC-HRMS) to identify BC-related metabolic features. RESULTS: We found eight metabolic features associated with BC risk. For the four metabolites negatively associated with risk, the adjusted odds ratios (ORs) ranged from 0.31 (95% confidence interval (CI): 0.14, 0.66) (L-Histidine) to 0.65 (95% CI: 0.43, 0.98) (N-Acetylgalactosamine), and for the four metabolites positively associated with risk, ORs ranged from 1.61 (95% CI: 1.04, 2.51, (m/z: 101.5813, RT: 90.4, 1,3-dibutyl-1-nitrosourea, a potential carcinogen)) to 2.20 (95% CI: 1.15, 4.23) (11-cis-Eicosenic acid). These results were no longer statistically significant after adjusting for multiple comparisons. Adding the BC-related metabolic features to a model, including age, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk score improved the accuracy of BC prediction from an area under the curve (AUC) of 66% to 83%. CONCLUSIONS: If replicated in larger prospective cohorts, these findings offer promising new ways to identify exposures related to BC and improve BC risk prediction.

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.

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

BACKGROUND: 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. OBJECTIVES: To study the association between PFASs and CVD in U.S. population, and to reveal the mechanism of PFASs' effects on CVD. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Tuning multispectral fluorescence quantum dot-based identification of short-length amyloid ß peptides by applying Cu(II) ions.

Currently available methods for detecting amyloid ß (Aß) derivatives are mainly dedicated to determining the long forms Aß1-42 and Aß1-40. At the same time, the number of physiologically occurring Aß analogs is much higher, including those truncated at the N- and C-termini. Their identification using standard methods is challenging due to the structural similarity of various Aß analogs, but could highly benefit from both biomarkers discovery and pathophysiological studies of Alzheimer's disease. Therefore a "chemical tongue" sensing strategy was employed for the detection of seven Aß peptide derivatives: Aß1-16, Aß4-16, Aß4-9, Aß5-16, Aß5-12, Aß5-9, Aß12-16. The proposed sensing system is based on competitive interactions between quantum dots, Cu(II) ions, and Aß peptides, providing unique fluorescence fingerprints useful for the identification of analytes. After carefully evaluating the Aß sample preparation protocol, perfect determination of all studied Aß peptides was achieved using partial least square-discriminant analysis (PLS-DA). The developed PLS-DA models are characterized by excellent accuracy, sensitivity, precision, and specificity of analyte determination, emphasizing the potential of the proposed sensing strategy.

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.

A Comparison Analysis of Four Different Drying Treatments on the Volatile Organic Compounds of Gardenia Flowers.

The gardenia flower not only has extremely high ornamental value but also is an important source of natural food and spices, with a wide range of uses. To support the development of gardenia flower products, this study used headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) technology to compare and analyze the volatile organic compounds (VOCs) of fresh gardenia flower and those after using four different drying methods (vacuum freeze-drying (VFD), microwave drying (MD), hot-air drying (HAD), and vacuum drying (VD)). The results show that, in terms of shape, the VFD sample is almost identical to fresh gardenia flower, while the HAD, MD, and VD samples show significant changes in appearance with clear wrinkling; a total of 59 volatile organic compounds were detected in the gardenia flower, including 13 terpenes, 18 aldehydes, 4 esters, 8 ketones, 15 alcohols, and 1 sulfide. Principal component analysis (PCA), cluster analysis (CA), and partial least-squares regression analysis (PLS-DA) were performed on the obtained data, and the research found that different drying methods impact the VOCs of the gardenia flower. VFD or MD may be the most effective alternative to traditional sun-drying methods. Considering its drying efficiency and production cost, MD has the widest market prospects.

Rapid Assessment of Metabolomic Fingerprinting of Recycled Sunflower By-Products via DART-HRMS.

To comply with a more circular and environmentally friendly European common agricultural policy, while also valorising sunflower by-products, an ultrasound assisted extraction (UAE) was tested to optimise ethanol-wash solutes (EWS). Furthermore, the capabilities of DART-HRMS as a rapid and cost-effective tool for determining the biochemical changes after valorisation of these defatted sunflower EWS were investigated. Three batches of EWS were doubly processed into optimised EWS (OEWS) samples, which were analysed via DART-HRMS. Then, the metabolic profiles were submitted to a univariate analysis followed by a partial least square discriminant analysis (PLS-DA) allowing the identification of the 15 most informative ions. The assessment of the metabolomic fingerprinting characterising EWS and OEWS resulted in an accurate and well-defined spatial clusterization based on the retrieved pool of informative ions. The outcomes highlighted a significantly higher relative abundance of phenolipid hydroxycinnamoyl-glyceric acid and a lower incidence of free fatty acids and diglycerides due to the ultrasound treatment. These resulting biochemical changes might turn OEWS into a natural antioxidant supplement useful for controlling lipid oxidation and to prolong the shelf-life of foods and feeds. A standardised processing leading to a selective concentration of the desirable bioactive compounds is also advisable.

Vibrational spectroscopy data fusion for enhanced classification of different milk types.

The aim of this study is to classify seven types of Irish milk (butter, fresh, heart active, lactose free, light, protein, and slimline), supplied by a specific company, using vibrational spectroscopy methods: Near infrared (NIR), mid infrared (MIR), and Raman spectroscopy. In this regard, chemometric methods were used, and the impact of spectral data fusion on prediction accuracy was evaluated. A total of 105 samples were tested, with 21 used in the test set. The study assessed principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and sequential and orthogonalized partial least squares linear discriminant analysis (SO-PLS-LDA) for classifying different milk types. The prediction accuracy, when applying PLS-DA on individual blocks of data and low-level fused data, did not exceed 85.71 %. However, implementing the SO-PLS-LDA strategy significantly improved the accuracy to 95 %, suggesting a promising method for the development of classification models for milk using data fusion strategies.

Comparative chemometric modeling of fresh and dry cannabis inflorescences using FT-NIR spectroscopy: Quantification and classification insights.

INTRODUCTION: Cannabis sativa L. inflorescences are rich in cannabinoids and terpenes. Traditional chemical analysis methods for cannabinoids and terpenes, such as liquid and gas chromatography (using UV or MS detectors), are expensive and time-consuming. OBJECTIVES: This study explores the use of Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometric approaches for classifying cannabis chemovars and predicting cannabinoid and terpene concentrations for the first time in freshly harvested (wet) cannabis inflorescence. The study also compares the performance of FT-NIR spectroscopy on wet versus dry cannabis inflorescences. MATERIALS AND METHODS: Spectral data from 187 samples across seven cannabis chemovars were analyzed using partial least squares-discriminant analysis (PLS-DA) and partial least squares-regression (PLS-R) models. RESULTS: The PLS-DA models effectively classified chemovars and major classes using only two latent variables (LVs) with minimal overfitting risk, with sensitivity, specificity, and accuracy values approaching 1. Despite the high water content in wet cannabis inflorescence, the PLS-R models demonstrated good to excellent predictive capabilities for nine cannabinoids and eight terpenes using FT-NIR spectra for the first time, achieving cross-validation and prediction R-squared values greater than 0.7, ratio of performance to interquartile range (RPIQ) exceeding 2, and a RMSECV/RMSEC ratio below 1.24. However, the low-cannabidiolic acid submodel and (-)-Δ9-trans-tetrahydrocannabinol model showed poor predictive performance. Some cannabinoid and terpene prediction models in wet cannabis inflorescence exhibited lower predictive capabilities compared with previously published models for dry cannabis inflorescence. CONCLUSIONS: These findings suggest that FT-NIR spectroscopy can be a viable rapid on-site analytical tool for growers during the inflorescence flowering stage.

Characterization and Differentiation of Flavor Profile of 12 Air-Dried Yak Meat Products Using GC-IMS and Multivariate Analysis.

Volatile organic compounds (VOCs) in food are key factors constituting their unique flavor, while the characteristics of VOCs in air-dried yak meat (AYM) from various regions of the Tibetan Plateau and their inter-regional differences remain unclear. Therefore, this study conducted a comprehensive analysis of VOCs in the five-spice (FS), spicy and numbing (SN), and aromatic and spicy (AS) versions of AYM from four regions of the Tibetan Plateau (Gansu, Qinghai, Sichuan, and Tibet) using gas chromatography-ion mobility spectrometry (GC-IMS) A total of 58 VOCs were identified, with alcohols accounting for 28.40%, ketones 22.89%, aldehydes 18.85%, and terpenes 17.61%. Topographic plots, fingerprint profiles, and multivariate analysis not only distinguished AYM of the same flavor from different regions but also discriminated those of different flavors within the same region. Furthermore, 17 key VOCs were selected as the primary aroma characteristics of the 12 types of AYM, including linalool, 3-methylbutanal, acetone, and limonene. Meanwhile, the differential VOCs for each flavor were determined, with linalyl acetate being unique to the FS, (E)-ocimene and ethyl propanoate being specific to the SN, and 2-methyl-3-(methylthio)furan-D and Hexanal-D being characteristic of the AS flavor. Based on the above results, the flavor of AYM can be improved to suit the taste of most people and increase its consumption.

Integrated Chemometrics and Data-Independent Acquisition Proteomics for the Discovery of Meat Authenticity Biomarkers: A Study on Early Post-Mortem Pectoralis major Muscle Proteomes of Ross 308 and Ranger Classic Chicken Produced by Organic versus Antibiotic-Free Farming Systems.

Many factors, such as the farming systems and preslaughter rearing practices, can influence the physiological and metabolic functions of poultry with consequent effects on poultry meat quality. In this trial, label-free shotgun proteomics was used to analyze the early post-mortem Pectoralis major muscle proteomes of Ross 308 and Ranger Classic chicken strains raised under two divergent farming systems these being organic and antibiotic-free. The combination of chemometrics using partial-least-square discriminant analysis (PLS-DA) and shotgun proteomics allowed clear discrimination between the different groups. Chicken strains were discriminated by differences in the abundance of 73 and 62 proteins within the antibiotic-free and organic farming systems, respectively. The abundances of 71 and 52 proteins were impacted by the farming system within the Ross 308 and Ranger Classic chicken strains, respectively. The analyses allowed for the proposal of several putative biomarkers of meat authenticity, which were found to be related to muscle structure and energy metabolism pathways. This study is a significant step forward in elucidating the potential of proteomics profiling and chemometrics in chicken meat, which may provide opportunities for the efficient assessment of chicken authenticity.

Mass spectrometry (MS)-based metabolomics of plasma and urine in dry eye disease (DED)-induced rat model.

Dry eye disease (DED) is an ophthalmic disease associated with poor quality and quantity of tears, and the number of patients is steadily increasing. The aim of this study was to determine plasma and urine metabolites obtained from DED scopolamine animal model where dry eye conditions (DRY) are induced. It was also of interest to examine whether DED (scopolamine) rat model was exacerbated by treatment with benzalkonium chloride (BAC). Subsequently, plasma and urine metabolites were analyzed using liquid chromatography (LC) and gas chromatography (GC)-mass spectrometry (MS), respectively. Data demonstrated that DED indicators such as tear volume, tear breakup time (TBUT), and corneal damage in the DED groups (DRY and BAC group) differed from those of control (CON). Similar results were noted in inflammatory factors such as interleukin (IL-1ß), IL-6, and tumor necrosis factor (TNF)-α. In the partial least squares-discriminant analysis (PLS-DA) score plots, the three groups were distinctly separated from each other. In addition, the related metabolites were also associated with these distinct separations as evidenced by 9 and 14 in plasma and urine, respectively. Almost all of the selected metabolites were decreased in the DRY group compared to CON, and the BAC group was lower than the DRY. In plasma and urine, lysophosphatidylcholine/lysophosphatidylethanolamine, organic acids, amino acids, and sugars varied between three groups, and these metabolites were related to inflammation and oxidative stress. Data suggest that treatment with scopolamine with/without BAC-induced DED and affected the level of systemic metabolites involved in inflammation and oxidative stress.