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Stool is a mixture of excrement, microbiota, enzymes, undigested material, and small molecules. Fecal metabolomics has gained interest recently, owing to advances in metabolomics and growing research into both the host's physiology and the gut microbiome. One challenge with fecal metabolomics is preserving the sample integrity from collection until analysis, as the microbiota and enzymes continue to alter the metabolome following defecation. Currently, flash-freezing or lyophilization are utilized to minimize post-collection metabolome changes; however, this requires complex equipment and immediate processing, precluding the possibility for at-home sampling. Commercial devices containing stabilizing solvents have been developed to facilitate at-home collection, ambient transport, and sample storage. Here, we explore the efficacy of a commercially available stool collection device with a stabilization reagent tailored to fecal metabolomics. Stool samples from six donors were either processed shortly post-collection or stored at room temperature for seven days in the tube, with and without the stabilization reagent. Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS)-based untargeted metabolomics was utilized for analyzing extracted metabolites. Chemometric analysis was used to evaluate the performance of the device. We found that the device with the stabilization reagent minimized changes in the metabolite profile relative to unstabilized stool left at room temperature for one week. [ABSTRACT FROM AUTHOR]

Introduction: Olive oil, derived from the olive tree (Olea europaea L.), is used in cooking, cosmetics, and soap production. Due to its high value, some producers adulterate olive oil with cheaper edible oils or fraudulently mislabel oils as olive to increase profitability. Adulterated products can cause allergic reactions in sensitive individuals and can lack compounds which contribute to the perceived health benefits of olive oil, and its corresponding premium price. Objective: There is a need for robust methods to rapidly authenticate olive oils. By utilising machine learning models trained on the nuclear magnetic resonance (NMR) spectra of known olive oil and edible oils, samples can be classified as olive and authenticated. While high‐field NMRs are commonly used for their superior resolution and sensitivity, they are generally prohibitively expensive to purchase and operate for routine screening purposes. Low‐field benchtop NMR presents an affordable alternative. Methods: We compared the predictive performance of partial least squares discrimination analysis (PLS‐DA) models trained on low‐field 60 MHz benchtop proton (1H) NMR and high‐field 400 MHz 1H NMR spectra. The data were acquired from a sample set consisting of 49 extra virgin olive oils (EVOOs) and 45 other edible oils. Results: We demonstrate that PLS‐DA models trained on low‐field NMR spectra are highly predictive when classifying EVOOs from other oils and perform comparably to those trained on high‐field spectra. We demonstrated that variance was primarily driven by regions of the spectra arising from olefinic protons and ester protons from unsaturated fatty acids in models derived from data at both field strengths. This study compares the performance of partial least squares discrimination analysis (PLS‐DA) models trained on low‐field (LF) and high‐field (HF) nuclear magnetic resonance (NMR) spectra when distinguishing extra virgin olive oil (EVOO) from other edible oils. When predicting on an external dataset, LF NMR models were highly effective in classifying EVOO and had predictive performance comparable to HF models. These findings support LF NMR trained machine learning algorithms as a rapid, cost‐effective tool for EVOO authentication. [ABSTRACT FROM AUTHOR]

The search for potent antimicrobial compounds is critical in the face of growing antibiotic resistance. This study explores Acalypha arvensis Poepp. (A. arvensis), a Caribbean plant traditionally used for disease treatment. The dried plant powder was subjected to successive extractions using different solvents: hexane (F1), dichloromethane (F2), methanol (F3), a 50:50 mixture of methanol and water (F4), and water (F5). Additionally, a parallel extraction was conducted using a 50:50 mixture of methanol and chloroform (F6). All the fractions were evaluated for their antimicrobial activity, and the F6 fraction was characterized using untargeted metabolomics using SPME-GC×GC-TOFMS. The extracts of A. arvensis F3, F4, and F5 showed antibacterial activity against Staphylococcus aureus ATCC 25923 (5 mg/mL), MRSA BA22038 (5 mg/mL), and Pseudomonas aeruginosa ATCC 27853 (10 mg/mL), and fraction F6 showed antibacterial activity against Staphylococcus aureus ATCC 29213 (2 mg/mL), Escherichia coli ATCC 25922 (20 mg/mL), Pseudomonas aeruginosa ATCC 27853 (10 mg/mL), Enterococcus faecalis ATCC 29212 (10 mg/mL), Staphylococcus aureus 024 (2 mg/mL), and Staphylococcus aureus 003 (2 mg/mL). Metabolomic analysis of F6 revealed 2861 peaks with 58 identified compounds through SPME and 3654 peaks with 29 identified compounds through derivatization. The compounds included methyl ester fatty acids, ethyl ester fatty acids, terpenes, ketones, sugars, amino acids, and fatty acids. This study represents the first exploration of A. arvensis metabolomics and its antimicrobial potential, providing valuable insights for plant classification, phytochemical research, and drug discovery. [ABSTRACT FROM AUTHOR]