Discriminating extra virgin olive oils from common edible oils: Comparable performance of PLS-DA models trained on low-field and high-field 1H NMR data.

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.

Evaluation of normalization strategies for GC-based metabolomics.

INTRODUCTION: For many samples studied by GC-based metabolomics applications, extensive sample preparation involving extraction followed by a two-step derivatization procedure of methoximation and trimethylsilylation (TMS) is typically required to expand the metabolome coverage. Performing normalization is critical to correct for variations present in samples and any biases added during the sample preparation steps and analytical runs. Addressing the totality of variations with an adequate normalization method increases the reliability of the downstream data analysis and interpretation of the results. OBJECTIVES: Normalizing to sample mass is one of the most commonly employed strategies, while the total peak area (TPA) as a normalization factor is also frequently used as a post-acquisition technique. Here, we present a new normalization approach, total derivatized peak area (TDPA), where data are normalized to the intensity of all derivatized compounds. TDPA relies on the benefits of silylation as a universal derivatization method for GC-based metabolomics studies. METHODS: Two sample classes consisting of systematically incremented sample mass were simulated, with the only difference between the groups being the added amino acid concentrations. The samples were TMS derivatized and analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). The performance of five normalization strategies (no normalization, normalized to sample mass, TPA, total useful peak area (TUPA), and TDPA) were evaluated on the acquired data. RESULTS: Of the five normalization techniques compared, TUPA and TDPA were the most effective. On PCA score space, they offered a clear separation between the two classes. CONCLUSION: TUPA and TDPA carry different strengths: TUPA requires peak alignment across all samples, which depends upon the completion of the study, while TDPA is free from the requirement of alignment. The findings of the study would enhance the convenient and effective use of data normalization strategies and contribute to overcoming the data normalization challenges that currently exist in the metabolomics community.

Use of reconstituted kefir consortia to determine the impact of microbial composition on kefir metabolite profiles.

Kefir is fermented traditionally with kefir grains, but commercial kefir production often relies on fermentation with planktonic cultures. Kefir has been associated with many health benefits, however, the utilization of kefir grains to facilitate large industrial production of kefir is challenging and makes to difficult to ensure consistent product quality and consistency. Notably, the microbial composition of kefir fermentations has been shown to impact kefir associated health benefits. This study aimed to compare volatile compounds, organic acids, and sugar composition of kefir produced through a traditional grain fermentation and through a reconstituted kefir consortium fermentation. Additionally, the impact of two key microbial communities on metabolite production in kefir was assessed using two modified versions of the consortium, with either yeasts or lactobacilli removed. We hypothesized that the complete kefir consortium would closely resemble traditional kefir, while the consortia without yeasts or lactobacilli would differ significantly from both traditional kefir and the complete consortium fermentation. Kefir fermentations were examined after 12 and 18 h using two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) to identify volatile compounds and high performance liquid chromatography (HPLC) to identify organic acid and sugar composition. The traditional kefir differed significantly from the kefir consortium fermentation with the traditional kefir having 15-20 log2(fold change) higher levels of esters and the consortium fermented kefir having between 1 and 3 log2(fold change) higher organic acids including lactate and acetate. The use of a version of kefir consortium that lacked lactobacilli resulted in between 2 and 20 log2(fold change) lower levels of organic acids, ethanol, and butanoic acid ethyl ester, while the absence of yeast from the consortium resulted in minimal change. In summary, the kefir consortium fermentation is significantly different from traditional grain fermented kefir with respect to the profile of metabolites present, and seems to be driven by lactobacilli, as evidenced by the significant decrease in multiple metabolites when the lactobacilli were removed from the fermentation and minimal differences observed upon the removal of yeast.

Optimization of fecal sample homogenization for untargeted metabolomics.

INTRODUCTION: Fecal samples are highly complex and heterogeneous, containing materials at various stages of digestion. The heterogeneity and complexity of feces make stool metabolomics inherently challenging. The level of homogenization influences the outcome of the study, affecting the metabolite profiles and reproducibility; however, there is no consensus on how fecal samples should be prepared to overcome the topographical discrepancy and obtain data representative of the stool as a whole. OBJECTIVES: Various combinations of homogenization conditions were compared to investigate the effects of bead size, addition of solvents and the differences between wet-frozen and lyophilized feces. METHODS: The homogenization parameters were systematically altered to evaluate the solvent usage, bead size, and whether lyophilization is required in homogenization. The metabolic coverage and reproducibility were compared among the different conditions. RESULTS: The current work revealed that a combination of mechanical and chemical lysis obtained by bead-beating with a mixture of big and small sizes of beads in an organic solvent is an effective way to homogenize fecal samples with adequate reproducibility and metabolic coverage. Lyophilization is required when bead-beating is not available. CONCLUSIONS: A comprehensive and systematical evaluation of various fecal matter homogenization conditions provides a profound understanding for the effects of different homogenization methods. Our findings would be beneficial to assist with standardization of fecal sample homogenization protocol.

Untargeted Metabolomic Profiling of Aqueous and Lyophilized Pooled Human Feces from Two Diet Cohorts Using Two-Dimensional Gas Chromatography Coupled with Time-of-Flight Mass Spectrometry.

The metabolic profiles of human feces are influenced by various genetic and environmental factors, which makes feces an attractive biosample for numerous applications, including the early detection of gut diseases. However, feces is complex, heterogeneous, and dynamic with a significant live bacterial biomass. With such challenges, stool metabolomics has been understudied compared to other biospecimens, and there is a current lack of consensus on methods to collect, prepare, and analyze feces. One of the critical steps required to accelerate the field is having a metabolomics stool reference material available. Fecal samples are generally presented in two major forms: fecal water and lyophilized feces. In this study, two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) was used as an analytical platform to characterize pooled human feces, provided by the National Institute of Standards and Technology (NIST) as Research-Grade Test Materials. The collected fecal samples were derived from eight healthy individuals with two different diets: vegans and omnivores, matched by age, sex, and body mass index (BMI), and stored as fecal water and lyophilized feces. Various data analysis strategies were presented to determine the differences in the fecal metabolomic profiles. The results indicate that the sample storage condition has a major influence on the metabolic profiles of feces such that the impact from storage surpasses the metabolic differences from the diet types. The findings of the current study would contribute towards the development of a stool reference material.

Dietary benzoic acid and supplemental enzymes alter fiber-fermenting taxa and metabolites in the cecum of weaned pigs.

Inclusion of enzymes and organic acids in pig diets is an important strategy supporting decreased antibiotic usage in pork production. However, limited knowledge exists about how these additives impact intestinal microbes and their metabolites. To examine the effects of benzoic acid and enzymes on gut microbiota and metabolome, 160 pigs were assigned to one of four diets 7 days after weaning: a control diet or the addition of 0.5% benzoic acid, 0.045% dietary enzymes (phytase, ß-glucanase, xylanase, and α-amylase), or both and fed ad libitum for 21 to 22 d. Individual growth performance and group diarrhea incidence data were collected throughout the experimental period. A decrease of 20% in pen-level diarrhea incidence from days 8 to 14 in pigs-fed both benzoic acid and enzymes compared to the control diet (P = 0.047). Cecal digesta samples were collected at the end of the experimental period from 40 piglets (n = 10 per group) and evaluated for differences using 16S rRNA sequencing and two-dimensional gas chromatography and time-of-flight mass spectrometry (GCxGC-TOFMS). Analysis of cecal microbiota diversity revealed that benzoic acid altered microbiota composition (Unweighted Unifrac, P = 0.047, r2 = 0.07) and decreased α-diversity (Shannon, P = 0.041; Faith's Phylogenetic Diversity, P = 0.041). Dietary enzymes increased fiber-fermenting bacterial taxa such as Prevotellaceae. Two-step feature selection identified 17 cecal metabolites that differed among diets, including increased microbial cross-feeding product 1,2-propanediol in pigs-fed benzoic acid-containing diets. In conclusion, dietary benzoic acid and enzymes affected the gut microbiota and metabolome of weaned pigs and may support the health and resolution of postweaning diarrhea.

Feeding weaned pigs diets containing benzoic acid or supplemental enzymes for 21 d after weaning changed the gut microbiota and metabolome. Benzoic acid increased feed intake, weight gain, and the presence of 1,2-propanediol in cecal digesta, which is an important microbial cross-feeding product. Dietary enzymes altered microbiota composition, increasing the presence of fiber-fermenting microbes including Prevotellaceae. Pigs fed a combination of both benzoic acid and enzymes showed improved resolution of postweaning diarrhea. These differences demonstrate the role of these feed additives in the establishment of gut microbes and metabolic pathways for the degradation of complex dietary components in the weaned pig. This study provides new information about alterations in microbial function and community composition using microbiota sequencing and metabolomic analysis.

Evaluation of fresh, frozen, and lyophilized fecal samples by SPME and derivatization methods using GC×GC-TOFMS.

INTRODUCTION: Feces is a highly complex matrix containing thousands of metabolites. It also contains live bacteria and enzymes, and does not have a static chemistry. Consequently, proper control of pre-analytical parameters is critical to minimize unwanted variations in the samples. However, no consensus currently exists on how fecal samples should be stored/processed prior to analysis. OBJECTIVE: The effects of sample handling conditions on fecal metabolite profiles and abundances were examined using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS). METHODS: Solid-phase microextraction (SPME) and derivatization via trimethylsilylation (TMS) were employed as complementary techniques to evaluate fresh, frozen, and lyophilized fecal samples with expanded coverage of the fecal metabolome. The total number of detected peaks and the signal intensities were compared among the different handling conditions. RESULTS: Our analysis revealed that the metabolic profiles of fecal samples depend greatly on sample handling and processing conditions, which had a more pronounced effect on results obtained by SPME than by TMS derivatization. Overall, lyophilization resulted in a greater amount of total and class-specific metabolites, which may be attributed to cell lysis and/or membrane disintegration. CONCLUSIONS: A comprehensive comparison of the sample handling conditions provides a deeper understanding of the physicochemical changes that occur within the samples during freezing and lyophilization. Based on our results, snap-freezing at -80 °C would be preferred over lyophilization for handling samples in the field of fecal metabolomics as this imparts the least change from the fresh condition.

Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMS.

Urine is a popular biofluid for metabolomics studies due to its simple, non-invasive collection and its availability in large quantities, permitting frequent sampling, replicate analyses, and sample banking. The biggest disadvantage with using urine is that it exhibits significant variability in concentration and composition within an individual over relatively short periods of time (arising from various external factors and internal processes regulating the body's water and solute content). In treating the data from urinary metabolomics studies, one must account for the natural variability of urine concentrations to avoid erroneous data interpretation. Amongst various proposed approaches to account for broadly varying urine sample concentrations, normalization to creatinine has been widely accepted and is most commonly used. MS total useful signal (MSTUS) is another normalization method that has been recently reported for mass spectrometry (MS)-based metabolomics studies. Herein, we explored total useful peak area (TUPA), a modification of MSTUS that is applicable to GC×GC-TOFMS (and data from other separations platforms), for sample normalization in urinary metabolomics studies. Performance of TUPA was compared to the two most common normalization approaches, creatinine adjustment and Total Peak Area (TPA) normalization. Each normalized dataset was evaluated using Principal Component Analysis (PCA). The results showed that TUPA outperformed alternative normalization methods to overcome urine concentration variability. Results also conclusively demonstrate the risks in normalizing data to creatinine.

Comprehensive two-dimensional gas chromatographic profiling and chemometric interpretation of the volatile profiles of sweat in knit fabrics.

Human axillary sweat is a poorly explored biofluid within the context of metabolomics when compared to other fluids such as blood and urine. In this paper, we explore the volatile organic compounds emitted from two different types of fabric samples (cotton and polyester) which had been worn repeatedly during exercise by participants. Headspace solid-phase microextraction (SPME) and comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) were employed to profile the (semi)volatile compounds on the fabric. Principal component analysis models were applied to the data to aid in visualizing differences between types of fabrics, wash treatment, and the gender of the subject who had worn the fabric. Statistical tools included with commercial chromatography software (ChromaTOF) and a simple Fisher ratio threshold-based feature selection for model optimization are compared with a custom-written algorithm that uses cluster resolution as an objective function to maximize in a hybrid backward-elimination forward-selection approach for optimizing the chemometric models in an effort to identify some compounds that correlate to differences between fabric types. The custom algorithm is shown to generate better models than the simple Fisher ratio approach. Graphical Abstract A route from samples and questions to data and then answers.