Chromatography hyphenated to high resolution mass spectrometry in untargeted metabolomics for investigation of food (bio)markers

Untargeted metabolomics unravels distinct gut microbial metabolites derived from plant-based and animal-origin proteins using in vitro modeling

The popularity of plant-based meat alternatives (PBMAs) has sparked a contentious debate about their influence on intestinal homeostasis compared to traditional animal-based meats. This study aims to explore the changes in gut microbial metabolites (GMMs) induced by the gut microbiota on different digested patties: beef meat and pea-protein PBMA. After digesting in vitro, untargeted metabolomics revealed 32 annotated metabolites, such as carnitine and acylcarnitines correlated with beef meat, and 45 annotated metabolites, like triterpenoids and lignans, linked to our PBMA. Secondly, (un)targeted approaches highlighted differences in GMM patterns during colonic fermentations. Our findings underscore significant differences in amino acids and their derivatives. Beef protein fermentation resulted in higher production of methyl-histidine, gamma-glutamyl amino acids, indoles, isobutyric and isovaleric acids. In contrast, PBMAs exhibit a significant release of N-acyl amino acids and unique dipeptides, like phenylalanine-arginine. This research offers valuable insights into how PBMAs and animal-based proteins differently modulate intestinal microenvironments.

Exhaustive Search of Dietary Intake Biomarkers as Objective Tools for Personalized Nutrimetabolomics and Precision Nutrition Implementation

OBJECTIVE

To conduct an exhaustive scoping search of existing literature, incorporating diverse bibliographic sources to elucidate the relationships between metabolite biomarkers in human fluids and dietary intake.

BACKGROUND

The search for biomarkers linked to specific dietary food intake holds immense significance for precision health and nutrition research. Using objective methods to track food consumption through metabolites offers a more accurate way to provide dietary advice and prescriptions on healthy dietary patterns by healthcare professionals. An extensive investigation was conducted on biomarkers associated with the consumption of several food groups and consumption patterns. Evidence is integrated from observational studies, systematic reviews, and meta-analyses to achieve precision nutrition and metabolism personalization.

METHODS

Tailored search strategies were applied across databases and gray literature, yielding 158 primary research articles that met strict inclusion criteria. The collected data underwent rigorous analysis using STATA and Python tools. Biomarker-food associations were categorized into 5 groups: cereals and grains, dairy products, protein-rich foods, plant-based foods, and a miscellaneous group. Specific cutoff points (≥3 or ≥4 bibliographic appearances) were established to identify reliable biomarkers indicative of dietary consumption.

RESULTS

Key metabolites in plasma, serum, and urine revealed intake from different food groups. For cereals and grains, 3-(3,5-dihydroxyphenyl) propanoic acid glucuronide and 3,5-dihydroxybenzoic acid were significant. Omega-3 fatty acids and specific amino acids showcased dairy and protein foods consumption. Nuts and seafood were linked to hypaphorine and trimethylamine N-oxide. The miscellaneous group featured compounds like theobromine, 7-methylxanthine, caffeine, quinic acid, paraxanthine, and theophylline associated with coffee intake.

CONCLUSIONS

Data collected from this research demonstrate potential for incorporating precision nutrition into clinical settings and nutritional advice based on accurate estimation of food intake. By customizing dietary recommendations based on individualized metabolic profiles, this approach could significantly improve personalized food consumption health prescriptions and support integrating multiple nutritional data.This article is part of a Nutrition Reviews special collection on Precision Nutrition.

Analyzing the differences and correlations between key metabolites and dominant microorganisms in different regions of Daqu based on off-target metabolomics and high-throughput sequencing

Daqu is usually produced in an open environment, which makes its quality unstable. The microbial community of Daqu largely determines its quality. Therefore, in order to improve the fermentation characteristics of Daqu, samples were collected from Jinsha County (MT1), Xishui County (MT2), and Maotai Town (MT3) in Guizhou Province to explore the microbial diversity of Daqu and its impact on Daqu's metabolites.Off-target metabolomics was used to detect metabolites, and high-throughput sequencing was used to detect microorganisms. Metabolomics results revealed that MT1 and MT2 had the highest relative fatty acid content, whereas MT3 had the highest organooxygen compound content. Principal component analysis and partial least squares discriminant analysis revealed significant differences in the metabolites among the three groups, followed by the identification of 33 differential metabolites (key metabolites) filtered using the criteria of variable importance in projection >1 and p < 0.001. According to the microbiological results, Proteobacteria was the dominant bacteria phylum in three samples. Gammaproteobacteria was the dominant class in MT1(26.84 %) and MT2(36.54 %), MT3 is Alphaproteobacteria(25.66 %). And Caulobacteraceae was the dominant family per the abundance analysis, MTI was 24.32 %, MT2 and MT3 were 33.71 % and 24.40 % respectively. Three samples dominant fungi phylum were Ascomycota, and dominant fungi family were Thermoascaceae. Pseudomonas showed a significant positive connection with various fatty acyls, according to correlation analyses between dominant microorganisms (genus level) and key metabolites. Fatty acyls and Thermomyces showed a positive correlation, but Thermoascus had the reverse relation. These findings offer a theoretical framework for future studies on the impact of metabolites on Baijiu quality during fermentation.

Integrated, high-throughput metabolomics approach for metabolite analysis of four sprout types

In this study, we combined two distinct extraction and separation techniques with the aim of comprehensively collecting metabolite features in sprouts, particularly hydrophilic compounds. By synergistically analyzing the data using MS-DIAL and MetaboAnalystR, we obtained a greater number of annotated metabolites and explored differences in annotation across analytical tools. We found that this approach significantly increased the number of detected metabolite features and the final identification counts. Furthermore, we explored the functional component characteristics of four sprout types. This study provides data supporting the potential of sprouts as nutritious vegetables and functional food ingredients, emphasizing their value in the development of functional foods.

Identification and quantification of flavor compounds in smoked tuna fish based on GC-Orbitrap volatolomics approach

Cold smoking enhances the appeal of fish products, offering consumers a smooth texture and a delicate smoky flavor. This study aims to explore variations in the volatile profile from different exposure times during cold smoking processing (light, moderate, and full-cure) in tune samples. An innovative untargeted analytical approach, headspace solid-phase microextraction combined with gas chromatography and a hybrid quadrupole-orbitrap mass analyzer, was employed to identify 86 volatiles associated with the cold smoking process. Most of these compounds, including phenols, furan derivates, aldehydes, cyclic ketones, and different aromatic species, were found to contribute to the smoke odor. The development of a QuEChERS-based extraction and clean-up method facilitated the quantification of 25 relevant smoky markers across all smoking degrees, revealing significant concentration differences after 15 h of smoking. This research sheds light on the dynamics of cold smoking impact and its on the flavor profile and safety quality of processed fish products.

In-depth characterization of cycloartane triterpenoids and discovery of species-specific markers from three Cimicifuga species guided by a strategy that integrates in-source fragment elimination, diagnostic ion recognition, and feature-based molecular networking

Characterization studies of the plant metabolome are crucial for revealing plant physiology, developing functional foods, and controlling quality. Mass spectrometry-based metabolite profiling allows unprecedented qualitative coverage of complex biological extract composition. However, the electrospray ionization used in metabolite profiling generates multiple artifactual signals for a single analyte, which makes it challenging to filter out redundant signals and organize the signals corresponding to abundant constituents. This study proposed a strategy integrating in-source fragments elimination, diagnostic ions recognition, and feature-based molecular networking (ISFE-DIR-FBMN) to simultaneously characterize cycloartane triterpenoids (CTs) from three medicinal Cimicifuga species. The results showed that 63.1 % of the measured ions were redundant. A total of 184 CTs were annotated, with 27.1 % being reported for the first time. It presents a promising approach to assess the composition of natural extracts, thus facilitating new ingredient registrations or natural-extracts-based drug discovery campaigns. Besides, chemometrics analysis of the three Cimicifuga species identified 32 species-specific markers, highlighting significant differences among them. The valuable information can enhance the sustainable utilization and further development of Cimicifuga resources. The codes involved in ISFE-DIR-FBMN are freely available on GitHub (https://github.com/LHJ-Group/ISFE-DIR-FBMN.git).

Exposure to PFAS contaminated urban wetland water causes similar metabolic alterations to laboratory-based exposures in the freshwater amphipod Austrochiltonia subtenuis

Assessing the harm caused by pollutants in urban ecosystems remains a significant challenge. Traditional ecotoxicological endpoints are often not sensitive enough to detect the effects of toxicants at environmentally relevant concentrations (≤ng/L). A potential solution is using molecular biology methods to look at small biochemical changes caused by exposure to ng/L concentrations of contaminants. This has been tested in the lab but not conclusively demonstrated in the field. We exposed the freshwater amphipod (Austrochiltonia subtenuis) to water from an urban wetland containing known concentrations of per-and polyfluoroalkyl substances (as well as very low concentrations of pesticides) for 14 days and analyzed their metabolite profiles. Mannose, Myo-inositol, and Isopropyl propionate were found to change in PFAS exposed amphipods, a similar response to that previously observed in laboratory exposures to the same PFAS, but not pesticides. The results give a better understanding of PFAS toxicity at environmentally relevant concentrations and conditions.

Potential nutritional and functional matters in yeast culture prepared by soybean meal fermentation

BACKGROUND

Yeast culture (YC) is a product fermented on a specific medium, which is a type of postbiotic of anaerobic solid-state fermentation. Although YC has positive effects on the animal growth and health, it contains a variety of beneficial metabolites as dark matter, which have not been quantified. In the present study, liquid chromatography-tandem mass spectrometry is employed to identify the unknown metabolites. Following their identification, the important chemicals are quantified using HPLC-diode array detection methods.

RESULTS

Non-targeted metabolomics studies showed that 670 metabolites in total were identified in YC, of which 23 metabolites significantly increased, including organic acids, amino acids, nucleosides and purines, isoflavones, and other substances. The chemical quantitative analysis showed that the contents of succinic acid, aminobutyric acid, glutamine, purine and daidzein increased by 84.42%, 51.07%, 100%, 68.85% and 4.60%, respectively.

CONCLUSION

Therefore, the use of non-targeted metabolomics combined with chemical quantitative analysis to reveal the nutritional and functional substances of YC could help to elucidate the postbiotic mechanism and provide theoretical support for the regulation of the directional accumulation of beneficial metabolites. © 2024 Society of Chemical Industry.

Exploration of the anthocyanin and proanthocyanidin profile of Greek red grape skins belonging to Vradiano, Limnio, and Kotsifali cultivars, analyzed by a novel LC-QTOF-MS/MS method

INTRODUCTION

Winegrape varieties Kotsifali, Limnio, and Vradiano OBJECTIVE: The aim of this study was to develop a liquid chromatographic quadrupole time-of-flight tandem mass spectrometric (LC-QTOF-MS/MS) method for the investigation of the anthocyanin and proanthocyanidin content of Greek grape varieties employing target and suspect screening strategies.

METHODOLOGY

A novel LC-QTOF-MS/MS method was developed and validated to assess the anthocyanin content of Kotsifali, Limnio, and Vradiano grape varieties. Sixteen grape samples were collected from the main growing areas of each variety in Greece. The influence of the grape variety on the anthocyanin and proanthocyanidin composition of three Greek winegrapes was investigated using chemometrics.

RESULTS

Excellent linearity (R2 > 0.99) was achieved for all the target analytes, and recoveries ranged between 90.1% and 119.1%. The limits of quantification (LOQs) and limits of detection (LODs) were calculated over the range of 0.020-0.40 mg/g and 0.010-0.13 mg/g, respectively. The RSD% was lower than 9.1% and 7.3% for intra-day and inter-day studies, respectively, indicating satisfactory trueness and precision. Target and suspect screening resulted in the identification of 5 and 26 anthocyanins, respectively.

CONCLUSIONS

Kotsifali variety exhibited a higher concentration of anthocyanins compared with Vradiano and Limnio. Higher levels of mean degree of polymerization (mDp) and different percentage levels of prodelphinidins (%P) were established among the varieties.

Quantifying chemical correlations between fruits and processed fruit products: A non-targeted analysis approach

Juices and beverages are produced by industry for long-distance distribution and shelf-stability, providing valuable nutrients. However, their nutritional value is often underestimated due to insufficient analytical methods. We have employed non-targeted analysis through a standardized analytical protocol, taking advantage of Data Independent Acquisition (DIA) technique and a novel Chromatographic Retention Behavior (CRB) data deconvolution algorithm. After analyzing 9 fruits and their products, correlations between fruits and their juices are accurately digitalized by similarities of their LC-MS fingerprints. We also specify non-targeted molecules primarily associate with nutrient loss in these analyzed juice products, including nitrogenous nutrients, flavonoids, glycosides, and vitamins. Moreover, we unveiled previously unreported fruit-characteristic metabolites, of which reconstituted-from-concentrate (RFC) juices contain over 40% of the content found in their fresh counterparts. Conclusively, our method establishes a quantitative benchmark for rational selection of RFC juices to substitute natural fruits.

Identification of lymph node adulteration in minced pork by comprehensive metabolomics and lipidomics approach based on UPLC/LTQ-Orbitrap MS

The deliberate pork adulteration with lymph nodes is a common adulteration phenomenon, and it poses a serious threat to public health and food safety. An untargeted metabolomics and lipidomics approach based on ultrahigh performance liquid chromatography coupled with linear ion trap quadrupole-Orbitrap high resolution mass spectrometry (MS) was used to distinguish lymph nodes from minced pork. The principal component analysis and orthogonal projection to latent structures discriminant analysis models were established with the good of fitness and predictivity. The results showed that there were significant differences in metabolites and lipids between lymph nodes and pork. A total of 16 significantly differentiated metabolites were identified, of which 1-palmitoylglycerophosphocholine, 12,13-dihydroxy-9-octadecenoic acid, and prostaglandin E2 (PGE2) were positively correlated with lymph node content and were identified as potential markers of lymph nodes. These three markers were combined to create a binary logistic regression model, and a combined-factor exceeding 0.75 was ultimately identified as a marker for pork adulteration with lymph nodes. The desorption electrospray ionization-MS images showed that PGE2 had a higher relative abundance in the lymph node region than in adjacent non-lymph node regions, indicating that PGE2 was a marker that contributed significantly for identifying lymph nodes adulteration into pork. Our results provide a theoretical basis for identifying lymph node adulteration, which will contribute to combating fraud in the meat industry.

A proof-of-concept study on the versatility of liquid chromatography coupled to high-resolution mass spectrometry to screen for various contaminants and highlight markers of floral and geographical origin for different honeys

The high-resolution mass spectrometry is a powerful analytical tool for improving food safety and authenticity, but still underused in official control laboratories. The present work is a proof-of-concept study overviewing how liquid-chromatography coupled to high-resolution mass spectrometry could be used simultaneously for large-scale screening of contaminants and differentiation of honey samples. Within this study, the samples were extracted using all-in-one QuEChERS-based protocol that allowed for analysis of various anthropogenic contaminants and endogenous compounds. First, targeted-analysis of 52 honey samples led to unequivocal identification of 23 chemicals, including neonicotinoids, triazole fungicides and synergist. Then, suspect-screening using MSDial software allowed for tentative identification of 30 chemicals including plasticizers, flame-retardants and additives. Suspect-screening also made it possible to highlight tentative markers of chestnut honey (deoxyvasicinone, 2-quinolone, indoleacrylic acid and kynurenic acid) and citrus honey (caffeine, 2-oxindole and indole-3-carbinol). Lastly, non-targeted analysis enabled to separate honeys by their type, floral and geographical origins.

Effect of low-voltage electrostatic field-assisted partial freezing on large yellow croaker protein properties and metabolomic analysis during storage

BACKGROUND

Large yellow croaker is highly perishable during storage because of high protein and moisture content. The degradation of the fish is mainly attributed to microbial growth and enzyme activity, so it is important to find an efficient storage method to extend its shelf life.

METHODOLOGY

This study investigated the effect of a low-voltage electrostatic field combined with partial freezing treatment on the physicochemical properties of myofibrillar protein (MP) and metabolomic analysis of large yellow croaker during preservation. The samples in chilled storage (C), partial freezing storage (PF) and 6 kV/m low-voltage electrostatic field partial freezing storage (LVEF-PF) were analyzed during an 18 day storage period.

RESULTS

In comparison with the C and PF groups, LVEF-PF delayed the oxidation of MP by inhibiting the formation of carbonyl groups (2.25 nmol/mg pro), and maintaining higher sulfhydryl content (29.73 nmol/mg pro). Fourier transform infrared (FTIR) spectroscopy and fluorescence spectroscopy analysis also demonstrated that the LVEF-PF treatment maintained the stability of the protein structure by increasing the a-helix ratio (19.88%) and reducing the random coil ratio (17.83%). Scanning electron microscopy showed that, compared with the LVEF-PF group, there was more degeneration and aggregation of MP in the C and PF groups after 18 days' storage. The results of untargeted metabolomic analysis showed that 415 kinds of differential metabolites were identified after storage, and the difference levels of differential metabolites were least between the samples treated with LVEF-PF stored on the ninth day and the fresh samples. The main differential metabolic pathways during storage were amino acid metabolism and lipid metabolism.

CONCLUSION

The LVEF-PF treatment could maintain the stability of myofibrillar protein in large yellow croaker during storage. These results showed a potential application of the LVEF-PF method for aquatic product preservation. © 2023 Society of Chemical Industry.

Development of an Untargeted LC-MS Metabolomics Method with Postcolumn Infusion for Matrix Effect Monitoring in Plasma and Feces

Untargeted metabolomics based on reverse phase LC-MS (RPLC-MS) plays a crucial role in biomarker discovery across physiological and disease states. Standardizing the development process of untargeted methods requires paying attention to critical factors that are under discussed or easily overlooked, such as injection parameters, performance assessment, and matrix effect evaluation. In this study, we developed an untargeted metabolomics method for plasma and fecal samples with the optimization and evaluation of these factors. Our results showed that optimizing the reconstitution solvent and sample injection amount was critical for achieving the balance between metabolites coverage and signal linearity. Method validation with representative stable isotopically labeled standards (SILs) provided insights into the analytical performance evaluation of our method. To tackle the issue of the matrix effect, we implemented a postcolumn infusion (PCI) approach to monitor the overall absolute matrix effect (AME) and relative matrix effect (RME). The monitoring revealed distinct AME and RME profiles in plasma and feces. Comparing RME data obtained for SILs through postextraction spiking with those monitored using PCI compounds demonstrated the comparability of these two methods for RME assessment. Therefore, we applied the PCI approach to predict the RME of 305 target compounds covered in our in-house library and found that targets detected in the negative polarity were more vulnerable to the RME, regardless of the sample matrix. Given the value of this PCI approach in identifying the strengths and weaknesses of our method in terms of the matrix effect, we recommend implementing a PCI approach during method development and applying it routinely in untargeted metabolomics.

Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) Fingerprinting and Chemometrics for Coffee Classification and Authentication

Nowadays, the quality of natural products is an issue of great interest in our society due to the increase in adulteration cases in recent decades. Coffee, one of the most popular beverages worldwide, is a food product that is easily adulterated. To prevent fraudulent practices, it is necessary to develop feasible methodologies to authenticate and guarantee not only the coffee's origin but also its variety, as well as its roasting degree. In the present study, a C18 reversed-phase liquid chromatography (LC) technique coupled to high-resolution mass spectrometry (HRMS) was applied to address the characterization and classification of Arabica and Robusta coffee samples from different production regions using chemometrics. The proposed non-targeted LC-HRMS method using electrospray ionization in negative mode was applied to the analysis of 306 coffee samples belonging to different groups depending on the variety (Arabica and Robusta), the growing region (e.g., Ethiopia, Colombia, Nicaragua, Indonesia, India, Uganda, Brazil, Cambodia and Vietnam), and the roasting degree. Analytes were recovered with hot water as the extracting solvent (coffee brewing). The data obtained were considered the source of potential descriptors to be exploited for the characterization and classification of the samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). In addition, different adulteration cases, involving nearby production regions and different varieties, were evaluated by pairs (e.g., Vietnam Arabica-Vietnam Robusta, Vietnam Arabica-Cambodia and Vietnam Robusta-Cambodia). The coffee adulteration studies carried out with partial least squares (PLS) regression demonstrated the good capability of the proposed methodology to quantify adulterant levels down to 15%, accomplishing calibration and prediction errors below 2.7% and 11.6%, respectively.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Exploring Human Metabolome after Wine Intake-A Review

Wine has a rich history dating back to 2200 BC, originally recognized for its medicinal properties. Today, with the aid of advanced technologies like metabolomics and sophisticated analytical techniques, we have gained remarkable insights into the molecular-level changes induced by wine consumption in the human organism. This review embarks on a comprehensive exploration of the alterations in human metabolome associated with wine consumption. A great number of 51 studies from the last 25 years were reviewed; these studies systematically investigated shifts in metabolic profiles within blood, urine, and feces samples, encompassing both short-term and long-term studies of the consumption of wine and wine derivatives. Significant metabolic alterations were observed in a wide variety of metabolites belonging to different compound classes, such as phenolic compounds, lipids, organic acids, and amino acids, among others. Within these classes, both endogenous metabolites as well as diet-related metabolites that exhibited up-regulation or down-regulation following wine consumption were included. The up-regulation of short-chain fatty acids and the down-regulation of sphingomyelins after wine intake, as well as the up-regulation of gut microbial fermentation metabolites like vanillic and syringic acid are some of the most important findings reported in the reviewed literature. Our results confirm the intact passage of certain wine compounds, such as tartaric acid and other wine acids, to the human organism. In an era where the health effects of wine consumption are of growing interest, this review offers a holistic perspective on the metabolic underpinnings of this centuries-old tradition.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Mass spectrometry-based approaches to assess the botanical authenticity of dietary supplements

Dietary supplements are legally considered foods despite frequently including medicinal plants as ingredients. Currently, the consumption of herbal dietary supplements, also known as plant food supplements (PFS), is increasing worldwide and some raw botanicals, highly demanded due to their popularity, extensive use, and/or well-established pharmacological effects, have been attaining high prices in the international markets. Therefore, botanical adulteration for profit increase can occur along the whole PFS industry chain, from raw botanicals to plant extracts, until final PFS. Besides the substitution of high-value species, unintentional mislabeling can happen in morphologically similar species. Both cases represent a health risk for consumers, prompting the development of numerous works to access botanical adulterations in PFS. Among different approaches proposed for this purpose, mass spectrometry (MS)-based techniques have often been reported as the most promising, particularly when hyphenated with chromatographic techniques. Thus, this review aims at describing an overview of the developments in this field, focusing on the applications of MS-based techniques to targeted and untargeted analysis to detect botanical adulterations in plant materials, extracts, and PFS.

Recent Review on Selected Xenobiotics and Their Impacts on Gut Microbiome and Metabolome

As it is well known, the gut is one of the primary sites in any host for xenobiotics, and the many microbial metabolites responsible for the interactions between the gut microbiome and the host. However, there is a growing concern about the negative impacts on human health induced by toxic xenobiotics. Metabolomics, broadly including lipidomics, is an emerging approach to studying thousands of metabolites in parallel. In this review, we summarized recent advancements in mass spectrometry (MS) technologies in metabolomics. In addition, we reviewed recent applications of MS-based metabolomics for the investigation of toxic effects of xenobiotics on microbial and host metabolism. It was demonstrated that metabolomics, gut microbiome profiling, and their combination have a high potential to identify metabolic and microbial markers of xenobiotic exposure and determine its mechanism. Further, there is increasing evidence supporting that reprogramming the gut microbiome could be a promising approach to the intervention of xenobiotic toxicity.

A comparison of high- and low-resolution gas chromatography-mass spectrometry for herbal product classification: A case study with Ocimum essential oils

INTRODUCTION

Selection of marker compounds for targeted chemical analysis is complicated when considering varying instrumentation and closely related plant species. High-resolution gas chromatography-mass spectrometry (GC-MS), via orbitrap detection, has yet to be evaluated for improved marker compound selection.

OBJECTIVE

This study directly compares high- and low-resolution GC-MS for botanical maker compound selection using Ocimum tenuiflorum L. (OT) and Ocimum gratissimum L. (OG) for botanical ingredient authentication.

METHODS

The essential oils of OT and OG were collected via hydrodistillation before untargeted chemical analysis with gas chromatography coupled to single-quadrupole (GC-SQ) and orbitrap (GC-Orbitrap) detectors. The Global Natural Products Social Molecular Networking (GNPS) software was used for compound annotation, and a manual search was used to find the 41 most common Ocimum essential oil metabolites.

RESULTS

The GC-Orbitrap resulted in 1.7-fold more metabolite detection and increased dynamic range compared to the GC-SQ. Spectral matching and manual searching were improved with GC-Orbitrap data. Each instrument had differing known compound concentrations; however, there was an overlap of six compounds with higher abundance in OG than OT and three compounds with a higher abundance in OT than OG, suggesting consistent detection of the most variable compounds. An unsupervised principal component analysis (PCA) could not discern the two species with either dataset.

CONCLUSION

GC-Orbitrap instrumentation improves compound detection, dynamic range, and feature annotation in essential oil analysis. However, considering both high- and low-resolution data may improve reliable marker compound selection, as GC-Orbitrap analysis alone did not improve unsupervised separation of two Ocimum species compared to GC-SQ data.

Screening strategy for predominant phenolic components of digestive enzyme inhibitors in passion fruit peel extracts on simulated gastrointestinal digestion

BACKGROUND

The targeted biological activity of a natural product is often the result of the combined action of multiple functional components. Screening for predominant contributing components of targeting activity is crucial for quality evaluation.

RESULTS

Thirteen and nine phenolic compounds inhibiting α-glucosidase and α-amylase, respectively, were identified in the ethanol extracts of passion fruit peel through liquid chromatography-tandem mass spectrometry and multivariate analysis. Considering the different concentrations of components and their interactions, the role of the semi-inhibitory concentration (IC₅₀ ) in the dose-effect relationship is limited. We proposed the active contribution rate (ACR), which is the ratio of a single component concentration to its IC₅₀ in the whole, to assess the relative activity of each compound. Luteolin, quercetin, and vitexin exhibited a minimum IC₅₀ . Before the simulation of gastrointestinal digestion, quercetin, salicylic acid, and luteolin were identified as the dominant contributors to α-glucosidase inhibition according to ACR, while salicylic acid, 2,3-dihydroxybenzoic acid, and quercetin were identified as dominant contributors to α-amylase inhibition. After simulated digestion, the contents of all polyphenolic compounds decreased by various degrees. Salicylic acid, gentisic acid, and vitexin became the dominant inhibitors of α-glucosidase based on ACR (cumulative 57.96%), while salicylic acid and 2,3-dihydroxybenzoic acid became the dominant inhibitors of α-amylase (cumulative 84.50%).

CONCLUSION

Therefore, the ACR evaluation strategy can provide a quantitative reference for screening the predominant contributor components of a specific activity in complex systems. © 2022 Society of Chemical Industry.

Application of UPLC-Triple TOF-MS/MS metabolomics strategy to reveal the dynamic changes of triterpenoid saponins during the decocting process of Asian ginseng and American ginseng

Triterpenoid saponins are the main bioactive components contributed to the nutritional value of ginseng, and different process conditions will affect their content and quality. To study the holistic characterization and dynamic changes of triterpenoid saponins in Asian ginseng (ASG) and American ginseng (AMG) during soaking and decoction, a UPLC-Triple TOF-MS/MS-based metabolomics strategy was used to characterize and discover differential saponin markers. In total, 739 triterpenoid saponins (including 225 potential new saponins) were identified from ASG and AMG in untargeted metabolomics. Based on PCA and OPLS-DA, 51 and 48 saponin markers were screened from soaked and decocted ASG and AMG, respectively. Additionally, targeted metabolomics analysis and HCA of 22 ginsenoside markers suggested that decoction of ASG and AMG for 2 h to 4 h could significantly increase the contents of rare ginsenosides (G), such as G-Rg₃, G-Rg₅, G-F₄. This study provides a scientific insight that high boiling combined with simmering enriches ASG and AMG extracts with rich rare ginsenosides that are more beneficial to human health.

Development and application of a data processing method for food metabolomics analysis

Food metabolomics is described as the implementation of metabolomics to food systems such as food materials, food processing, and food nutrition. These applications generally create large amounts of data, and although technologies exist to analyze these data and different tools exist for various ecosystems, downstream analysis is still a challenge and the tools are not integrated into a single method. In this article, we developed a data processing method for untargeted LC-MS data in metabolomics, derived from the integration of computational MS tools from OpenMS into the workflow system Konstanz Information Miner (KNIME). This method can analyze raw MS data and produce high-quality visualization. A MS1 spectra-based identification, two MS2 spectra-based identification workflows and a GNPSExport-GNPS workflow are included in this method. Compared with conventional approaches, the results of MS1&MS2 spectra-based identification workflows are combined in this approach via the tolerance of retention times and mass to charge ratios (m/z), which can greatly reduce the rate of false positives in metabolomics datasets. In our example, filtering with the tolerance removed more than 50% of the possible identifications while retaining 90% of the correct identification. The results demonstrated that the developed method is a rapid and reliable method for food metabolomics data processing.

Applications of multi-omics techniques to unravel the fermentation process and the flavor formation mechanism in fermented foods

Fermented foods are important components of the human diet. There is increasing awareness of abundant nutritional and functional properties present in fermented foods that arise from the transformation of substrates by microbial communities. Thus, it is significant to unravel the microbial communities and mechanisms of characteristic flavor formation occurring during fermentation. There has been rapid development of high-throughput and other omics technologies, such as metaproteomics and metabolomics, and as a result, there is growing recognition of the importance of integrating these approaches. The successful applications of multi-omics approaches and bioinformatics analyses have provided a solid foundation for exploring the fermentation process. Compared with single-omics, multi-omics analyses more accurately delineate microbial and molecular features, thus they are more apt to reveal the mechanisms of fermentation. This review introduces fermented foods and an overview of single-omics technologies - including metagenomics, metatranscriptomics, metaproteomics, and metabolomics. We also discuss integrated multi-omics and bioinformatic analyses and their role in recent research progress related to fermented foods, as well as summarize the main potential pathways involved in certain fermented foods. In the future, multilayered analyses of multi-omics data should be conducted to enable better understanding of flavor formation mechanisms in fermented foods.

Untargeted Metabolomics Using UHPLC-HRMS Reveals Metabolic Changes of Fresh-Cut Potato during Browning Process

Surface browning plays a major role in the quality loss of fresh-cut potatoes. Untargeted metabolomics were used to understand the metabolic changes of fresh-cut potato during the browning process. Their metabolites were profiled by ultra-high performance liquid chromatography coupled with high resolution mass spectrometry (UHPLC-HRMS). Data processing and metabolite annotation were completed by Compound Discoverer 3.3 software. Statistical analysis was applied to screen the key metabolites correlating with browning process. Fifteen key metabolites responsible for the browning process were putatively identified. Moreover, after analysis of the metabolic causes of glutamic acid, linolenic acid, glutathione, adenine, 12-OPDA and AMP, we found that the browning process of fresh-cut potatoes was related to the structural dissociation of the membrane, oxidation and reduction reaction and energy shortage. This work provides a reference for further investigation into the mechanism of browning in fresh-cut products.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Untargeted metabolomics analysis based on LC-IM-QTOF-MS for discriminating geographical origin and vintage of Chinese red wine

Identifying wine geographical origin and vintage is vital due to the abundance of fraudulent activity associated with wine mislabeling of region and vintage. In this study, an untargeted metabolomic approach based on liquid chromatography/ion mobility quadrupole time-of-flight mass spectrometry (LC-IM-QTOF-MS) was used to discriminate wine geographical origin and vintage. Wines were well discriminated according to region and vintage with orthogonal partial least squares-discriminant analysis (OPLS-DA). The differential metabolites subsequently were screened by OPLS-DA with pairwise modeling. 42 and 48 compounds in positive and negative ionization modes were screened as differential metabolitesfor the discrimination of different wine regions, and 37 and 35 compounds were screened for wine vintage. Furthermore, new OPLS-DA models were performed using these compounds, and the external verification trial showed excellent practicality with an accuracy over 84.2%. This study indicated that LC-IM-QTOF-MS-based untargeted metabolomics was a feasible tool for wine geographical origin and vintage discrimination.

Untargeted metabolomics approach using UHPLC-IMS-QTOF MS for surface body samples to identify low-volatility chemosignals related to maternal care in mice

The present study is focused on the determination of low-volatile chemosignals excreted or secreted by mouse pups in their early days of life involved in maternal care induction in mice adult females. Untargeted metabolomics was employed to differentiate between samples collected with swabs from facial and anogenital area from neonatal mouse pups receiving maternal care (first two weeks of life) and the elder mouse pups in the weaning period (4th week old). The sample extracts were analysed by ultra-high pressure liquid chromatography (UHPLC) coupled to ion mobility separation (IMS) in combination with high resolution mass spectrometry (HRMS). After data processing with Progenesis QI and multivariate statistical analysis, five markers present in the first two weeks of mouse pups life and putatively involved in materno-filial chemical communication were tentatively identified: arginine, urocanic acid, erythro-sphingosine (d17:1), sphingosine (d18:1) and sphinganine. The four-dimensional data and the tools associated to the additional structural descriptor obtained by IMS separation were of great help in the compound identification. The results demonstrated the great potential of UHPLC-IMS-HRMS based untargeted metabolomics to identity putative pheromones in mammals.

Is substrate choice an overlooked variable in ecotoxicology experiments?

It is crucial to understand the effects caused by experimental parameters such as temperature, light, and food type on lab and field-based ecotoxicology experiments, as these variables, and combinations thereof, can affect results. The type of substrate used in exposure experiments, however, is generally assumed to have no effect. This may not always be correct. The metabolic changes in the freshwater crustacean, Austrochiltonia subtenuis exposed to copper, using three common substrates, gauze; toilet paper; and cellulose were investigated. Substrate alone did not affect survival, but each substrate elicited a different metabolic response and adult and juvenile amphipods had different substrate preferences. Several classes of metabolites were shown to change in response to different substrates and toxicant. These included disaccharides, monosaccharides, fatty acids, and tricarboxylic acid cycle intermediates. The results illustrate that metabolomic responses can differ in response to experimental factors that were previously thought not to be significant. In fact, our data indicate that substrate should be viewed as an experimental factor as important to control for as more well-known confounders such as temperature or food, thus challenging the current paradigm. Assuming substrate type has no effect on the experiment could potentially lead to errors in contaminant toxicity assessments. We propose that ideal good practise would be that all experimental factors should be evaluated for their potential influence on metabolomic profiles prior to contaminant response experiments being undertaken.

Pseudotargeted Metabolomics Approach Enabling the Classification-Induced Ginsenoside Characterization and Differentiation of Ginseng and Its Compound Formulation Products

The use of diversified ginseng extracts in health-promoting foods is difficult to differentiate, as they share bioactive ginsenosides among different Panax species (e.g., P. ginseng, P. quinquefolius, P. notoginseng, and P. japonicus) and different parts (e.g., root, leaf, and flower). This work was designed to develop a pseudo-targeted metabolomics approach to discover ginsenoside markers facilitating the precise authentication of ginseng and its use in compound formulation products (CFPs). Versatile mass spectrometry experiments on the QTrap mass spectrometer achieved classified characterization of the neutral, malonyl, and oleanolic acid-type ginsenosides, with 567 components characterized. A pseudo-targeted metabolomics approach by multiple reaction monitoring (MRM) of 262 ion pairs could assist to establish key identification points for 12 ginseng species. The simultaneous detection of 14 markers enabled the identification of ginseng from 15 ginseng-containing CFPs. The pseudo-targeted metabolomics strategy enabled better performance in differentiating among multiple ginseng, compared with the full-scan high-resolution mass spectrometry approach.

Metabolomics during the spontaneous fermentation in cocoa (Theobroma cacao L.): An exploraty review

Spontaneous fermentation is a process that depends on substrates' physical characteristics, crop variety, and postharvest practices; it induces variations in the metabolites that are responsible for the taste, aroma, and quality. Metabolomics makes it possible to detect key metabolites using chemometrics and makes it possible to establish patterns or identify biomarker behaviors under certain conditions at a given time. Therefore, sensitive and highly efficient analytical techniques allow for studying the metabolomic fingerprint changes during fermentation; which identify and quantify metabolites related to taste and aroma formation of an adequate processing time. This review shows that studying metabolomics in spontaneous fermentation permits the characterization of spontaneous fermentation in different stages. Also, it demonstrates the possibility of modulating the quality of cocoa by improving the spontaneous fermentation time (because of volatile aromatic compounds formation), thus standardizing the process to obtain attributes and quality that will later impact the chocolate quality.

Discovery of Food Intake Biomarkers Using Metabolomics

Due to the high impact of diet exposure on health, it is crucial the generation of robust data of regular dietary intake, hence improving the accuracy of dietary assessment. The metabolites derived from individual food or group of food have great potential to become biomarkers of food intake (BFIs) and provide more objective food consumption measurements.Herein, it is presented an untargeted metabolomic workflow for the discovery BFIs in blood and urine samples, from the study design to the biomarker identification. Samples are analyzed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). A wide variety of compounds are covered by separate analyses of medium to nonpolar molecules and polar metabolites based on two LC separations as well as both positive and negative electrospray ionization. The main steps of data treatment of the comprehensive data sets and statistical analysis are described, as well as the principal considerations for the BFI identification.

Untargeted Metabolomics Reveals New Markers of Food Processing for Strawberry and Apple Purees

In general, food processing and its conditions affect nutrients, bioactive compounds, and sensory characteristics of food products. This research aims to use a non-targeted metabolomics approach based on UPLC-ESI-QTOF-MS to determine how fruit processing can affect the metabolic profile of fruits and, through a comprehensive metabolic analysis, identify possible markers to assess their degree of processing. The present study uses a real case from the food industry to evaluate markers of the processing of strawberry and apple purees industrially elaborated with different processing techniques and conditions. The results from the multivariate analysis revealed that samples were grouped according to the type of processing, evidencing changes in their metabolic profiles and an apparent temperature-dependent effect. These metabolic profiles showed changes according to the relevance of thermal conditions but also according to the exclusively cold treatment, in the case of strawberry puree, and the pressure treatment, in the case of apple puree. After data analysis, seven metabolites were identified and proposed as processing markers: pyroglutamic acid, pteroyl-D-glutamic acid, 2-hydroxy-5-methoxy benzoic acid, and 2-hydroxybenzoic acid β-d-glucoside in strawberry and di-hydroxycinnamic acid glucuronide, caffeic acid and lysoPE(18:3(9Z,12Z,15Z)/0:0) in apple purees. The use of these markers may potentially help to objectively measure the degree of food processing and help to clarify the controversial narrative on ultra-processed foods.

Untargeted Metabolomics reveals Doenjang metabolites affected by manufacturing process and microorganisms

Doenjang is a traditional Korean fermented soybean-based food that is now produced industrially as traditional Doenjang and modernized Doenjang, depending on the conditions of the manufacturing process. In this study, the effect of the production process on Doenjang metabolites was analyzed using untargeted capillary electrophoresis time-of-flight mass spectrometry (CE-TOF/MS). Of the 247 metabolites detected in 19 traditional and three modernized Doenjang samples, the level of 55 metabolites were significantly different between the two production groups. The S-plot of the orthogonal projection to latent structures-discriminant analysis (OPLS-DA) revealed that nine compounds (tryptamine, 2-phenylethylamine, citrulline, gamma-aminobutyric acid, putrescine, tyramine, 2-aminoisobutyric acid, stachydrine, and N5-ethylglutamine) were highly distributed in the traditional group, and six compounds (arginine, citric acid, choline, cytidine, hypoxanthine, and glucaric acid) were considered distinguishable metabolites of the modernized group. Microbial community analysis indicated that the levels of these metabolites were significantly altered by the presence of Bacillus spp., Enterococcus faecium, Tetragenococcus halophilus, Aspergillus oryzae, Penicillium spp., and Saccharomyces cerevisiae. These findings will give better understanding of the influence of the manufacturing process on Doenjang production in terms of both microbial activity and metabolite profiles.

Untargeted metabolomic analysis of Chinese red wines for geographical origin traceability by UPLC-QTOF-MS coupled with chemometrics

Identifying geographical origins of red wines made in specific regions is of significance since the false claim of geographical origins has been frequently exposed in China's wine industry. In this work, an untargeted metabolomic approach based on UPLC-QTOF-MS was established to discriminate geographical origins of Chinese red wines. The principal component analysis (PCA) showed significant differences between wine samples from three famous geographical origins in China. The metabolites contributing to the differentiation were screened by orthogonal partial least squares-discriminant analysis (OPLS-DA) with pairwise modeling. 40 and 46 differential metabolites in positive and negative ionization modes were putatively identified as chemical markers. Furthermore, heatmap visualization and OPLS-DA models were constructed based on these identified markers and external verification wine samples from different regions were successfully discriminated, with recognition rate up to 96.7%. This study indicated that UPLC-QTOF-MS-based untargeted metabolomics has great potential for the geographical origin traceability of Chinese red wines.

Impact of the Cooking Process on Metabolite Profiling of Acanthocereus tetragonus, a Plant Traditionally Consumed in Mexico

Acanthocereus tetragonus (L.) Hummelinck is used as an alternative food source in some Mexican communities. It has been shown that the young stems of A. tetragonus provide crude protein, fiber, and essential minerals for humans. In this work, we analyzed the phytochemical profile, the total phenolic content (TPC), and the antioxidant activity of cooked and crude samples of A. tetragonus to assess its functional metabolite contribution to humans. The phytochemical profile was analyzed using Ultra-High-Performance Liquid Chromatography coupled to High-Resolution Mass Spectrometry (UHPLC-PDA-HESI-Orbitrap-MS/MS). Under the proposed conditions, 35 metabolites were separated and tentatively identified. Of the separated metabolites, 16 occurred exclusively in cooked samples, 6 in crude samples, and 9 in both crude and cooked samples. Among the detected compounds, carboxylic acids, such as threonic, citric, and malic acids, phenolic acids, and glycosylated flavonoids (luteolin-O-rutinoside) were detected. The TPC and antioxidant activity were analyzed using the Folin–Ciocalteu method and the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical inhibition method, respectively. The TPC and antioxidant activity were significantly reduced in the cooked samples. We found that some metabolites remained intact after the cooking process, suggesting that A. tetragonus represents a source of functional metabolites for people who consume this plant species.

Identification of characteristic volatiles and metabolomic pathway during pork storage using HS-SPME-GC/MS coupled with multivariate analysis

Previous researches have been conducted evaluating the volatile compounds of pork. However, data regarding the changes in volatiles and metabolic pathways during pork storage were inadequately investigated. Herein, a headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC/MS) coupled multivariate analysis was proposed for characterizing the profiles of volatile compounds and metabolic pathways during pork storage. A total of 37 metabolites, including aldehydes, ketones, alcohols etc. were successfully identified. Multivariate statistical analysis revealed a substantial variation in metabolite phenotype among samples over the pork storage period, with 12 characteristic metabolites and 5 potential characteristic metabolites screened as biomarkers. Moreover, three metabolomic pathways analysis and transformation between each other (thermal reactions, lipid metabolism and amino acid metabolism) reveals the underlying mechanisms of metabolites change of pork. Therefore, the present study may provide insight into future understanding of the variation in the pork metabolite profiles.

Characterization of Cabernet Sauvignon Wines by Untargeted HS-SPME GC-QTOF-MS

Untargeted metabolomics approaches are emerging as powerful tools for the quality evaluation and authenticity of food and beverages and have been applied to wine science. However, most fail to report the method validation, quality assurance and/or quality control applied, as well as the assessment through the metabolomics-methodology pipeline. Knowledge of Mexican viticulture, enology and wine science remains scarce, thus untargeted metabolomics approaches arise as a suitable tool. The aim of this study is to validate an untargeted HS-SPME-GC-qTOF/MS method, with attention to data processing to characterize Cabernet Sauvignon wines from two vineyards and two vintages. Validation parameters for targeted methods are applied in conjunction with the development of a recursive analysis of data. The combination of some parameters for targeted studies (repeatability and reproducibility < 20% RSD; linearity > 0.99; retention-time reproducibility < 0.5% RSD; match-identification factor < 2.0% RSD) with recursive analysis of data (101 entities detected) warrants that both chromatographic and spectrometry-processing data were under control and provided high-quality results, which in turn differentiate wine samples according to site and vintage. It also shows potential biomarkers that can be identified. This is a step forward in the pursuit of Mexican wine characterization that could be used as an authentication tool.

Comparison of chemometric strategies for potential exposure marker discovery and false-positive reduction in untargeted metabolomics: application to the serum analysis by LC-HRMS after intake of Vaccinium fruit supplements

Untargeted liquid chromatographic-high-resolution mass spectrometric (LC-HRMS) metabolomics for potential exposure marker (PEM) discovery in nutrikinetic studies generates complex outputs. The correct selection of statistically significant PEMs is a crucial analytical step for understanding nutrition-health interactions. Hence, in this paper, different chemometric selection workflows for PEM discovery, using multivariate or univariate parametric or non-parametric data analyses, were comparatively tested and evaluated. The PEM selection protocols were applied to a small-sample-size untargeted LC-HRMS study of a longitudinal set of serum samples from 20 volunteers after a single intake of (poly)phenolic-rich Vaccinium myrtillus and Vaccinium corymbosum supplements. The non-parametric Games-Howell test identified a restricted group of significant features, thus minimizing the risk of false-positive retention. Among the forty-seven PEMs exhibiting a statistically significant postprandial kinetics, twelve were successfully annotated as purine pathway metabolites, benzoic and benzodiol metabolites, indole alkaloids, and organic and fatty acids, and five (i.e. octahydro-methyl-β-carboline-dicarboxylic acid, tetrahydro-methyl-β-carboline-dicarboxylic acid, citric acid, caprylic acid, and azelaic acid) were associated to Vaccinium berry consumption for the first time. The analysis of the area under the curve of the longitudinal dataset highlighted thirteen statistically significant PEMs discriminating the two interventions, including four intra-intervention relevant metabolites (i.e. abscisic acid glucuronide, catechol sulphate, methyl-catechol sulphate, and α-hydroxy-hippuric acid). Principal component analysis and sample classification through linear discriminant analysis performed on PEM maximum intensity confirmed the discriminating role of these PEMs.

Stability of Wheat Floret Metabolites during Untargeted Metabolomics Studies

A typical metabolomic analysis consists of a multi-step procedure. Variation can be introduced in any analysis segment if proper care in quality assurance is not taken, thus compromising the final results. Sample stability is one of those factors. Although sophisticated studies addressing sample decay over time have been performed in the medical field, they are emerging in plant metabolomics. Here, we focus on the stability of wheat floret extracts on queue inside an auto-injector held at 25 °C. The objective was to locate an analytical time window from extraction to injection with no significant difference occurring in the sample. Total ion current chromatograms, principal component analysis, and volcano plots were used to measure changes in the samples. Results indicate a maximum work window time of 7:45 h for Steele-ND wheat methanolic extractions in an auto-sampler at 25 °C. Comparisons showed a significant gradual increase in the number and intensity of compounds observed that may be caused by the degradation of other molecules in the sample extract. The approach can be applied as preliminary work in a metabolite profiling study, helping to set the appropriate workload to produce confident results.