Application of mass spectrometry-based metabolomics approaches for food safety, quality and traceability

Pesticide residues in animal-derived food: Current state and perspectives

Pesticides are widely used in the cultivation and breeding of agricultural products all over the world. However, their direct use or indirect pollution in animal breeding may lead to residual accumulation, migration, and metabolism in animal-derived foods, posing potential health risks to humans through the food chain. Therefore, it is necessary to detect pesticide residues in animal-derived food using simple, reliable, and sensitive methods. This review summarizes sample extraction and clean-up methods, as well as the instrumental determination technologies such as chromatography and chromatography-mass spectrometry for residual analysis in animal-derived foods, including meat, eggs and milk. Additionally, we perspectives on the future of this field. This information aims to assist relevant researchers in this area, contribute to the development of ideas and novel technical methods for residual detection, metabolic research and risk assessment of pesticides in animal-derived food.

Recent Studies and Applications of Hydrogel-Based Biosensors in Food Safety

Food safety has increasingly become a human health issue that concerns all countries in the world. Some substances in food that can pose a significant threat to human health include, but are not limited to, pesticides, biotoxins, antibiotics, pathogenic bacteria, food quality indicators, heavy metals, and illegal additives. The traditional methods of food contaminant detection have practical limitations or analytical defects, restricting their on-site application. Hydrogels with the merits of a large surface area, highly porous structure, good shape-adaptability, excellent biocompatibility, and mechanical stability have been widely studied in the field of food safety sensing. The classification, response mechanism, and recent application of hydrogel-based biosensors in food safety are reviewed in this paper. Furthermore, the challenges and future trends of hydrogel biosensors are also discussed.

Research advances of SERS analysis method based on silent region molecules for food safety detection

Food safety is a critical issue that is closely related to people's health and safety. As a simple, rapid, and sensitive detection technique, surface-enhanced Raman scattering (SERS) technology has significant potential for food safety detection. Recently, researchers have shown a growing interest in utilizing silent region molecules for SERS analysis. These molecules exhibit significant Raman scattering peaks in the cellular Raman silent region between 1800 and 2800 cm-1 avoiding overlapping with the SERS spectrum of biological matrices in the range 600-1800 cm-1, which could effectively circumvent matrix effects and improve the SERS accuracy. In this review, the application of silent region molecules-based SERS analytical technique for food safety detection is introduced, detection strategies including label-free detection and labeled detection are discussed, and recent applications of SERS analysis technology based on molecules containing alkyne and nitrile groups, as well as Prussian blue (PB) in the detection of pesticides, mycotoxins, metal ions, and foodborne pathogens are highlighted. This review aims to draw the attention to the silent region molecules-based SERS analytical technique and to provide theoretical support for its further applications in food safety detection.

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.

LC-IMS-HRMS for identification of biomarkers in untargeted metabolomics: The effects of pterostilbene and resveratrol consumption in liver steatosis, animal model

Untargeted metabolomics with the combination of ion mobility separation coupled to high resolution mass spectrometry (IMS-HRMS) was applied to investigate the impact of resveratrol and pterostilbene supplementation on the metabolic fingerprint of the Wistar rats liver with induced liver steatosis. RP-LC and HILIC in both ionisation modes were employed to analyse the liver samples (n = 40) from Wistar rats fed with a high-fat and high-fructose diet, supplemented or not with resveratrol and pterostilbene. After univariate and multivariate statistical analysis, 34 metabolites were highlighted in the different diets and elucidated. Despite the structural similarity, different alterations in liver metabolism were observed by the supplementations. Resveratrol treatment was characterised by the alteration in metabolism of 17 lysophospholipids, while pterostilbene affected some vitamins and derivatives, among others. IMS has demonstrated great potential in the elucidation process thanks to the additional structural descriptor the CCS (Ų), providing more confidence in the identification.

Hydrogel-integrated sensors for food safety and quality monitoring: Fabrication strategies and emerging applications

Food safety is a global issue in public hygiene. The accurate, sensitive, and on-site detection of various food contaminants performs significant implications. However, traditional methods suffer from the time-consuming and professional operation, restricting their on-site application. Hydrogels with the merits of highly porous structure, high biocompatibility, good shape-adaptability, and stimuli-responsiveness offer a promising biomaterial to design sensors for ensuring food safety. This review describes the emerging applications of hydrogel-based sensors in food safety inspection in recent years. In particular, this study elaborates on their fabrication strategies and unique sensing mechanisms depending on whether the hydrogel is stimuli-responsive or not. Stimuli-responsive hydrogels can be integrated with various functional ligands for sensitive and convenient detection via signal amplification and transduction; while non-stimuli-responsive hydrogels are mainly used as solid-state encapsulating carriers for signal probe, nanomaterial, or cell and as conductive media. In addition, their existing challenges, future perspectives, and application prospects are discussed. These practices greatly enrich the application scenarios and improve the detection performance of hydrogel-based sensors in food safety detection.

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.

Multi-Omics Approach in Amelioration of Food Products

Determination of the quality of food products is an essential key factor needed for safe-guarding the quality of food for the interest of the consumers, along with the nutritional and sensory improvements that are necessary for delivering better quality products. Bacteriocins are a group of ribosomally synthesized antimicrobial peptides that help in maintaining the quality of food. The implementation of multi-omics approach has been important for the overall enhancement of the quality of the food. This review uses various recent technologies like proteomics, transcriptomics, and metabolomics for the overall enhancement of the quality of food products. The matrix associated with the food products requires the use of sophisticated technologies that help in the extraction of a large amount of information necessary for the amelioration of the food products. This review would provide a wholesome view of how various recent technologies can be used for improving the quality food products and for enhancing their shelf-life.

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.

Foodomic-Based Approach for the Control and Quality Improvement of Dairy Products

The food quality assurance before selling is a needed requirement intended for protecting consumer interests. In the same way, it is also indispensable to promote continuous improvement of sensory and nutritional properties. In this regard, food research has recently contributed with studies focused on the use of 'foodomics'. This review focuses on the use of this technology, represented by transcriptomics, proteomics, and metabolomics, for the control and quality improvement of dairy products. The complex matrix of these foods requires sophisticated technology able to extract large amounts of information with which to influence their aptitude for consumption. Thus, throughout the article, different applications of the aforementioned technologies are described and discussed in essential matters related to food quality, such as the detection of fraud and/or adulterations, microbiological safety, and the assessment and improvement of transformation industrial processes (e.g., fermentation and ripening). The magnitude of the reported results may open the door to an in-depth transformation of the most conventional analytical processes, with the introduction of new techniques that allow a greater understanding of the biochemical phenomena occurred in this type of food.

Microbial Diversity and Non-volatile Metabolites Profile of Low-Temperature Sausage Stored at Room Temperature

Sausage is a highly perishable food with unique spoilage characteristics primarily because of its specific means of production. The quality of sausage during storage is determined by its microbial and metabolite changes. This study developed a preservative-free low-temperature sausage model and coated it with natural casing. We characterized the microbiota and non-volatile metabolites in the sausage after storage at 20°C for up to 12 days. Bacillus velezensis was the most prevalent species observed after 4 days. Lipids and lipid-like molecules, organoheterocyclic compounds, and organic acids and their derivatives were the primary non-volatile metabolites. The key non-volatile compounds were mainly involved in protein catabolism and β-lipid oxidation. These findings provide useful information for the optimization of sausage storage conditions.

Metabolomics: An analytical technique for food processing evaluation

This review aimed to retrieve the most recent research with strong impact concerning the application of metabolomics analysis in food processing. The literature reveals the high capacity of this methodology to evaluate chemical and organoleptic transformations that occur during food production. Current and potential applications of metabolomics analysis will be addressed, focusing on process-composition-function relationships. The use of the metabolomics approach to evaluate transformations in foods submitted to minimal processes, heat or cold treatments, drying, fermentation, chemical and enzymatic treatments and processes using innovative technologies will be discussed. Moreover, the main strategies and advantages of metabolomics-based approaches are reviewed, as well as the most used analytical platforms. Overall, metabolomics can be seen as an important tool to support academia and industry on pursuing knowledge about the transformation of raw animal or plant materials into ready-to-eat products.

Combination of untargeted metabolomics approach and molecular networking analysis to identify unique natural components in wild Morchella sp. by UPLC-Q-TOF-MS

Morchella sp. is a typical edible fungus. However, its unique natural products, especially bioactive composition, are rarely reported. In this study, an untargeted metabolomics approach combined with multivariate statistical analysis was employed to screen the differential metabolites in Morchella sp. compared to four other edible fungi. Among the screened 50-top differential metabolites, 18 compounds involving fatty acids and peptides showed higher content in Morchella sp.. A unique peptide of desferriferricrocin was identified. Subsequently, molecular networking analysis revealed three novel homologous compounds of desferriferricrocin, which were annotated as desferriferrichrome, desferriferrichrome C, and a new homolog not previously reported. These results were further verified by quantitative analysis. To the best of our knowledge, this is the first report of four peptides in Morchella sp..

Understanding the Gastrointestinal Protective Effects of Polyphenols using Foodomics-Based Approaches

Plant polyphenols are rich sources of natural anti-oxidants and prebiotics. After ingestion, most polyphenols are absorbed in the intestine and interact with the gut microbiota and modulated metabolites produced by bacterial fermentation, such as short-chain fatty acids (SCFAs). Dietary polyphenols immunomodulatory role by regulating intestinal microorganisms, inhibiting the etiology and pathogenesis of various diseases including colon cancer, colorectal cancer, inflammatory bowel disease (IBD) and colitis. Foodomics is a novel high-throughput analysis approach widely applied in food and nutrition studies, incorporating genomics, transcriptomics, proteomics, metabolomics, and integrating multi-omics technologies. In this review, we present an overview of foodomics technologies for identifying active polyphenol components from natural foods, as well as a summary of the gastrointestinal protective effects of polyphenols based on foodomics approaches. Furthermore, we critically assess the limitations in applying foodomics technologies to investigate the protective effect of polyphenols on the gastrointestinal (GI) system. Finally, we outline future directions of foodomics techniques to investigate GI protective effects of polyphenols. Foodomics based on the combination of several analytical platforms and data processing for genomics, transcriptomics, proteomics and metabolomics studies, provides abundant data and a more comprehensive understanding of the interactions between polyphenols and the GI tract at the molecular level. This contribution provides a basis for further exploring the protective mechanisms of polyphenols on the GI system.

Aspergillus derived mycotoxins in food and the environment: Prevalence, detection, and toxicity

Aspergillus species are the paramount ubiquitous fungi that contaminate various food substrates and produce biochemicals known as mycotoxins. Aflatoxins (AFTs), ochratoxin A (OTA), patulin (PAT), citrinin (CIT), aflatrem (AT), secalonic acids (SA), cyclopiazonic acid (CPA), terrein (TR), sterigmatocystin (ST) and gliotoxin (GT), and other toxins produced by species of Aspergillus plays a major role in food and human health. Mycotoxins exhibited wide range of toxicity to the humans and animal models even at nanomolar (nM) concentration. Consumption of detrimental mycotoxins adulterated foodstuffs affects human and animal health even trace amounts. Bioaerosols consisting of spores and hyphal fragments are active elicitors of bronchial irritation and allergy, and challenging to the public health. Aspergillus is the furthermost predominant environmental contaminant unswervingly defile lives with a 40-90 % mortality risk in patients with conceded immunity. Genomics, proteomics, transcriptomics, and metabolomics approaches useful for mycotoxins' detection which are expensive. Antibody based detection of toxins chemotypes may result in cross-reactivity and uncertainty. Aptamers (APT) are single stranded DNA (ssDNA/RNA), are specifically binds to the target molecules can be generated by systematic evolution of ligands through exponential enrichment (SELEX). APT are fast, sensitive, simple, in-expensive, and field-deployable rapid point of care (POC) detection of toxins, and a better alternative to antibodies.

Probabilistic topic modelling in food spoilage analysis: A case study with Atlantic salmon (Salmo salar)

Probabilistic topic modelling is frequently used in machine learning and statistical analysis for extracting latent information from complex datasets. Despite being closely associated with natural language processing and text mining, these methods possess several properties that make them particularly attractive in metabolomics applications where the applicability of traditional multivariate statistics tends to be limited. The aim of the study was thus to introduce probabilistic topic modelling - more specifically, Latent Dirichlet Allocation (LDA) - in a novel experimental context: volatilome-based (sea) food spoilage characterization. This was realized as a case study, focusing on modelling the spoilage of Atlantic salmon (Salmo salar) at 4 °C under different gaseous atmospheres (% CO₂/O₂/N₂): 0/0/100 (A), air (B), 60/0/40 (C) or 60/40/0 (D). First, an exploratory analysis was performed to optimize the model tunings and to consequently model salmon spoilage under 100% N₂ (A). Based on the obtained results, a systematic spoilage characterization protocol was established and used for identifying potential volatile spoilage indicators under all tested storage conditions. In conclusion, LDA could be used for extracting sets of underlying VOC profiles and identifying those signifying salmon spoilage, giving rise to an extensive discussion regarding the key points associated with model tuning and/or spoilage analysis. The identified compounds were well in accordance with a previously established approach based on partial least squares regression analysis (PLS). Overall, the outcomes of the study not only reflect the promising potential of LDA in spoilage characterization, but also provide several new insights into the development of data-driven methods for food quality analysis.

Comprehensive Metabolomics Analysis of Nontargeted LC-HRMS Data Provides Valuable Insights Regarding the Origin of Veterinary Drug Residues

Liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS) complements standard triple-quadrupole mass spectrometry in veterinary drug residue control. LC-HRMS offers the opportunity for nontargeted screening for metabolites and biomarkers representing metabolic changes. In this work, the feasibility of a nontargeted metabolomics approach based on LC-HRMS data (LC-Q-Orbitrap and LC-Q-TOF) to distinguish between porcine muscle tissue from infected animals and from healthy animals is demonstrated. The differences arise from various compounds associated with metabolic changes in infected animals. Two new biomarker candidates have been identified: tripeptide prolyphenylalanylglycine and a lysophosphatidylcholine derivative. For the first time, a bivariate data analysis procedure is described that may be used to evaluate whether the presence of antibiotic residues points to a therapeutic application or may be the result of a contamination during sampling and/or analysis.

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity

Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds.

Application of High Resolution Mass Spectrometric methods coupled with chemometric techniques in olive oil authenticity studies - A review

Extra Virgin Olive Oil (EVOO), the emblematic food of the Mediterranean diet, is recognized for its nutritional value and beneficial health effects. The main authenticity issues associated with EVOO's quality involve the organoleptic properties (EVOO or defective), mislabeling of production type (organic or conventional), variety and geographical origin, and adulteration. Currently, there is an emerging need to characterize EVOOs and evaluate their genuineness. This can be achieved through the development of analytical methodologies applying advanced "omics" technologies and the investigation of EVOOs chemical fingerprints. The objective of this review is to demonstrate the analytical performance of High Resolution Mass Spectrometry (HRMS) in the field of food authenticity assessment, allowing the determination of a wide range of food constituents with exceptional identification capabilities. HRMS-based workflows used for the investigation of critical olive oil authenticity issues are presented and discussed, combined with advanced data processing, comprehensive data mining and chemometric tools. The use of unsupervised classification tools, such as Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), as well as supervised classification techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Partial Least Square Discriminant Analysis (PLS-DA), Orthogonal Projection to Latent Structure-Discriminant Analysis (OPLS-DA), Counter Propagation Artificial Neural Networks (CP-ANNs), Self-Organizing Maps (SOMs) and Random Forest (RF) is summarized. The combination of HRMS methodologies with chemometrics improves the quality and reliability of the conclusions from experimental data (profile or fingerprints), provides valuable information suggesting potential authenticity markers and is widely applied in food authenticity studies.

Metabolomic approach to measuring quality of chilled chicken meat during storage

The metabolites of stored, chilled chicken meat were analyzed using liquid chromatograph-mass spectrometry and metabolomics. The results showed significant differences in the metabolites of chicken meat stored at 4°C for 0 D and meat stored for longer periods of 2 D, 4 D, 6 D, and 10 D, when analyzed based on a variable of importance >2 and P < 0.05. These changed metabolites included amino acids, amines, nucleosides, nucleotides, carbohydrates, organic acids, and other substances. The data from this study provide a holistic understanding of food quality changes in chicken meat during deterioration in storage.

UHPLC-MS metabolome based classification of umbelliferous fruit taxa: a prospect for phyto-equivalency of its different accessions and in response to roasting

Herbs of the Umbelliferae family are popular spices valued worldwide for their many nutritional and health benefits. Herein, five chief umbelliferous fruits viz., cumin, fennel, anise, coriander and caraway were assessed for its secondary metabolites diversity along with compositional changes incurring upon roasting as analyzed via ultra-high performance liquid chromatography coupled to photodiode array and electrospray ionization mass detectors UHPLC-qToF/MS. A total of 186 metabolites were annotated, according to metabolomics society guidelines, belonging mainly to flavonoids, fatty acids and phenolic acids. Multivariate models viz., PCA, HCA and OPLS-DA were further employed to assess fruits' heterogeneity in an untargeted manner and determine mechanistic changes in bioactive makeup post roasting viz., glycosidic cleavage, lipid degradation and Maillard reaction. Finally, the fruits' antioxidant activity showed decline upon roasting and in correlation with its total phenolic content. This study presents the first complete map of umbelliferous fruit metabolome, compositional differences and its roasting effect.

First metabolome map for umbelliferous fruits secondary metabolites profiling via UHPLC-QToF/MS with scrutinizing its metabolome compositional changes incurred upon roasting.

Targeted and Untargeted Metabolomics as an Enhanced Tool for the Detection of Pomegranate Juice Adulteration

Pomegranate juice is one of the most popular fruit juices, is well-known as a "superfood", and plays an important role in healthy diets. Due to its constantly growing demand and high value, pomegranate juice is often targeted for adulteration, especially with cheaper substitutes such as apple and red grape juice. In the present study, the potential of applying a metabolomics approach to trace pomegranate juice adulteration was investigated. A novel methodology based on high-resolution mass spectrometric analysis was developed using targeted and untargeted screening strategies to discover potential biomarkers for the reliable detection of pomegranate juice adulteration from apple and red grape juice. Robust classification and prediction models were built with the use of unsupervised and supervised techniques (principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA)), which were able to distinguish pomegranate juice adulteration to a level down to 1%. Characteristic m/z markers were detected, indicating pomegranate juice adulteration, and several marker compounds were identified. The results obtained from this study clearly demonstrate that Mass Spectrometry (MS)-based metabolomics have the potential to be used as a reliable screening tool for the rapid determination of food adulteration.

Current and Emerging Technologies for the Detection of Norovirus from Shellfish

Reports of norovirus infections associated with the consumption of contaminated bivalve molluscan shellfish negatively impact both consumers and commercial shellfish operators. Current virus recovery and PCR detection methods can be expensive and time consuming. Due to the lack of rapid, user-friendly and onsite/infield methods, it has been difficult to establish an effective virus monitoring regime that is able to identify contamination points across the production line (i.e., farm-to-plate) to ensure shellfish quality. The focus of this review is to evaluate current norovirus detection methods and discuss emerging approaches. Recent advances in omics-based detection approaches have the potential to identify novel biomarkers that can be incorporated into rapid detection kits for onsite use. Furthermore, some omics techniques have the potential to simultaneously detect multiple enteric viruses that cause human disease. Other emerging technologies discussed include microfluidic, aptamer and biosensor-based detection methods developed to detect norovirus with high sensitivity from a simple matrix. Many of these approaches have the potential to be developed as user-friendly onsite detection kits with minimal costs. However, more collaborative efforts on research and development will be required to commercialize such products. Once developed, these emerging technologies could provide a way forward that minimizes public health risks associated with shellfish consumption.

Simultaneous authentication of species identity and geographical origin of shrimps: Untargeted metabolomics to recurrent biomarker ions

Mandatory disclosure of the species identity, production method, and geographical origin are embedded in the regulations and traceability systems, governing international seafood trade. A high-resolution mass spectrometry-based metabolomics approach could simultaneously authenticate the species identity and geographical origin of commercially important shrimps. The highly innovative approach spared the need for multiple testing methods which are in routine use currently. A robust chemometric model, developed using the metabolite fingerprint dataset, could accurately predict the species identity of the shrimp samples. Subsequently, species-specific biomarkers were discovered and a tandem mass spectrometry method for authentication of the species was developed. Two other chemometric models from the metabolomics experiment accurately predicted the geographical origin of king prawns and tiger prawns. The study has shown for the first time that food-metabolomics along with chemometrics can simultaneously check for multiple seafood fraud issues in the global seafood supply-chain.

Metabolomics Provides Valuable Insight for the Study of Durum Wheat: A Review

Metabolomics is increasingly being applied in various fields offering a highly informative tool for high-throughput diagnostics. However, in plant sciences, metabolomics is underused, even though plant studies are relatively easy and cheap when compared to those on humans and animals. Despite their importance for human nutrition, cereals, and especially wheat, remain understudied from a metabolomics point of view. The metabolomics of durum wheat has been essentially neglected, although its genetic structure allows the inference of common mechanisms that can be extended to other wheat and cereal species. This review covers the present achievements in durum wheat metabolomics highlighting the connections with the metabolomics of other cereal species (especially bread wheat). We discuss the metabolomics data from various studies and their relationships to other "-omics" sciences, in terms of wheat genetics, abiotic and biotic stresses, beneficial microbes, and the characterization and use of durum wheat as feed, food, and food ingredient.

Quality and Authenticity Control of Fruit Juices-A Review

Food fraud, being the act of intentional adulteration of food for financial advantage, has vexed the consumers and the food industry throughout history. According to the European Committee on the Environment, Public Health and Food Safety, fruit juices are included in the top 10 food products that are most at risk of food fraud. Therefore, reliable, efficient, sensitive and cost-effective analytical methodologies need to be developed continuously to guarantee fruit juice quality and safety. This review covers the latest advances in the past ten years concerning the targeted and non-targeted methodologies that have been developed to assure fruit juice authenticity and to preclude adulteration. Emphasis is placed on the use of hyphenated techniques and on the constantly-growing role of MS-based metabolomics in fruit juice quality control area.

Phytochemical Profiling of Coryphantha macromeris (Cactaceae) Growing in Greenhouse Conditions Using Ultra-High-Performance Liquid Chromatography⁻Tandem Mass Spectrometry

Chromatographic separation combined with mass spectrometry is a powerful tool for the characterization of plant metabolites because of its high sensitivity and selectivity. In this work, the phytochemical profile of aerial and radicular parts of Coryphantha macromeris (Engelm.) Britton & Rose growing under greenhouse conditions was qualitatively investigated for the first time by means of modern ultra-high-performance liquid chromatography⁻tandem mass spectrometry (UHPLC-PDA-HESI-Orbitrap-MS/MS). The UHPLC-PDA-HESI-Orbitrap-MS/MS analysis indicated a high complexity in phenolic metabolites. In our investigation, 69 compounds were detected and 60 of them were identified. Among detected compounds, several phenolic acids, phenolic glycosides, and organic acids were found. Within this diversity, 26 metabolites were exclusively detected in the aerial part, and 19 in the roots. Twenty-four metabolites occurred in both plant parts. According to the relative abundance of peaks in the chromatogram, ferulic and piscidic acids and their derivatives may correspond to one of the main phenolic compounds of C. macromeris. Our results contribute to the phytochemical knowledge regarding C. macromeris and its potential applications in the pharmaceutical and cosmetic industries. Besides, some metabolites and their fragmentation patterns are reported here for the first time for cacti species.

Current Status and Future Opportunities of Omics Tools in Mycotoxin Research

Mycotoxins are toxic secondary metabolites of low molecular weight produced by filamentous fungi, such as Aspergillus, Fusarium, and Penicillium spp. Mycotoxins are natural contaminants of agricultural commodities and their prevalence may increase due to global warming. Dangerous mycotoxins cause a variety of health problems not only for humans, but also for animals. For instance, they possess carcinogenic, immunosuppressive, hepatotoxic, nephrotoxic, and neurotoxic effects. Hence, various approaches have been used to assess and control mycotoxin contamination. Significant challenges still exist because of the complex heterogeneous nature of food composition. The potential of combined omics approaches such as metabolomics, genomics, transcriptomics, and proteomics would contribute to our understanding about pathogen fungal crosstalk as well as strengthen our ability to identify, isolate, and characterise mycotoxins pre and post-harvest. Multi-omics approaches along with advanced analytical tools and chemometrics provide a complete annotation of such metabolites produced before/during the contamination of crops. We have assessed the merits of these individual and combined omics approaches and their promising applications to mitigate the issue of mycotoxin contamination. The data included in this review focus on aflatoxin, ochratoxin, and patulin and would be useful as benchmark information for future research.

Edible nuts deliver polyphenols and their transformation products to the large intestine: An in vitro fermentation model combining targeted/untargeted metabolomics

The fate of polyphenols from edible tree nuts was investigated using a simulated in vitro intestinal fermentation system. The digested food matrix was fermented for 48 h and the changes in the phenolic profiles were evaluated by both untargeted UHPLC-QTOF and targeted UHPLC-Orbitrap mass spectrometry. The untargeted metabolomics approach allowed us to monitor the comprehensive changes in phenolic profiles from 0 up to 48 h of in vitro fermentation. Multivariate statistics (i.e., orthogonal projection to latent structures discriminant analysis) applied to this untargeted data allowed us to identify the most discriminating phenolic metabolites and to further understand the colonic transformation pathways involved. In particular, 13 putatively identified compounds derived from flavonoids, lignans and phenolic acids were found to have the highest discrimination potential. Six phenolic metabolites were then quantified by means of targeted metabolomics (using a UHPLC-Orbitrap). These metabolites included 3,4-dihydroxyphenylacetic acid, 4-hydroxybenzoic acid, hippuric acid, caffeic acid, protocatechuic acid and protocatechuic aldehyde. Using the targeted data, a clear matrix effect was observed over time, with an increase of some phenolic metabolites moving from 8 to 48 h of in vitro fermentation. Based on these data, catabolic pathways for colonic microbial degradation of flavonoids, hydroxycinnamic acids, tyrosols and lignans are proposed. Our findings show that edible tree nuts deliver polyphenols to the colon, where several microbial transformations occur that lead to smaller phenolic metabolites being observed. Furthermore, we found that the combined use of targeted and untargeted metabolomics can be particularly effective for investigating the fate of polyphenols in the large intestine.