Food Metabolites as Tools for Authentication, Processing, and Nutritive Value Assessment

Targeted Analysis, Metabolic Profiling, and Fingerprinting Based on an LC(GC)-MS Approach for the Comprehensive Evaluation of Pesticide Content in Edible Plants

Pesticides are commonly found in plant-based foods, which inevitably reduces food quality and poses significant health risks to consumers. The extensive variety of crops and the wide range of pesticides used means that no single analytical approach can provide clear and comprehensive information on the pesticide-protection status of a crop. Since most pesticide analyses in food rely on chromatographic techniques combined with various MS platforms, this article focuses exclusively on LC-MS and GC-MS system methodologies. In summary, this paper critically reviews analytical modes-specifically, multi reaction monitoring, data-dependent analysis, and data-independent analysis-and scanning regimes, including full scan, MS, MS/MS, suspect screening, and fingerprinting strategies, for pesticide detection in edible plants. The advantages and disadvantages of these methodologies, as well as their complementary applications, are thoroughly examined.

Untargeted metabolomics profiling for revealing water-soluble bioactive components and biological activities in edible bird's nest

Edible bird's nest (EBN) is a functional food renowned for its numerous health benefits. While its nutritional and therapeutic value is well-documented, the metabolites contributing to the bioactivities of EBN remain poorly understood. This study aimed to identify the metabolites present in EBN subjected to different treatments, including double-boiled EBN (EBNdb), EBN hydrolysate (EBNhydro), EBN fermented with Lactobacillus helveticus (EBNLH), Latilactobacillus curvatus (EBNLC), and Latilactobacillus sakei (EBNLS) using liquid chromatography-mass spectrometry (LC-MS) and correlate the identified bioactive metabolites with the bioactivities of EBN. It was found that the fermented EBNs exhibited the highest number of metabolites, with 76 tentatively identified, followed by EBNhydro (45) and EBNdb (37). Citric acid (1.97-4.48 g/kg) was present in all treated EBN samples, while L(+)-lactic acid (3.03-8.07 g/kg) and adipic acid (2.33-3.18 g/kg) were only found in fermented EBNs. Among the treated EBN samples, EBNLC demonstrated the significantly highest (p ≤ 0.05) antioxidative (22.34 ± 0.41 % 1,1-diphenyl-2-picrylhydrazil radical scavenging activity), antihypertensive (5.46 ± 0.26 % angiotensin-converting enzyme inhibitory activity), and antihyperglycemic activities (6.48 ± 0.34 % α-amylase inhibitory activity). A total of 18 metabolites, including citric acid, 4-sphingenin, N-acetylcitrulline, 4-aminophenol, L(+)-lactic acid, 2-oxoadipate, sildenafil, formylglycinamidin-RP, 11β,17α,21-α-5β-pregnane-3,20-dione, 2-ketobutyric acid, homoserine, benzaldehyde, 1-pyrroline4-hydroxy-2-carboxylate, nortriptyline, 1-methylguanine, 3-hydroxy-trimethyllysine, 3-phenylpropionate, and reserphine were predicted as bioactive metabolites using the partial least squares discriminant analysis (PLS-DA). This study provides valuable insights into the metabolites present in EBN and serves as fundamental data for future investigations into the bioactive compounds responsible for its specific health benefits, potentially leading to the development of enhanced EBN-based functional foods.

Impact of Metabolites from Foodborne Pathogens on Cancer

Foodborne pathogens are microorganisms that cause illness through contamination, presenting significant risks to public health and food safety. This review explores the metabolites produced by these pathogens, including toxins and secondary metabolites, and their implications for human health, particularly concerning cancer risk. We examine various pathogens such as Salmonella sp., Campylobacter sp., Escherichia coli, and Listeria monocytogenes, detailing the specific metabolites of concern and their carcinogenic mechanisms. This study discusses analytical techniques for detecting these metabolites, such as chromatography, spectrometry, and immunoassays, along with the challenges associated with their detection. This study covers effective control strategies, including food processing techniques, sanitation practices, regulatory measures, and emerging technologies in pathogen control. This manuscript considers the broader public health implications of pathogen metabolites, highlighting the importance of robust health policies, public awareness, and education. This review identifies research gaps and innovative approaches, recommending advancements in detection methods, preventive strategies, and policy improvements to better manage the risks associated with foodborne pathogens and their metabolites.

Artificial intelligence decision making tools in food metabolomics: Data fusion unravels synergies within the hazelnut (Corylus avellana L.) metabolome and improves quality prediction

This study investigates the metabolome of high-quality hazelnuts (Corylus avellana L.) by applying untargeted and targeted metabolome profiling techniques to predict industrial quality. Utilizing comprehensive two-dimensional gas chromatography and liquid chromatography coupled with high-resolution mass spectrometry, the research characterizes the non-volatile (primary and specialized metabolites) and volatile metabolomes. Data fusion techniques, including low-level (LLDF) and mid-level (MLDF), are applied to enhance classification performance. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) reveal that geographical origin and postharvest practices significantly impact the specialized metabolome, while storage conditions and duration influence the volatilome. The study demonstrates that MLDF approaches, particularly supervised MLDF, outperform single-fraction analyses in predictive accuracy. Key findings include the identification of metabolites patterns causally correlated to hazelnut's quality attributes, of them aldehydes, alcohols, terpenes, and phenolic compounds as most informative. The integration of multiple analytical platforms and data fusion methods shows promise in refining quality assessments and optimizing storage and processing conditions for the food industry.

Mass spectrometry-based techniques for identification of compounds in milk and meat matrix

Food including milk and meat is often viewed as the mixture of different components such as fat, protein, carbohydrates, moisture and ash, which are estimated using well-established protocols and techniques. However, with the advent of metabolomics, low-molecular weight substances, also known as metabolites, have been recognized as one of the major factors influencing the production, quality and processing. Therefore, different separation and detection techniques have been developed for the rapid, robust and reproducible separation and identification of compounds for efficient control in milk and meat production and supply chain. Mass-spectrometry based techniques such as GC-MS and LC-MS and nuclear magnetic resonance spectroscopy techniques have been proven successful in the detailed food component analysis owing to their associated benefits. Different metabolites extraction protocols, derivatization, spectra generated, data processing followed by data interpretation are the major sequential steps for these analytical techniques. This chapter deals with not only the detailed discussion of these analytical techniques but also sheds light on various applications of these analytical techniques in milk and meat products.

Untargeted metabolomics-based approach using UHPLC-HRMS to authenticate carrots (Daucus carota L.) based on geographical origin and production mode

Carrots produced in different agricultural regions with organic or conventional mode were analyzed by untargeted UHPLC-HRMS using reversed-phase and HILIC modes. Data were first treated separately, and further combined to possibly improve results. An in-house data processing workflow was applied to identify relevant features after peak detection. Based on these features, discrimination models were built using chemometrics. A tentative annotation of chemical markers was performed using online databases and UHPLC-HRMS/MS analyses. An independent set of samples was analyzed to assess the discrimination potential of these markers. Carrots produced in the New Aquitaine region could be successfully discriminated from carrots originating from the Normandy region by an OLPS-DA model. Arginine and 6-methoxymellein could be identified as potential markers with the C18-silica column. Additional markers (N-acetylputrescine, l-carnitine) could be identified thanks to the polar column. Discrimination based on production mode was more challenging: some trend was observed but model metrics remained unsatisfactory.

Pattern Recognition of Varieties of Peach Fruit and Pulp from Their Volatile Components and Metabolic Profile Using HS-SPME-GC/MS Combined with Multivariable Statistical Analysis

A fruit's aroma profile, composed of a complex mixture of volatile organic compounds, is among the core attributes related to the overall taste and consumer preference. Prunus persica L. is a preferred summer fruit with a distinct, favorable olfactory characteristic. The volatile compositions of both peach fruits and fruit pulps from eight peach cultivars (four native and four introduced) was investigated to compare their composition and assess flavor-contributing compounds. In total, 65 compounds were profiled after a HS-SPME-GC-MS analysis: 16 esters, 14 aldehydes, 5 alcohols, 7 hydrocarbons, 7 ketones, 8 acids, and 8 terpenes. The most common compounds were esters, acids, and aldehydes. Although the same compounds were identified in both fruit and pulp, their %TIC (total ion current) differed in favor of the whole fruit. Following the metabolic profiling of the whole fruit and fruit pulp, a total of 44 compounds were identified from the studied varieties. Among them, amino acids, organic acids, sugar alcohols, saccharides, fatty acids, and phenolic acids were identified as existing groups. According to the provided principal component analysis (PCA) and hierarchical cluster analysis (HCA), the relative %TIC of the identified volatile compounds fluctuated depending on the studied cultivar. No differences were visible in the PCA biplots, which suggested that the polar and lipid metabolites do not provide significant variations when considering different parts of the fruit, contrary to the volatile compounds. The obtained results could successfully be applied in the metabolic chemotaxonomy of peaches and the differentiation of the metabolites present in different parts of the peach.

NMR Characterization of Lignans

Lignans are particularly interesting secondary metabolites belonging to the phenyl-propanoid biosynthetic pathway. From the structural point of view, these molecules could belong to the aryltetralin, arylnaphtalene, or dibenzylbutyrolactone molecular skeleton. Lignans are present in different tissues of plants but are mainly accumulated in seeds. Extracts from plant tissues could be characterized by using the NMR-based approach, which provides a profile of aromatic molecules and detailed structural information for their elucidation. In order to improve the production of these secondary metabolites, elicitors could effectively stimulate lignan production. Several plant species are considered in this review with a particular focus on Linum species, well recognized as the main producer of lignans.

Metabolomics-Based Approach for Coffee Beverage Improvement in the Context of Processing, Brewing Methods, and Quality Attributes

Coffee is a worldwide beverage of increasing consumption, owing to its unique flavor and several health benefits. Metabolites of coffee are numerous and could be classified on various bases, of which some are endogenous to coffee seeds, i.e., alkaloids, diterpenes, sugars, and amino acids, while others are generated during coffee processing, for example during roasting and brewing, such as furans, pyrazines, and melanoidins. As a beverage, it provides various distinct flavors, i.e., sourness, bitterness, and an astringent taste attributed to the presence of carboxylic acids, alkaloids, and chlorogenic acids. To resolve such a complex chemical makeup and to relate chemical composition to coffee effects, large-scale metabolomics technologies are being increasingly reported in the literature for proof of coffee quality and efficacy. This review summarizes the applications of various mass spectrometry (MS)- and nuclear magnetic resonance (NMR)-based metabolomics technologies in determining the impact of coffee breeding, origin, roasting, and brewing on coffee chemical composition, and considers this in relation to quality control (QC) determination, for example, by classifying defected and non-defected seeds or detecting the adulteration of raw materials. Resolving the coffee metabolome can aid future attempts to yield coffee seeds of desirable traits and best flavor types.