Towards superior plant-based foods using metabolomics

Untargeted metabolomics analysis using UPLC-QTOF/MS and GC-MS to unravel changes in antioxidant activity and compounds of almonds before and after roasting

Almonds are a commonly consumed nut. They possess significantof nutritional and health benefitsand are commonly processed by roasting. This study aimed to investigatthe effects of roasting on the compound composition and antioxidant activity of almonds. Metabolomics analysis, performed via UPLC-QTOF/MS, and fatty acid analysis, conducted via GC-MS, employed, and the results demonstrated a significant increase in antioxidant activity of post-roasting and in vitro digestion, ranging from 1.16 to 3.44 times. Untargeted metabolomics identified a total of 172 compounds, with notable differences observed in organic oxides, fatty acids, and their derivatives. Correlation analysis identified fatty acids as the primary influencers of changes in antioxidant activity following roasting. Taken together, these findings suggest that roasting enhances the antioxidant activity of almonds, primarily due to alterations in fatty acid analogs, thereby providing valuable insights into optimizing almond consumption for health benefits.

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.

Widely Targeted Metabolomics Analysis to Reveal Metabolite of Morus alba L. in Different Medicinal Parts

Morus alba L. is a tradition medical and edible plant. It is rich in many important bioactive components. However, there is a dearth of systematic information about the components. Here, the Mori Cortex, Mori Folium, Mori Fructus, and Mori Ramulus were studied. Ultrahigh-performance liquid chromatography-mass spectrometry (UHPLC-MS) is used to study primary and secondary metabolites. Eight hundred two metabolites were identified and classified into 10 different categories in total. Correlation analysis, hierarchical clustering analysis, and principal component analysis of metabolites showed that different parts of the sample could be significantly different. In different medicinal parts, alkaloids accounted for 4.0%, 3.6%, 5.1%, and 4.5%; flavonoids accounted for 0.7%, 27.2%, 5.6%, 1.2%; terpenes accounted for 20.1%, 2.1%, 2.6%, 2.5%. Furthermore, the abundance of phenols, phenylpropanoids, and lipids metabolites sequentially accounted for 2.3-4.4%, 0.5-1.8%, and 2.4-5.3%. These results have improved our understanding of metabolites and provided a reference for research on the medicinal and edible value of Morus alba L. In addition, the study reveals the correlation between the components of Traditional Chinese medicine and the basic theory of TCM properties and reinterprets the ancient wisdom in the world's traditional herbs through the perspective of modern science.

Identification of biological signatures of cruciferous vegetable consumption utilizing machine learning-based global untargeted stable isotope traced metabolomics

In recent years there has been increased interest in identifying biological signatures of food consumption for use as biomarkers. Traditional metabolomics-based biomarker discovery approaches rely on multivariate statistics which cannot differentiate between host- and food-derived compounds, thus novel approaches to biomarker discovery are required to advance the field. To this aim, we have developed a new method that combines global untargeted stable isotope traced metabolomics and a machine learning approach to identify biological signatures of cruciferous vegetable consumption. Participants consumed a single serving of broccoli (n = 16), alfalfa sprouts (n = 16) or collard greens (n = 26) which contained either control unlabeled metabolites, or that were grown in the presence of deuterium-labeled water to intrinsically label metabolites. Mass spectrometry analysis indicated 133 metabolites in broccoli sprouts and 139 metabolites in the alfalfa sprouts were labeled with deuterium isotopes. Urine and plasma were collected and analyzed using untargeted metabolomics on an AB SCIEX TripleTOF 5,600 mass spectrometer. Global untargeted stable isotope tracing was completed using openly available software and a novel random forest machine learning based classifier. Among participants who consumed labeled broccoli sprouts or collard greens, 13 deuterium-incorporated metabolomic features were detected in urine representing 8 urine metabolites. Plasma was analyzed among collard green consumers and 11 labeled features were detected representing 5 plasma metabolites. These deuterium-labeled metabolites represent potential biological signatures of cruciferous vegetables consumption. Isoleucine, indole-3-acetic acid-N-O-glucuronide, dihydrosinapic acid were annotated as labeled compounds but other labeled metabolites could not be annotated. This work presents a novel framework for identifying biological signatures of food consumption for biomarker discovery. Additionally, this work presents novel applications of metabolomics and machine learning in the life sciences.

Flavor characterization of Citri Reticulatae Pericarpium (Citrus reticulata 'Chachiensis') with different aging years via sensory and metabolomic approaches

Guangchenpi (GCP), which is the peel of Citrus reticulata 'Chachiensis', is widely used as an herbal medicine, tea and food ingredient in southeast Asia. Prolonging its aging process results in a more pleasant flavor and increases its profitability. Through the integration of sensory evaluation with flavoromic analysis approaches, we evaluated the correlation between the flavor attributes and the profiles of the volatiles and flavonoids of GCP with various aging years. Notably, d-limonene, γ-terpinene, dimethyl anthranilate and α-phellandrene were the characteristic aroma compounds of GCP. Besides, α-phellandrene and nonanal were decisive for consumers' perception of GCP aging time due to changes of their odor activity values (OAVs). The flavor attributes of GCP tea liquid enhanced with the extension of aging time, and limonene-1,2-diol was identified as an important flavor enhancer. Combined with machine learning models, key flavor-related metabolites could be developed as efficient biomarkers for aging years to prevent GCP adulteration.

Umami Characteristics and Taste Improvement Mechanism of Meat

Taste is one of the five senses that detect vital information about what we are eating. Comprehending taste is crucial for enhancing the flavor of foodstuffs and high-protein foods like meat. Umami has global recognition as the fifth elementary taste, alongside sweetness, sourness, saltiness, and bitterness. Umami compounds are known to enhance the sensation of recognized flavors such as salty, sweet, bitter, and others. This could end up in greater food consumption by consumption by consumers. With the rising global population, meat consumption is rising and is projected to double by 2025. It is crucial to comprehend the umami mechanism of meat and meat products, identify novel compounds, and employ laboratory methodologies to gather varied information. This knowledge will aid in the development of new consumer products. Although very limited information is available on umami taste and compounds in meat through research data. This article discusses recent advancements in umami compounds in other foodstuff as well as meat to aid in designing future research and meat product development. Moreover, another objective of this review is to learn present techniques in foodstuffs to enhance umami taste and utilize that knowledge in meat products.

Metabolomic analysis of black sesame seeds: Effects of processing and active compounds in antioxidant and anti-inflammatory properties

Black sesame seeds (BSS) have been recognized as a functional food due to their nutritional and therapeutic value for many years. In China, BSS is traditionally processed and consumed through two methods, namely, nine steaming nine sun-drying and stir-frying. The present study aimed to evaluate the effects of these processing techniques on the antioxidant and anti-inflammatory activities of BSS. UPLC-QTOF/MS was used for untargeted metabolomics to analyze the composition changes. The results indicated that the different samples had good antioxidant and anti-inflammatory activities, but thermal treatment reduced their activities. Untargeted metabolomics identified a total of 196 metabolites. Molecular docking studies targeting proteins associated with inflammation (iNOS) demonstrated that compounds acting as inhibitors were significantly reduced under both treatments. These results indicate that both nine steaming nine sun-drying and stir-frying lead to substantial loss of antioxidant, anti-inflammatory, and bioactive metabolites in BSS, which provides an important reference for its rational utilization.

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.

Nutritional metabolomics: Recent developments and future needs

Metabolomics has rapidly been adopted as one of the key methods in nutrition research. This review focuses on the recent developments and updates in the field, including the analytical methodologies that encompass improved instrument sensitivity, sampling techniques and data integration (multiomics). Metabolomics has advanced the discovery and validation of dietary biomarkers and their implementation in health research. Metabolomics has come to play an important role in the understanding of the role of small molecules resulting from the diet-microbiota interactions when gut microbiota research has shifted towards improving the understanding of the activity and functionality of gut microbiota rather than composition alone. Currently, metabolomics plays an emerging role in precision nutrition and the recent developments therein are discussed.

Peptide hydrogels: Synthesis, properties, and applications in food science

Due to the unique and excellent biological, physical, and chemical properties of peptide hydrogels, their application in the biomedical field is extremely wide. The applications of peptide hydrogels are closely related to their unique responsiveness and excellent properties. However, its defects in mechanical properties, stability, and toxicity limit its application in the food field. In this review, we focus on the fabrication methods of peptide hydrogels through the physical, chemical, and biological stimulations. In addition, the functional design of peptide hydrogels by the incorporation with materials is discussed. Meanwhile, the excellent properties of peptide hydrogels such as the stimulus responsiveness, biocompatibility, antimicrobial properties, rheology, and stability are reviewed. Finally, the application of peptide hydrogel in the food field is summarized and prospected.

Value Addition Employing Waste Bio-Materials in Environmental Remedies and Food Sector

Overall, combating food waste necessitates a multifaceted approach that includes education, infrastructure, and policy change. By working together to implement these strategies, we can help reduce the negative impacts of food waste and create a more sustainable and equitable food system. The sustained supply of nutrient-rich agrifood commodities is seriously threatened by inefficiencies caused by agricultural losses, which must be addressed. As per the statistical data given by the Food and Agriculture Organisation (FAO) of the United Nations, nearly 33.33% of the food that is produced for utilization is wasted and frittered away on a global level, which can be estimated as a loss of 1.3 billion metric tons per annum, which includes 30% cereals, 20% dairy products 35% seafood and fish, 45% fruits and vegetables, and 20% of meat. This review summarizes the various types of waste originating from various segments of the food industry, such as fruits and vegetables, dairy, marine, and brewery, also focusing on their potential for developing commercially available value-added products such as bioplastics, bio-fertilizers, food additives, antioxidants, antibiotics, biochar, organic acids, and enzymes. The paramount highlights include food waste valorization, which is a sustainable yet profitable alternative to waste management, and harnessing Machine Learning and Artificial Intelligence technology to minimize food waste. Detail of sustainability and feasibility of food waste-derived metabolic chemical compounds, along with the market outlook and recycling of food wastes, have been elucidated in this review.

New Approach for the Identification of Isobaric and Isomeric Metabolites

The structural elucidation of metabolite molecules is important in many branches of the life sciences. However, the isomeric and isobaric complexity of metabolites makes their identification extremely challenging, and analytical standards are often required to confirm the presence of a particular compound in a sample. We present here an approach to overcome these challenges using high-resolution ion mobility spectrometry in combination with cryogenic vibrational spectroscopy for the rapid separation and identification of metabolite isomers and isobars. Ion mobility can separate isomeric metabolites in tens of milliseconds, and cryogenic IR spectroscopy provides highly structured IR fingerprints for unambiguous molecular identification. Moreover, our approach allows one to identify metabolite isomers automatically by comparing their IR fingerprints with those previously recorded in a database, obviating the need for a recurrent introduction of analytical standards. We demonstrate the principle of this approach by constructing a database composed of IR fingerprints of eight isomeric/isobaric metabolites and use it for the identification of these isomers present in mixtures. Moreover, we show how our fast IR fingerprinting technology allows to probe the IR fingerprints of molecules within just a few seconds as they elute from an LC column. This approach has the potential to greatly improve metabolomics workflows in terms of accuracy, speed, and cost.

Metabolomics and sensory evaluation of white asparagus ingredients in instant soups unveil important (off-)flavours

Split-stream processing of asparagus waste stream is a novel approach to produce spray-dried powder and fibre. Asparagus ingredients processed by this method and a commercial asparagus powder were compared by evaluating their flavour profile in a soup formulation. Professional sensory panel and untargeted metabolomics approaches using GC-MS and LC-MS were carried out. Unsupervised and supervised statistical analyses were performed to highlight discriminatory metabolites and correlate these to sensory attributes. The spray-dried powder scored higher on asparagus flavour compared to the commercial powder. The fibre negatively impacted the taste and mouthfeel of the soups. GC-O-MS confirmed the role of dimethyl sulphide, 2-methoxy-3-isopropyl pyrazine and 2-methoxy-3-isobutyl pyrazine in asparagus odour. Seven new volatile compounds are also proposed to contribute to asparagus flavour notes, most of which were more abundant in the spray-dried powder. This research demonstrates the feasibility of upcycling asparagus waste streams into flavour-rich ingredients with good sensorial properties.

Integrated untargeted metabolome, full-length sequencing, and transcriptome analyses reveal insights into the fruit quality at different harvest times of Chaenomeles speciosa

Chaenomeles speciosa fruit is a homologous medicine and food plant with a long history of multiple uses. It could be harvested near maturity and last for a long time. However, the optimal harvest strategy of Chaenomeles speciosa for various uses is currently unavailable. Here, untargeted metabolome at different harvest times during maturation was investigated for the first time, and 896 metabolites, including sugars, organic acids, amino acids, and phenylpropanoids, were identified. Optimal harvesting methods were proposed for different purposes. During the early maturation stages (before 105 days after full bloom), Ch. speciosa fruit could be harvested as Chinesemedicine. Whereas as snacks and food, Ch. speciosa fruit might be harvested at late maturity (after 120 days after full bloom). In addition, the overall network was revealed by integrating full-length Iso-seq and transcriptomics (RNA-seq) to investigate the association between quality-associated metabolites and Chaenomeles speciosa fruit gene expression during maturation. A few putative genes were captured via screening, dissecting and correlation analysis with the quality-associated metabolites (including d-glucose, catechin, gallocatechin, and succinic acid). Overall, in addition to providing a harvesting strategy for food and medicine, we also investigated the metabolism and gene expression pattern of Chaenomeles speciosa fruit during maturation. This comprehensive data and analyses laid the foundation for further investigating potential regulatory mechanisms during harvest and provided a new possibility for its development and utilization.

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.

Metabolomics Approach to Characterize Green Olive Leaf Extracts Classified Based on Variety and Season

The huge interest in the health-related properties of plant polyphenols to be applied in food and health-related sectors has brought about the development of sensitive analytical methods for metabolomic characterization. Olive leaves constitute a valuable waste rich in polyphenols with functional properties. A (HR)LC-ESI-ORBITRAP-MS analysis with a multivariate statistical analysis approach using PCA and/or PLS-DA projection methods were applied to identify polyphenols in olive leaf extracts of five varieties from the Apulia region (Italy) in two different seasonal times. A total of 26 metabolites were identified, further finding that although metabolites are common among the different cultivars, they differ in the relative intensity of each peak and within each cultivar in the two seasonal periods taken into consideration. The results of the total phenol contents showed the highest content in November for Bambina and Cima di Mola varieties (1816 and 1788 mg/100 g, respectively), followed by Coratina, Leccino, and Cima di Melfi; a similar trend was found for the antioxidant activity and RapidOxy evaluations by reaching in Bambina values of 45 mmol TE/100 g and 85 min of induction time.

Plant-Based Innovations for the Transition to Sustainability: A Bibliometric and in-Depth Content Analysis

Plants maintain the ecological equilibrium of the earth and stabilize the ecosystem. Today, traditional commodities and new value-added markets can be served simultaneously. There is significant biosource and bioprocess innovation for biobased industrial products. Furthermore, plant-based innovation is associated with the transition to sustainability. This study performed a bibliometric and in-depth content analysis to review plant-based innovations in the research field between 1995 and 2022. A set of 313 articles was identified from the Scopus and Web of Science databases. Different analytical scientometric tools (topic mapping and overlay visualization networks) were used to analyze 124 articles; the most influential countries, institutions, authors, journals and articles were identified. Through in-depth studies, based on the grounded theory approach, five leading research areas related to plant-based innovation were determined: (1) agricultural/environmental innovation, (2) plant-based food or feed innovation, (3) innovation within the medical/pharmaceutical research area, (4) technology-related innovation and (5) economic/business aspects of plant-based innovations. Future research directions include exploring less examined and new topics, such as the sustainability implications of incorporating various plant-based foods and Industry 4.0 in plant-based innovation, and linking and developing findings from different research areas.

Pepino (Solanum muricatum) Metabolic Profiles and Soil Nutrient Association Analysis in Three Growing Sites on the Loess Plateau of Northwestern China

Different soil nutrients affect the accumulation characteristics of plant metabolites. To investigate the differences among the metabolites of pepino grown in greenhouses on the Loess Plateau in northwest China, we investigated the main soil nutrients and their correlation with metabolites. A total of 269 pepino metabolites were identified using UPLC-QTOF-MS to detect metabolites in fruits from three major pepino growing regions and analyze their differential distribution characteristics. A total of 99 of these substances differed among pepino fruits from the three areas, and the main classes of the differential metabolites were, in order of number: amino acids and derivatives, nucleotides and derivatives, organic acids, alkaloids, vitamins, saccharides and alcohols, phenolic acids, lipids and others. An environmental factor analysis identified soil nutrients as the most significant differentiator. Five soil nutrient indicators: TN (total nitrogen), TP (total phosphorus), AP (available phosphorus), AK (available potassium), and OM (organic matter), exhibited significant differences in three growing sites. Metabolite and soil nutrient association analysis using redundancy analysis (RDA) and the Mantel test indicated that TN and OM contributed to the accumulation of amino acids and derivatives, nucleotides and derivatives, and alkaloids while inhibiting organic acids, vitamins coagulation biosynthesis. Moreover, AP and TP were associated with the highest accumulation of saccharides and, alcohols, phenolic acids. Consequently, differences in soil nutrients were reflected in pepino metabolite variability. This study clarified the metabolite variability and the relationship between pepino and soil nutrients in the main planting areas of northwest China. It provides a theoretical basis for the subsequent development of Pepino’s nutritional value and cultivation management.

Integration of bioassay and non-target metabolite analysis of tomato reveals that β-carotene and lycopene activate the adiponectin signaling pathway, including AMPK phosphorylation

Adiponectin, an adipokine, regulates glucose metabolism and insulin sensitivity through the adiponectin receptor (AdipoR). In this study, we searched for metabolites that activate the adiponectin signaling pathway from tomato (Solanum lycopersicu). Metabolites of mature tomato were separated into 55 fractions by liquid chromatography, and then each fraction was examined using the phosphorylation assay of AMP-protein kinase (AMPK) in C2C12 myotubes and in AdipoR-knockdown cells by small interfering RNA (siRNA). Several fractions showed AMPK phosphorylation in C2C12 myotubes and siRNA-mediated abrogation of the effect. Non-targeted metabolite analysis revealed the presence of 721 diverse metabolites in tomato. By integrating the activity of fractions on AMPK phosphorylation and the 721 metabolites based on their retention times of liquid chromatography, we performed a comprehensive screen for metabolites that possess adiponectin-like activity. As the screening suggested that the active fractions contained four carotenoids, we further analyzed β-carotene and lycopene, the major carotenoids of food. They induced AMPK phosphorylation via the AdipoR, Ca²⁺/calmodulin-dependent protein kinase kinase and Ca²⁺ influx, in addition to activating glucose uptake via AdipoR in C2C12 myotubes. All these events were characteristic adiponectin actions. These results indicated that the food-derived carotenoids, β-carotene and lycopene, activate the adiponectin signaling pathway, including AMPK phosphorylation.

Quality control of Zingiberis Rhizoma and its processed products by UHPLC-Q-TOF/MS-based non-targeted metabonomics combining with SIBDV method

Zingiberis Rhizoma (ZR) is a homologous plant with pungent tastes and aromas, which has unique nutritional value and tremendous application potentiality. Zingiberis Rhizoma Praeparatum (ZRP) and Carbonised Ginger (CG) are processed products of ZR through different processing methods, and they are commonly used ingredients in food supplements. This study used ZR, ZRP and CG from different batches to further understand composition differences after processing. Additionally, we performed non-targeted metabolomics-based profiling of gingerols by ultra-high-performance liquid chromatography coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) in combination with multivariate analysis and compounds identification. In which, we developed a comprehensive SWATH-IDA bi-directionally verified (SIBDV) method integrating the advantages of Sequential Windowed Acquisition of all Theoretical fragment ions (SWATHTM) and traditional information-dependent acquisition (IDA) mode for characterization of gingerols. Potential chemical markers were selected by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) of chemometrics methods. After that, the threshold variable importance in projection (VIP) value and P value were employed to screen the valuable MS features for discriminating ZR, ZRP and CG. In total, 59 gingerols in the different samples were structurally identified. Results allowed the selection of 33 gingerols, which are nominated as novel markers for materials authentication in ZR, ZRP and CG. The analysis of the study showed that the content of gingerols showed a downward trend after processing, but shogaols and gingerone compounds had an upward trend, resulting in differences in application and pharmacodynamic efficacy. These findings provide promising perspectives in the quality control of ZR, ZRP and CG, as well as for laying the foundation in food design and development.

Comparative metabolite fingerprinting of chia, flax and sesame seeds using LC-MS untargeted metabolomics

Chia, flax, and sesame seeds are well known for their nutritional quality and are commonly included in bakery products. So far, the development of methods to verify their presence and authenticity in foods is a requisite and a raised need. In this work we applied untargeted metabolomics to propose authenticity markers. Seeds were analyzed by HPLC-MS/MS and 9938 features in negative mode and 9044 in positive mode were obtained by Mzmine. After isotopes grouping, alignment, gap-filling, filtering adducts, and normalization, PCA was applied to explore the dataset and recognize pre-existent classification patterns. OPLS-DA analysis and S-Plots were used as supervised methods. Twenty-five molecules (12 in negative mode and 13 in positive mode) were selected as discriminant for the three seeds, polyphenols and lignans were identified among them. To the best of our knowledge, this is the first approach using non-target HPLC-MS/MS for the authentication of chia, flax and sesame seeds.

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

Secondary metabolites are molecules with unlimited applications that have been gaining importance in various industries and studied from many angles. They are mainly used for their bioactive capabilities, but due to the improvement of sensibility in analytical chemistry, they are also used for authentication and as a quality control parameter for foods, further allowing to help avoid food adulteration and food fraud, as well as helping understand the nutritional value of foods. This manuscript covers the examples of secondary metabolites that have been used as qualitative and authentication molecules in foods, from production, through processing and along their shelf-life. Furthermore, perspectives of analytical chemistry and their contribution to metabolite detection and general perspectives of metabolomics are also discussed.

Capillary electromigration methods for food analysis and Foodomics: Advances and applications in the period February 2019-February 2021

This work presents a revision of the main applications of capillary electromigration methods in food analysis and Foodomics. Articles that were published during the period February 2019-February 2021 are included. The work shows the multiple CE methods that have been developed and applied to analyze different types of molecules in foods. Namely, CE methods have been applied to analyze amino acids, biogenic amines, carbohydrates, chiral compounds, contaminants, DNAs, food additives, heterocyclic amines, lipids, secondary metabolites, peptides, pesticides, phenols, pigments, polyphenols, proteins, residues, toxins, vitamins, small organic and inorganic compounds, as well as other minor compounds. The last results on the use of CE for monitoring food interactions and food processing, including recent microchips developments and new applications of CE in Foodomics, are discussed too. The new procedures of CE to investigate food quality and safety, nutritional value, storage and bioactivity are also included in the present review work.

Metabolomics as a Tool to Elucidate the Sensory, Nutritional and Safety Quality of Wheat Bread-A Review

Wheat (Triticum aestivum) is one of the most extensively cultivated and used staple crops in human nutrition, while wheat bread is annually consumed in more than nine billion kilograms over the world. Consumers’ purchase decisions on wheat bread are largely influenced by its nutritional and sensorial characteristics. In the last decades, metabolomics is considered an effective tool for elucidating the information on metabolites; however, the deep investigations on metabolites still remain a difficult and longtime action. This review gives emphasis on the achievements in wheat bread metabolomics by highlighting targeted and untargeted analyses used in this field. The metabolomics approaches are discussed in terms of quality, processing and safety of wheat and bread, while the molecular mechanisms involved in the sensorial and nutritional characteristics of wheat bread are pointed out. These aspects are of crucial importance in the context of new consumers’ demands on healthy bakery products rich in bioactive compounds but, equally, with good sensorial acceptance. Moreover, metabolomics is a potential tool for assessing the changes in nutrient composition from breeding to processing, while monitoring and understanding the transformations of metabolites with bioactive properties, as well as the formation of compounds like toxins during wheat storage.

LC-ESI/LTQOrbitrap/MS Metabolomic Analysis of Fennel Waste (Foeniculum vulgare Mill.) as a Byproduct Rich in Bioactive Compounds

Food industries produce a high amount of waste every year. These wastes represent a source of bioactive compounds to be used to produce cosmetic and nutraceutical products. In this study, the possibility to retrain food waste as a potential source of bioactive metabolites is evaluated. In particular, metabolite profiles of different parts (bulb, leaves, stems and little stems) of fennel waste were investigated by liquid chromatography coupled with mass spectrometry (LC-ESI/LTQ Orbitrap MS). To discriminate the different plant parts, a Multivariate Data Analysis approach was developed. Metabolomic analysis allowed the identification of different metabolites mainly belonging to hydroxycinnamic acid derivatives, flavonoid glycosides, flavonoid aglycons, phenolic acids, iridoid derivatives and lignans. The identification of compounds was based on retention times, accurate mass measurements, MS/MS data, exploration on specific metabolites database and comparison with data reported in the literature for F. vulgare. Moreover, the presence of different oxylipins was relieved; these metabolites for the first time were identified in fennel. Most of the metabolites identified in F. vulgare possess anti-inflammatory, antioxidant and/or immunomodulatory properties. Considering that polyphenols are described to possess antioxidant activity, spectrophotometric tests were performed to evaluate the antioxidant activity of each part of the fennel.

Identifying sources of metabolomic diversity and reconfiguration in peach fruit: taking notes for quality fruit improvement

The metabolomic content determines many of the important features of a fruit, such as its taste, flavor, color, nutritional value, and abiotic or biotic resistance. Peach (Prunus persica (L.) Batsch) is one of the best genetically characterized species used as a model for Rosaceae, the drupes of which are a source of minerals, vitamins, fiber, and antioxidant compounds for healthy diets around the world. During the last few years, a great advance in the analysis of the metabolic diversity and reconfiguration in different peach varieties in response to developmental and environmental factors has occurred. These studies have shown that the great phenotypic diversity among different peach varieties is correlated with differential metabolomic content. Besides, the fruit metabolome of each peach variety is not static; on the contrary, it is drastically configured in response to both developmental and environmental signals, and moreover, it was found that these metabolic reconfigurations are also variety dependent. In the present review, the main sources of metabolic diversity and conditions that induce modifications in the peach fruit metabolome are summarized. It is postulated that comparison of the metabolic reconfigurations that take place among the fruits from different varieties may help us better understand peach fruit metabolism and their key drivers, which in turn may aid in the future design of high‐quality peach fruits.