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Metabolites
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Metabolomic Technology in Quality and Safety of Agricultural Products and...
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Metabolomic Technology in Quality and Safety of Agricultural Products and Foods, 2nd Edition

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Food Metabolomics".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 1602

Special Issue Editor

Dr. Yasuhiro Date

E-Mail Website1 Website2
Guest Editor
Kyushu Okinawa Agricultural Research Center, National Agriculture and Food Research Organization (NARO), Kumamoto 861-1192, Japan
Interests: metabolomics; NMR; data science; food chemistry; analytical chemistry; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Metabolomics is a powerful tool for the analysis and evaluation of the complex and diverse metabolite mixtures found in agricultural products and foods. Nuclear magnetic resonance spectroscopy and mass spectrometry are the most common analytical techniques used in this field. Metabolomic studies provide useful information, e.g., how to improve nutritional and functional qualities, prevent food poisoning, and understand processing and storage effects. Metabolomics has been used for applications such as assessing the quality and safety of products, food authentication, and breeding for disease resistance. In addition, the metabolomic technology in this field is constantly being advanced, owing to the recent developments in computer science and information technology.

This Special Issue focuses on the recent advancements in metabolomic technology that has been made to address agricultural products and foods. The topics to be covered include an application of metabolomic technology for the evaluation and analysis of agricultural products and foods as well as the methodological advancements in metabolomic analysis using agricultural and food big data.

Dr. Yasuhiro Date
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metabolomics
  • metabolic profiling
  • metabolic fingerprinting
  • foodomics
  • nuclear magnetic resonance spectroscopy
  • mass spectrometry
  • multivariate analysis
  • machine learning

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Related Special Issue

Published Papers (2 papers)

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Research

30 pages, 8145 KB  
Article
Revealing the Formation Mechanism of Key Metabolites During Japonica Rice Storage Driven by Microbial Functional Genes
by Xinwei Li, Wei Deng, Zongrui Zhang, Hui Tong and Yi Cao
Metabolites 2026, 16(5), 302; https://doi.org/10.3390/metabo16050302 - 29 Apr 2026
Viewed by 361
Abstract
Background: To elucidate the evolution of metabolites and fungal communities during storage of fragrant japonica rice (Liaoxiangjing 1396), and to investigate the biosynthetic mechanisms of key compounds and their association with quality deterioration, this study examined rice samples stored under simulated conditions for [...] Read more.
Background: To elucidate the evolution of metabolites and fungal communities during storage of fragrant japonica rice (Liaoxiangjing 1396), and to investigate the biosynthetic mechanisms of key compounds and their association with quality deterioration, this study examined rice samples stored under simulated conditions for 16 months. Method: Samples were collected at 4-month intervals (designated R20, R14, R13, R12, and R11). Metabolites were identified using GC-MS non-targeted metabolomics, while fungal community structure was analyzed through metagenomics. Core mechanisms were further elucidated via PLS-DA, KEGG pathway enrichment, and multiomics association analysis. Result: Results demonstrated that the fatty acid content of rice increased initially and then stabilized (from 12.24 mg/g in R20 to 17.63 mg/g in R12). A total of 263 metabolites were identified, with oxygenated organic compounds (38 species) and lipids/lepidid molecules (24 species) as the predominant categories. Twelve key differential metabolites were screened from the R20 and R12 groups, involving five major metabolic pathways, including amino acid metabolism and lipid metabolism. In the fungal community, Pseudomonas (60.2%) and Pantoea (38.19%) were dominant taxa, with a specific Pantoea species (Pantoea sp.) identified as a core potential biomarker. Multiomics association analysis revealed that Klebsiella dominated the ndhB energy metabolism pathway, while multiple bacteria cooperatively regulated the mcp chemotaxis pathway, interacting with monosaccharide and amino acid accumulation. Conclusions: This study reveals that the storage quality deterioration of fragrant japonica rice is driven by the “metabolite–microbe-pathway” chain regulation, and the dynamic changes in key metabolites and fungal communities can serve as quality early warning targets. Full article
(This article belongs to the Special Issue Metabolomic Technology in Quality and Safety of Agricultural Products and Foods, 2nd Edition)
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16 pages, 2826 KB  
Article
Integrative Genomic and Metabolomic Analysis Identifies mQTLs Associated with Genetic Selection for Tenderness in Nellore Cattle
by Joao Marcos Bovetto de Campos Valim, Vinicius Laerte Silva Herreira, Ana Laura dos Santos Munhoz Gôngora, Lauro César Ferreira Beltrão, Eduardo Solano Pina dos Santos, Brenda Santos de Oliveira, Guilherme Pugliesi, Miguel Henrique de Almeida Santana, Guilherme Henrique Gebim Polizel, Luiz Alberto Colnago, Fernanda Maria Marins Ocampos, Germán Dário Ramírez-Zamudio, Saulo Luz Silva and Nara Regina Brandão Cônsolo
Metabolites 2025, 15(12), 760; https://doi.org/10.3390/metabo15120760 - 25 Nov 2025
Viewed by 874
Abstract
Background: Beef tenderness is a key quality attribute that significantly influences consumer satisfaction; however, it exhibits considerable variability due to both genetic and environmental factors. While genomic selection based on Expected Progeny Differences (EPDs) has improved the accuracy of predictions, a substantial portion [...] Read more.
Background: Beef tenderness is a key quality attribute that significantly influences consumer satisfaction; however, it exhibits considerable variability due to both genetic and environmental factors. While genomic selection based on Expected Progeny Differences (EPDs) has improved the accuracy of predictions, a substantial portion of tenderness variability remains unexplained. Metabolomics has emerged as a valuable approach to address this gap, as metabolites reflect gene–environment interactions and may serve as biomarkers for complex traits such as meat tenderness. Objectives: This study aimed to integrate genomic and metabolomic data to identify genetic loci associated with serum metabolites in Nellore calves, offspring of sires with contrasting EPDs for meat tenderness. Methods: Ninety-five male calves were evaluated and divided into two groups according to the sires’ genetic merit: FA-T (favorable EPD for tenderness, n = 45) and UN-T (unfavorable EPD for tenderness, n = 46). Blood serum samples were analyzed by 1H NMR spectroscopy to quantify 40 metabolites, and genotyping was performed using a medium-density SNP panel. Metabolite quantitative trait loci (mQTL) were identified using the MatrixEQTL package, and metabolic enrichment analysis was performed in MetaboAnalyst 6.0. Results: In the FA-T group, SNPs were associated with metabolites such as phenylalanine, tyrosine, and succinate, suggesting enhanced oxidative metabolism and preservation of proteolysis. In the UN-T group, associations of pyruvate, creatinine, and glutamine with distinct SNPs indicated greater reliance on anaerobic glycolysis and early ATP consumption, potentially impairing phosphorylation and postmortem proteolytic activity. Conclusions: These findings suggest that genetic selection for tenderness may induce early divergent metabolic profiles, likely leading to persistent differences in postmortem biochemical pathways, with important implications for meat tenderness. Full article
(This article belongs to the Special Issue Metabolomic Technology in Quality and Safety of Agricultural Products and Foods, 2nd Edition)
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