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Metabolites
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Metabolomics in the Study of Cereal Grains and Their Derived...
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Metabolomics in the Study of Cereal Grains and Their Derived Products

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 4924

Special Issue Editor

Dr. Anna Piasecka

E-Mail Website
Guest Editor
Department of Functional Plant Metabolomics, Institute of Bioorganic Chemistry of the Polish Academy of Sciences, Poznan, Poland
Interests: Plant metabolomic, secondary metabolites profiling, cereals, plant stress response

Special Issue Information

Dear Colleagues,

Metabolomics is currently a rapidly developing scientific discipline that creates enormous opportunities for progress in plant biology. Specialized metabolites are widely associated with functional genomics as part of the interactive nature of plant metabolism. They significantly impact plant development, physiology, tolerance to biotic and abiotic stresses, as well as plant biodiversity. Approaches such as mass spectrometry, chromatographic separation or nuclear magnetic resonance, in combination with bioinformatic processing and functional analysis, boost the description of thousands of unknown phytochemicals. Metabolomics is especially important for cereal and pseudo-cereal crops, which are the basic source of food and beverages worldwide. A constantly growing number of scientific reports and new technologies in the field of cereal metabolomics reflect the progress in improving food quality, yield production and, therefore, sustainable agriculture. Determining the metabolome of cereal plants, despite efforts from scientists, is still a challenge. The main limitations are caused by fragmentary knowledge about the role of metabolites in cereals and derivative products, their structural diversity and complexity.

This Special Issue presents a collection of original research and review articles that highlight the latest discoveries and advances in the field of metabolomics of cereal crops, especially grains and products derived from them. The selected articles will strengthen our understanding of molecular processes related to improving cereal plants and health-promoting cereal food. There is potential to facilitate new knowledge for breeders, sustainable agriculture, and food security. It will also be an excellent opportunity to demonstrate modern technologies and research approaches in targeted and untargeted metabolomics in health and agriculture.

Dr. Anna Piasecka
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 100 words) can be sent to the Editorial Office for announcement on this website.

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
  • cereal crops
  • grain metabolites
  • functional genomic of cereals
  • grain food

Published Papers (2 papers)

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Research

19 pages, 3802 KiB  
Article
Effect of Maturity Stage on Cereal and Leguminous Seeds’ Metabolome as Analyzed Using Gas Chromatography Mass-Spectrometry (GC-MS) and Chemometric Tools
by Doaa B. Saied, Nehal S. Ramadan, Magdy M. El-Sayed and Mohamed A. Farag
Metabolites 2023, 13(2), 163; https://doi.org/10.3390/metabo13020163 - 23 Jan 2023
Cited by 2 | Viewed by 1993
Abstract
Cereal and leguminous seeds are considered as major generic dietary source of energy, carbohydrates as well as proteins in the Mediterranean diet and are frequently consumed in their immature form in several regions including the Middle East. Hence, the current study aimed to [...] Read more.
Cereal and leguminous seeds are considered as major generic dietary source of energy, carbohydrates as well as proteins in the Mediterranean diet and are frequently consumed in their immature form in several regions including the Middle East. Hence, the current study aimed to assess metabolites’ heterogeneity amongst five major cereal and leguminous seeds of different species, and cultivars, i.e., Triticum aestivum L. (two cultivars), Hordeum vulgare L., Vicia faba L. and Cicer arietinum L., at different maturity stages. Gas chromatography mass-spectrometry (GC-MS) analysis using multivariate data analyses was employed for nutrient profiling and sample segregation assessed using chemometric tools, respectively. A total of 70 peaks belonging to sugars, fatty acids/esters, steroids, amino acids and organic acids were identified including sucrose, melibiose, glucose and fructose as major sugars, with butyl caprylate, hydroxybutanoic acid and malic acid contributing to the discrimination between seed species at different maturity stages. The investigation of total protein content revealed comparable protein levels amongst all examined seeds with the highest level detected at 20.1% w/w in mature fava bean. Results of this study provide a novel insight on cereal and leguminous seeds’ metabolomics in the context of their maturity stages for the first time in literature. Full article
(This article belongs to the Special Issue Metabolomics in the Study of Cereal Grains and Their Derived Products)
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15 pages, 1542 KiB  
Article
Differentiation of Geographical Origin of White and Brown Rice Samples Using NMR Spectroscopy Coupled with Machine Learning Techniques
by Maham Saeed, Jung-Seop Kim, Seok-Young Kim, Ji Eun Ryu, JuHee Ko, Syed Farhan Alam Zaidi, Jeong-Ah Seo, Young-Suk Kim, Do Yup Lee and Hyung-Kyoon Choi
Metabolites 2022, 12(11), 1012; https://doi.org/10.3390/metabo12111012 - 24 Oct 2022
Cited by 4 | Viewed by 1952
Abstract
Rice (Oryza sativa L.) is a widely consumed food source, and its geographical origin has long been a subject of discussion. In our study, we collected 44 and 20 rice samples from different regions of the Republic of Korea and China, respectively, [...] Read more.
Rice (Oryza sativa L.) is a widely consumed food source, and its geographical origin has long been a subject of discussion. In our study, we collected 44 and 20 rice samples from different regions of the Republic of Korea and China, respectively, of which 35 and 29 samples were of white and brown rice, respectively. These samples were analyzed using nuclear magnetic resonance (NMR) spectroscopy, followed by analyses with various data normalization and scaling methods. Then, leave-one-out cross-validation (LOOCV) and external validation were employed to evaluate various machine learning algorithms. Total area normalization, with unit variance and Pareto scaling for white and brown rice samples, respectively, was determined as the best pre-processing method in orthogonal partial least squares–discriminant analysis. Among the various tested algorithms, support vector machine (SVM) was the best algorithm for predicting the geographical origin of white and brown rice, with an accuracy of 0.99 and 0.96, respectively. In external validation, the SVM-based prediction model for white and brown rice showed good performance, with an accuracy of 1.0. The results of this study suggest the potential application of machine learning techniques based on NMR data for the differentiation and prediction of diverse geographical origins of white and brown rice. Full article
(This article belongs to the Special Issue Metabolomics in the Study of Cereal Grains and Their Derived Products)
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