Advances in Plant and Food Metabolomics

Dear Colleagues,

Metabolomics, a rapidly evolving field, plays a pivotal role in understanding the intricate metabolic networks of plants and food systems. This Special Issue aims to explore the innovative applications of metabolomics and related chemometrics in unraveling complex metabolomic data, thereby advancing our comprehension of plant metabolism and food quality.

Contributions to this issue encompass a diverse array of topics, including but not limited to:

• Multivariate Data Analysis (MVDA): Articles delve into the utilization of MVDA techniques such as principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and hierarchical clustering analysis (HCA) to discern patterns, classify samples, and identify biomarkers in plant and food metabolomics datasets.
• Machine Learning and Pattern Recognition: Novel applications of machine learning algorithms, including support vector machines (SVM), random forests (RF), and neural networks, are explored for predictive modeling, feature selection, and classification tasks in metabolomics studies.
• Data Fusion and Integration: Researchers present methodologies for integrating multiple omics datasets (e.g., genomics, proteomics) with metabolomics data using chemometric approaches to achieve a holistic understanding of plant metabolic pathways and food quality attributes.
• Quantitative Structure-Activity Relationship (QSAR) Modeling: Studies investigate developing QSAR models to predict the biological activities of phytochemicals and food components, facilitating the discovery of bioactive compounds with potential health benefits.
• Metabolite Annotation and Identification: Innovative strategies employing cheminformatics tools, spectral databases, and statistical algorithms are proposed for accurate metabolite annotation and identification, enhancing the reliability of metabolomics studies.

This Special Issue, by compiling cutting-edge research, endeavors to foster interdisciplinary collaborations, accelerate methodological advancements, and contribute to elucidating complex metabolic processes in plants and assessing food quality parameters.

Dr. Luigi Lucini
Dr. Leilei Zhang
Dr. Pascual García-Pérez
Guest Editors

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. Plants is an international peer-reviewed open access semimonthly 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.

• untargeted analysis
• biomarker discovery
• metabolic profiling
• data fusion techniques
• cheminformatics
• quality markers
• fingerprinting