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

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