# Advances in Food Metabolomics
###### Abstract  
The study of food reactions in food metabolic networks is a rapidly evolving discipline that is transforming how we understand, monitor, and ensure the quality, safety, and traceability of food products. With its roots in systems biology, metabolomics focuses on the comprehensive analysis of small-molecule metabolites--compounds that represent the final downstream products of cellular activity. Since its formal emergence in 1999, metabolomics has become a vital tool for characterizing complex biological systems, and its application within food science has grown exponentially over the past two decades. In the field of food science, metabolomics provides an unparalleled approach to unravel the intricate biochemical composition of agricultural products and processed foods. Unlike traditional analytical methods, which often target a limited set of known compounds, food metabolomics enables high-throughput, untargeted screening of hundreds to thousands of metabolites in a single run [1]. This capability is essential when analyzing food matrices, which are inherently diverse in composition and influenced by myriad factors such as species, geographic origin, agricultural practices, post-harvest treatments, storage conditions, and processing methods. These variables collectively define the metabolomic signature of a food product. Consequently, metabolomics facilitates the identification of chemical fingerprints and biomarkers that reflect nutritional quality, freshness, spoilage, contamination, and fraudulent practices [2]. Technological advancements have played a pivotal role in enhancing the capabilities of food metabolomics. Analytical platforms such as ultra-high-performance liquid chromatography (UHPLC), capillary electrophoresis (CE), nuclear magnetic resonance (NMR), and mass spectrometry (MS)--especially when combined with ionization methods such as electrospray ionization (ESI) or matrix-assisted laser desorption/ionization (MALDI)--have improved the sensitivity, resolution, and reproducibility of metabolomic analysis [3]. For instance, UHPLC-QTOF and Orbitrap MS systems can now track changes in metabolites during thermal processing, fermentation, and storage, providing deeper insights into food quality and safety [4]. These systems also enable the identification and quantification of individual compounds in plant tissues, thus improving the understanding of their role in plant defense [5]. Rather than being used in isolation, food metabolomics is increasingly being complemented by multi-omics approaches. Integration with transcriptomics, proteomics, and lipidomics--collectively referred to as _foodomics_--enables a holistic assessment of molecular interactions within food systems [1]. This synergy is crucial for lipid profiling, flavor compound discovery, and the elucidation of metabolic responses during fermentation or processing. Multi-omics strategies also facilitate the detection of food fraud and enhancetraceability. For example, untargeted LC-MS metabolomics has been used to discriminate geographical origin in nuts and coffee, while metabolomics combined with chemometrics aids in detecting adulteration in spices [2]. The field of food metabolomics is seeing continuous development in the range of analytical techniques that are now available to support its diverse range of applications [6]. Handling complex datasets remains a major challenge, prompting the development of robust chemometric and machine learning solutions. These approaches range from unsupervised methods such as principal component analysis (PCA) and hierarchical clustering to supervised methods such as orthogonal partial least squares (OPLSs), as well as emerging artificial neural networks (ANNs). They are now integral to metabolomic data interpretation and biomarker discovery [3]. The creation of shared metabolite databases and spectral libraries, along with retention-time alignment tools, has further improved compound identification and cross-study comparability [1].