Metabolomics Advances Fish Meat Quality and Safety Authentication

A comprehensive review published in Food, Nutrition and Health in January 2026 outlines how metabolomics-based strategies are transforming the assessment of fish meat quality, authentication, and food safety. The study, authored by Pallavi Bhalavey and colleagues, examines the application of advanced analytical tools to profile diverse metabolites that serve as indicators of freshness, nutritional value, species origin, and environmental exposure. By bridging fish meat quality assessment with food safety monitoring, metabolomics ensures product integrity and traceability while safeguarding consumer health, reinforcing its role as a pivotal tool in modern seafood science and public health protection.

Metabolomics is defined as an advancing field of biology that emphasizes the systematic detection and measurement of metabolites with a low molecular weight, typically less than 1500 Da, in biological systems under specific conditions. These metabolites, which are the end products or intermediates of metabolic processes such as catabolism and anabolism, encompass amino acids, lipids, peptides, sugars, alcohols, nucleic acids, and organic acids. The review highlights that growth, physiological processes, and development depend on these primary metabolites, which are produced under host genomic control. Additionally, exogenous or xenobiotic compounds, including phytochemicals, drugs, food additives, microbial byproducts, pollutants, pesticides, and cosmetic residues, enter organisms through diet or environmental exposure, collectively influencing physiological responses and health outcomes.

The review distinguishes between targeted and untargeted metabolomics approaches. Untargeted metabolomics offers comprehensive profiling by simultaneously identifying a wide array of metabolites, including previously uncharacterized ones, providing broad metabolic coverage. In contrast, targeted metabolomics quantifies specific metabolites, often within defined pathways, with high sensitivity and accuracy. Both approaches rely on advanced analytical platforms such as gas chromatography-mass spectrometry (GC–MS), liquid chromatography-mass spectrometry (LC–MS), nuclear magnetic resonance (NMR), and capillary electrophoresis-mass spectrometry (CE-MS). Each platform offers distinct advantages in sensitivity, resolution, and metabolite coverage, enabling metabolomics to serve as a key system-level tool across microbial, plant, food, and environmental sciences.

Global demand for meat, especially from aquatic sources, is projected to rise significantly, with fish meat recognized for its exceptional nutritional value. Fish offers premium protein abundant in essential amino acids that promote growth and tissue repair, as well as omega-3 fatty acids like eicosapentaenoic acid (EPA) and docosahexanoic acid (DHA) that support cardiovascular and neurological health. However, maintaining fish meat quality throughout the supply chain poses significant challenges, including resistance to spoiling during distribution and storage. Traditional analytical approaches often fall short in capturing the subtle biochemical changes associated with spoilage, processing, or adulteration, highlighting the need for precise techniques to evaluate freshness, authenticity, and nutritional value.

Metabolomics provides high-resolution profiling to assess fish meat freshness, authenticity, composition, and origin by capturing unique metabolite signatures. This enables precise species identification, detection of adulteration, and monitoring of processing effects. Freshness is a critical quality trait that declines rapidly due to microbial activity and metabolic breakdown, altering nucleotides, amino acids, sugars, organic acids, lipids, and biogenic amines. Metabolomics techniques enable precise monitoring of freshness markers, including omega-3 fatty acids, K-value, trimethylamine, and lipid composition during storage. For instance, in freshwater carp fillets, enzymatic and microbial metabolism induces flavor changes through the production of volatile organic compounds and nucleotide catabolites that serve as freshness indicators.

Specific studies cited in the review demonstrate the practical application of metabolomics in freshness evaluation. In Atlantic salmon, alanine, inosine, and histidine levels varied considerably and may serve as biomarkers to track and assess freshness or spoiling after four to eight days of storage. In grass carp, six possible spoilage biomarkers were revealed: inosine, cytidine, L-aspartic acid, L-tyrosine, Pro-Ile, and PS (17:1(9Z)22:0). Furthermore, species-specific threshold values for hypoxanthine (Hx) and inosine have been defined as freshness indicators. Avan et al. demonstrated that in grass carp, Hx levels are minimal in fresh samples, rise in sub-fresh samples, and exceed specific thresholds in spoiled tissue, providing actionable thresholds for supply chain freshness monitoring.

Authenticity verification in fish and seafood focuses on ensuring accurate identification of species, origin, and production systems, as mislabelling and fraudulent claims pose major threats to consumer trust and biodiversity conservation. Metabolomics relies on the fact that each fish species exhibits a unique pattern of endogenous metabolites shaped by its genetic makeup, physiology, and ecological niche. Targeted and untargeted metabolomic analyses support the discrimination of closely related species, including those that are morphologically similar or frequently substituted in the market. NMR and MS-based methods enable robust classification based on amino acids, lipids, and other small molecules, which can be applied to complex or processed products where DNA may be degraded and traditional barcoding becomes unreliable.

Geographic origin authentication is another critical application, as food safety and traceability in international trade depend on verifying species and geographical origin. The environment, including climate, soil or water composition, feed, and management practices, imparts distinct biochemical and metabolomic signatures to aquatic species. Untargeted metabolomics, particularly using NMR and high-resolution MS, has proven highly effective in discriminating products by regional provenance. For example, Badaoui et al. utilized NMR spectroscopy and GC–MS to quantitatively distinguish wild, cultured, and escaped seabream and seabass based on their fatty acid and phospholipid profiles. Multivariate statistical models achieved 100% classification accuracy, with wild fish showing higher levels of arsenic, selenium, and DHA, while cultured fish had higher levels of linoleic and linolenic acids.

Safety and integrity assessment emphasizes the detection of chemical contaminants, adulterants, and environmental stressors in fish meat. Metabolomics, particularly when integrated with advanced LC–MS/MS and LC–HRMS platforms, has evolved as a powerful technique for identifying both well-known and emerging pollutants. Recent studies demonstrated the ability of optimized workflows to simultaneously identify nineteen classes of contaminants, including industrial chemicals, persistent organic pollutants, endocrine disruptors, pharmaceuticals, and marine toxins. Beyond direct detection, non-targeted metabolomics has revealed small-molecule biomarkers that indicate pollutant exposure, with shifts in amino acids, lipids, bile acids, and sugars frequently linked to disruption of key pathways such as purine metabolism, lipid metabolism, and glutathione metabolism.

Microplastics exposure results in distinctive changes in the composition of metabolites within fish tissues. Metabolomic profiling indicates that microplastics cause oxidative stress and disrupt numerous metabolic pathways, including amino acid biosynthesis, lipid metabolism, and aminoacyl-tRNA biosynthesis. In Carassius auratus, exposure to polystyrene microplastics leads to dose-dependent pathological changes in liver cells, reduced antioxidant enzyme activities, and increased tissue damage and inflammation. These liver injuries correlate with the downregulation of stress-related gene expression and significant metabolic dysfunctions, including disrupted lipid metabolism and immune suppression. Such findings highlight the potential of metabolomics to detect hidden metabolic disruptions that compromise food safety and public health.

Despite its potential, metabolomics faces several challenges and limitations in the analysis of fish meat. Matrix effects constitute a significant challenge, occurring when co-extracted compounds from the complex fish tissue matrix interfere with the ionization efficiency of metabolites during mass spectrometry analysis. This results in ion suppression or enhancement, which can lead to inaccuracies in metabolite quantification and identification. Matrix effects vary with fish species and the nature of compounds present, including lipids, salts, and proteins, which complicate consistent metabolite measurement. Addressing these effects requires robust sample preparation techniques, use of isotopically labelled internal standards, and optimized chromatographic separation to minimize interference.

Another limitation is the lack of comprehensive metabolite databases, which slows dereplication and often results in many unknown discriminatory features. Many MS signals are not confidently assigned because reference spectra and standards for fish-specific metabolites are scarce. Additionally, model complexity and a lack of standardized annotation frameworks restrict the interpretability of integrated models, particularly those using sophisticated machine learning. Accurate annotation of features remains one of the biggest bottlenecks in metabolomics, limiting inter-laboratory reproducibility and complicating meta-analysis. Addressing these limitations will require better fish-specific databases and reference materials, harmonized protocols, rigorous model validation, and closer interaction between researchers and industry.

Looking forward, the integration of metabolomics with other omics techniques like genomics, transcriptomics, and proteomics, alongside artificial intelligence-driven predictive models, promises to establish a more holistic understanding of fish physiology and product quality. These synergies will pave the way for on-site, real-time, and large-scale monitoring systems, supported by standardized global metabolomic databases that can underpin species authentication, sustainability certification, and environmental impact assessments. The translation of metabolomics into regulatory frameworks and industry practices will strengthen consumer confidence, safeguard public health, and promote sustainable aquaculture and fisheries management, ensuring that only authentic and high-quality seafood reaches consumers.

Subject of Research: Metabolomics applications in fish meat quality, authentication, and food safety

Article Title: Metabolomics-based strategies for fish meat quality, authentication and food safety: a review

Article References: Bhalavey, P., Sharma, S., Yadav, K. K., Gulati, R., Bishnoi, D. K., Bansal, N., Mohan, M., Ranga, M., U., S. H., Sharma, S., Pandey, H., Yadav, R. P., Patel, N., & Gurawa, K. (2026). Metabolomics-based strategies for fish meat quality, authentication and food safety: a review. Food, Nutrition and Health, 3(1), Article 2. https://doi.org/10.1007/s44403-025-00045-w

Image Credits: AI Generated

DOI: 10.1007/s44403-025-00045-w

Keywords: metabolomics, fish meat quality, food safety, species authentication, freshness indicators, mass spectrometry, NMR, seafood fraud, contaminant detection, microplastics, Metabolomics-based, strategies