We applied metabolomics to the evaluation of yellowtail muscle as a new freshness evaluation method for fish meat. Metabolites from yellowtail ordinary and dark muscle (DM) stored at 0 °C and 5 °C were subjected to metabolomics for primary metabolites based on gas chromatography-mass spectrometry (GC-MS). For the annotated metabolites, we created statistically significant models for storage time prediction for all storage conditions by orthogonal partial least squares analysis, using storage time as the y-variable. DM is difficult to evaluate using the K value method, the predominant existing freshness evaluation method. However, in the proposed method, the metabolic component profiles of DM changed depending on storage time. Important metabolites determined from variables important for prediction (VIP) values included various metabolites, such as amino acids and sugars, in addition to nucleic-acid-related substances, especially inosine and hypoxanthine. Therefore, metabolomics, which comprehensively analyses different molecular species, has potential as a new freshness evaluation method that can objectively evaluate conditions of stored fish meat.

A metabolic analysis technique employing gas chromatography-mass spectrometry was used to distinguish between muscle tissue types in yellowtail Seriola quinqueradiata. The dataset of metabolites from each yellowtail muscle type was subjected to metabolic profile analysis using the SIMCA 14 software package. Orthogonal partial least squares discriminant analysis revealed a marked difference in metabolic profiles of ordinary muscle (OM) and dark muscle (DM), and OM types were further separated into three groups (dorsal, caudal and ventral). Further, several metabolites, such as ornithine for OM vs DM and glycerol-3-phosphate for OM tissue types were identified as potential markers for distinguishing types of yellowtail flesh by S-plot analysis. Based on these findings, metabolic analysis techniques could become useful tools for distinguishing among fish muscle types.

We performed metabolic profiling on yellowtail (Seriola quinqueradiata) muscle to develop an objective taste evaluation method for fish meat. Dark (DM) and ordinary (OM) muscle samples before and after storage were subjected to gas chromatography-mass spectrometry (GC-MS) analysis and taste measurements using an electronic tongue. The metabolites identified by the GC-MS analysis were treated as x variables, and the taste values obtained by the electronic tongue were treated as y variables. The relationships between the metabolites and taste attributes were evaluated by two-way orthogonal projections to latent structures (O2PLS) analysis. The O2PLS analyses were normalized in two ways, unit variance (UV) and pareto (Par) scaling. The O2PLS (UV) analysis produced 3+1+0 models in Autofit and this model was statistically significant with R2Y (0.73) and Q2 (0.52) metrics. In particular, significant correlations were found between DM or OM and metabolite intensity and taste attributes, and strong associations were found between “sourness” and lysine, “irritant” and alanine and phenylalanine, “saltiness” and pantothenic acid, and “umami” and creatinine and histidine. The O2PLS (Par) analysis of DM generated significant predictive models for “acidic bitterness,” “irritant,” “saltiness,” “bitterness,” “astringency,” and “richness.” Among these, only “irritant” was affected by storage. This method was thus effective in evaluating the taste of yellowtail muscle.