Metabolomics-based framework for the quantitative evaluation of coriander leaf freshness using cumulative respiratory CO₂ as a physiological reference

A metabolomics-based framework for evaluating coriander freshness was developed using cumulative respiratory CO₂ as a physiological reference. Primary metabolite profiles obtained by gas chromatography–tandem mass spectrometry (GC–MS/MS) were used to develop a partial least squares regression (PLSR) model for predicting cumulative respiratory CO₂ production. The model showed moderate predictive performance under leave-one-out cross-validation, with cross-validated predictive ability (Q²) of 0.717 and root mean square error of cross-validation (RMSECV) of 38.86 mmol kg⁻¹ . Among 64 predictors, variable importance in projection (VIP) analysis identified tyramine, serine, lysine, isoleucine, and threonine as metabolites with high model importance. In an independent 10 °C application experiment, the model-derived cumulative CO₂ increased with storage duration, whereas leaf yellowness did not change significantly, suggesting that the metabolomics-based approach may capture storage-associated physiological change that is not readily reflected by conventional visual assessment. The framework was further applied under modified-atmosphere conditions generated by two packaging configurations. The model-derived values differed substantially from the independent gas-balance estimates under the strongly hypoxic 0.08-mm LDPE condition, suggesting limited quantitative transferability under physiological conditions not represented in the training dataset. These results support metabolite-based prediction as a framework for evaluating storage-associated physiological change in coriander while highlighting the need for further validation under a wider range of postharvest conditions.