Application of MIR-FTIR spectroscopy and chemometrics to the rapid prediction of fish fillet quality

Fourier transform mid-infrared spectroscopy (MIR-FTIR) and a partial least square algorithm (PLS-1) were used to predict the deterioration indices, pH, and chemical composition of Atlantic bluefin tuna, crevalle jack, and Atlantic Spanish mackerel chilled fillets. To build calibration models, 90 samples from the 3 fish species were analysed to different seasons and were stored for various times. The performance of the regression models was evaluated based on the coefficients of determination (R²), residual predictive deviation of cross-validation (RPDcv), and percentage relative difference (% RD). Chemometric models provided good reliability in the prediction of the chemical composition (R² between 0.969 and 0.992, RPDcv between 5.01 and 5.59%), the pH (R² = 0.987, RPDcv = 7.18), and can be used for screening of deterioration indices (R² between 0.944 and 0.969, RPDcv between 3.21 and 3.67%). The results demonstrated that the MIR-FTIR coupled with the PLS-1 algorithm could be simultaneously applied to predict the chemical parameters of chilled fillets of three fish species.