Prediction Model for Quality Changes in Repeatedly Frozen-Thawed Pork Based on MRI Scans and Chemometrics

This study investigated fresh pork and pork subjected to repeated freeze-thaw cycles. The effects of freeze-thaw treatments on water status, WHC, and quality attributes of pork were systematically analyzed, and a nondestructive prediction method for WHC based on magnetic resonance imaging (MRI) was developed. The results showed that increasing freeze-thaw cycles significantly reduced moisture content and increased drip loss, indicating a continuous deterioration of overall WHC. Texture parameters and shear force values decreased markedly, suggesting that muscle structure was progressively damaged by ice crystal formation and recrystallization. T₂-weighted MRI pseudo-color scans clearly reflected changes in internal water distribution, with high-signal regions gradually decreasing as freeze-thaw cycles increased, which was consistent with the experimentally measured trends in moisture content and WHC. Based on MRI features, principal component regression (PCR) and partial least squares regression (PLSR) models were established to predict pork WHC. The PCR model extracted 16 principal components (cumulative contribution rate of 96.394%), with calibration set results of Rc² = 0.8825 and RMSEC = 1.7959, and validation set results of Rp² = 0.8856 and RMSEP = 2.0284. The optimal number of latent variables for the PLSR model was six, yielding calibration set results of Rc² = 0.9634 and RMSEC = 1.0026, and validation set results of Rp² = 0.9656 and RMSEP = 1.1119, with all residuals less than 1. Overall, the combination of MRI and chemometric methods, particularly the PLSR model, enables rapid, nondestructive, and accurate prediction of pork WHC, providing a useful tool for quality evaluation under repeated freeze-thaw conditions and for quality control in pork processing, storage, and cold-chain management.