冷凍鶏肉のハイパースペクトルイメージングを用いた同時検出と可視化

The quality degradation of frozen meat during storage is mainly attributed to oxidative reactions in lipids and proteins. This study aimed to evaluate lipid and protein oxidation in frozen-thawed chicken meat by employing hyperspectral imaging (HSI). During ten freeze-thaw (F-T) cycles, the thiobarbituric acid reactive substances (TBARS) content in chicken meat increased from 0.0910 to 0.5120 mg/kg, and the carbonyl content increased from 1.0560 to 3.9550 nmol/mg. Correlation analysis result indicated a strong correlation (r = 0.982) between TBARS and carbonyl content. To address the inefficiency of conventional methods that require training separate models for each indicator, a novel multi-task deep learning framework integrating gramian angular difference fields (GADF) with multi-task convolutional neural network (MTCNN) was proposed to achieve end-to-end simultaneous prediction of TBARS and carbonyl content in a computer vision processing paradigm. The developed GADF-MTCNN model demonstrated superior predictive performance for correlated tasks, significantly outperforming the compared single-output deep learning models, with prediction results of R²_p = 0.9458, RMSEP = 0.0296 mg/kg, and RPD = 4.3030 for TBARS, and R²_p = 0.9545, RMSEP = 0.1905 nmol/mg, and RPD = 4.7014 for carbonyl content. Moreover, lipid and protein oxidation were simultaneously visualized in a pixel-wise manner by transferring the established GADF-MTCNN model to hyperspectral images, which contributes to the management of storage and sales. The results indicated the combination of HSI and GADF-MTCNN has great potential for evaluating lipid and protein oxidation in frozen-thawed chicken meat.