Heterospectral two-dimensional correlation analysis with near-infrared hyperspectral imaging for monitoring oxidative damage of pork myofibrils during frozen storage

Near-infrared (NIR) spectra contain abundant data, heterospectral two-dimensional correlation (H2D-CS) analysis offers a good way to interpret these data. For the first time, H2D-CS was used to correlate the NIR hyperspectral imaging (HSI) data with mid-infrared spectra and to identify feature-related wavebands for developing models for monitoring the oxidative damage of pork myofibrils during frozen storage. The HSI images were acquired at frozen state without thawing and the oxidative damage of myofibrils was assessed by carbonyl content. Results showed that the simplified PLSR model based on H2D-CS identified feature wavebands obtained determination coefficient in prediction (R-P(2)) of 0.896 and root mean square error in prediction (RMSEP) of 0.177 nmol/mg protein, which was better than the partial least square regression (PLSR) model based on full wavebands (R-P(2) = 0.856, RMSEP = 0.209 nmol/mg protein). Therefore, H2D-CS was effective in selecting feature-related wavebands of NIR HSI.