Prediction of pH of fresh chicken breast fillets by VNIR hyperspectral imaging

Abstract

Visible and near-infrared (VNIR) hyperspectral imaging (400–900 nm) was used to evaluate pH of fresh chicken breast fillets (pectoralis major muscle) from the bone (dorsal) side of individual fillets. After the principal component analysis (PCA), a band threshold method was applied to the first principal component (PC1) score image in order to get the region of interest (ROI). Then, the average reflective spectrum of ROI of each image was acquired by inverse PCA transform. Eight pretreatment algorithms were evaluated for partial least squares regression (PLSR) models. The PLSR model with the pretreatment of multiplicative scatter correction followed by second derivative showed the best performance with coefficients of determination for validation (Rv2) of 0.87, root mean square error for validation (RMSEv) of 0.16 and the ratio of percentage deviation (RPD) of 2.02. Optimal 20 wavelengths were selected using competitive adaptive reweighed sampling (CARS) method to develop a new multispectral PLSR model, leading to an enhanced result with Rv2 of 0.94, RMSEv of 0.06 and RPD of 3.55. To assess the performance of the prediction models, new ROIs where pH values were measured using a pH probe, were defined and corresponding mean spectra were used as an independent test set of the new multispectral PLSR model. Coefficients of determination for independent test set (Rp2) and root mean square error for independent test set (RMSEp) were 0.73 and 0.29, respectively. The prediction image showing the spatial distribution of the predicted pH values was generated to analyze the spatial context of pH values as well as the overall pH level of each fillet. The results demonstrated that VNIR hyperspectral imaging could be used to predict spatial and global pH values of fresh chicken breast meat.