Hyperspectral imaging combined with fluorescence for the prediction of microbial growth in chicken breasts under different packaging conditions

Abstract

The crucial feature of chicken is real-time microbial growth detection along the supply chain. Hyperspectral imaging (HSI) is an online and nondestructive technique that is well suited to microorganism analysis applications. In this study, visible and near-infrared (Vis/NIR, 400–1000 nm) HSI were utilized to establish prediction models of the total viable count (TVC) of chicken breasts stored in vacuum, pallet, and combined packaging using partial least squares regression (PLSR) and support vector machine (SVM) after spectral pretreatments and optimal wavelength selection. The SVM algorithm was used to develop the best TVC prediction models for storage, with the following outcome: vacuum (R2p = 0.91, RMSEp = 0.47, RPD = 2.44), pallet (R2p = 0.90, RMSEp = 0.54, RPD = 2.32), and combined (R2p = 0.89, RMSEp = 0.58, RPD = 2.21) packs. Successively, HSI combined with fluorescence (F–HSI) was used to predict the TVC and coliforms of chicken breasts in combined packaging. Compared to HSI, F–HSI achieved the best-predicted results for TVC (R2p = 0.93, RMSEp = 0.44, RPD = 2.74) and coliforms (R2p = 0.92, RMSEp = 0.46, RPD = 2.57). These results indicated that fluorescence, as a complementary technique for HSI, will be widely used in the chicken supply chain to predict microbial growth.