Prediction of chicken breast meat freshness based on hyperspectral imaging technique and high-throughput sequencing

Abstract

In this article, the hyperspectral imaging technique and the high-throughput sequencing were combined to construct prediction models for the freshness of chicken breast meat. The quality indicators including color, pH, TVC, TVB-N and TBARS were measured to reflect the freshness changes of chicken breast meat under 4 ℃ storage. Meanwhile, spectral images of chicken breast meat were obtained using visible near-infrared (400-1,000 nm) hyperspectral imaging. Through high-throughput sequencing, the major spoilage bacteria including Pseudomonas, Brochothrix and Escherichia were screened out to construct the models for predicting chicken freshness. After spectral preprocessing and characteristic wavelength selection, the prediction models were established using partial least squares regression (PLSR) and support vector machine (SVM). Among the models, the SNV-PLSR model based on characteristic wavelength for Pseudomonas content (R_p²=0.84, RMSEP=0.38, RPD=3.79) posed stronger predictive and generalization abilities. Therefore, the Pseudomonas count was chosen as a characteristic indicator for establishing an HSI-based prediction model to reflect the freshness of chicken breast meat.