Development of a predictive classification model and extraction of signature wavelengths for the identification of spoilage in chicken breast fillets during storage using Near Infrared Spectroscopy

Abstract

Technologies for rapid identification and prediction of food spoilage can be crucial in minimizing food waste and losses, although their efficiency requires further improvement. This study aimed to pinpoint specific near-infrared (NIR) wavelengths that could indicate spoilage in raw chicken breast fillets. In this study, commercial tray-packs of boneless, skinless chicken breast fillets stored in a walk-in cooler at 4˚C were periodically tested every other day until they reached the spoilage state (identified by > 7 log CFU/ml). A portable Hyper spectral spectroscopy device (Field Spec Hi-Res4), with a range of wavelengths of 350–2500 nm, was used to measure reflectance. In addition to hyper-spectral analysis, aerobic plate counts were conducted on the fillets. The data from these counts were then used to train a Back Propagation Neural Network (B.P.N.N.) with specific parameters (250,000 steps, a learning rate of 0.02, and 5 hidden layers) and Linear-Support Vector machines (SVM-Linear) with ten-fold cross-validation technique to categorize spoilage into three stages: baseline microbial count (up to 3 log CFU/ml) (Initiation), propagation (between 3 and 6.9 log CFU/ml), and spoiled (> 7 log CFU/ml). The feature extraction process successfully identified the most representative six signature wavelengths from the whole hyper-spectral profile, which facilitated the classification of different phases of spoilage. The BPNN model demonstrated a high classification accuracy, with 93.7% for baseline counts, 95.2% for the propagation phase, and 98% for the spoiled category. These signature hyperspectral wavelengths hold the potential for developing cost-effective and rapid food spoilage detection systems, particularly for perishable items.