Harnessing machine learning for chicken meat quality evaluation using curcumin-loaded methylcellulose intelligent films

Abstract

In this study, curcumin (Cur) was incorporated into a methylcellulose (MC) biopolymer to develop an intelligent packaging system for real-time monitoring of chicken meat freshness (at 4 ± 2 °C), using machine learning (ML). Artificial neural network (ANN)-based classification and regression models were developed to predict the freshness of chicken meat by analysing colourimetric changes in the MC/3.0% Cur intelligent film. The models were trained using the colour response of the intelligent film to various concentrations (0–1200 ppm) of biogenic amines (histamine, tyramine, putrescine, cadaverine, or spermine). Colour features were quantitatively extracted from digital images of the intelligent film in RGB (Red, Green, Blue), CIE Lab* (lightness, green-red, blue-yellow), and HSV (Hue, Saturation, Value) colour spaces. The ANN regression model achieved a high R² of 0.928, with a low root mean square error (RMSE) of 1.99 (mg N/100 g) for total volatile basic nitrogen (TVB-N) prediction in test-set data, demonstrating precise quantification. The ANN classification model demonstrated 96.5% accuracy in categorising test-set data into fresh, semi-fresh, or spoiled states. The models were applied to predict chicken meat freshness; the classification model accurately predicted freshness, while the regression model showed an R² of 0.901 between predicted and experimental TVB-N values.