A pH-Responsive colourimetric sensor array based on machine learning for real-time monitoring of beef freshness

A novel strategy based on a pH-responded colourimetric sensor array (CSA) was developed by combining it with machine learning models to effectively monitor the freshness of beef in real time through the detection of total volatile basic nitrogen (TVB-N) contents. A total of 168 colour features were calculated and effective colour features sensitive to the changes in TVB-N contents were extracted by sequential forward selection (SFS), random forest (RF), and principal component analysis (PCA). Besides, the linear regression model of partial least squares regression (PLSR) as well as nonlinear regression models of random forest regression (RFR) and support vector machine regression (SVR) were established to predict TVB-N values of beef at 28 °C based on full and effective colour features. The RF-SVR model had the best performance with a corresponding determination coefficient of prediction (Rp2) of 0.9596, a root-mean-square error of prediction (RMSEP) of 1.89 mg/100 g, and a relative prediction deviation (RPD) of 4.98. Moreover, the CSA based on the RF-SVR model was applied to the quantitative analysis of beef freshness at 4 °C storage, validated by using a standard method for the detection of TVB-N contents in beef samples. The results illustrated that the CSA as an objective and nondestructive tool can monitor TVB-N contents for the evaluation of beef freshness with the help of machine learning.