Porphyrin fluorescence imaging for real-time monitoring and visualization of the freshness of beef stored at different temperatures

•Real-time visualization of beef deterioration distribution by fluorescence imaging.•Fluorescence property of porphyrin was highly correlated with beef spoilage.•Gompertz growth curves for TVC at different storage temperatures were built.•Quantitative model for predicting microbial indicator was built by SVR algorithm.•Establish classification models for beef freshness by SVM, KNN and RF algorithms. This study presents a novel fluorescence imaging method for the real-time monitoring of beef quality deterioration and freshness. The fluorescence property of porphyrin in the form of heme can be used to characterize quality changes in beef during storage. Therefore, a fluorescence imaging system with an excitation light source of 440 nm and a CCD camera with a specific wavelength filter of 595 nm was constructed, and the porphyrin fluorescence images of beef samples stored at different temperatures were then collected. The quantitative model for predicting the microbial freshness indicator (TVC) of beef was built with the support vector machine regression (SVR) algorithm and produced satisfactory results with Rc2 and Rp2 of 0.858 and 0.812, respectively. The classification model based on the support vector machine (SVM) algorithm classified beef freshness into “fresh” and “spoiled”, with calibration and prediction accuracy of 100 % and 90.9 %, respectively.