Detection of adulteration of minced beef based on hyperspectral reflectance and transmission fusion technique

This study aims to improve the accuracy of the detection of adulterated minced beef with chicken, duck, and pork. A series of tests were carried out to quantitatively detect the adulterants in beef using hyperspectral reflectance (R) and transmittance (T) spectral data fusion. Firstly, three types of adulterants (chicken, duck, and pork) were added to the beef with a mass percentage of 2% to 50% concentration interval of 2%, respectively. Then the reflectance spectral data was collected to combine with SVM (support vector machine, SVM), random forest (RF), and long short-term memory (LSTM). A classification model was established to classify the samples into chicken, duck, and pork adulterated and pure beef samples (4 groups). Then, a partial least squares model (PLSR) was built from the single reflectance and transmittance spectral data to quantify the concentrations of adulterants. The performance of the model was optimized by competitive adaptive reweighted sampling (CARS), irrelevant variable elimination (UVE), and primary and intermediate data fusion. The predictions were also conducted on adulterants such as chicken, duck, and pork, according to the intermediate-level data fusion. The results showed the greatest effectiveness values of improvement were 3.9%, 9.3%, and 4.5% over those in the optimal model with single spectral data, respectively. The models with the best prediction results for chicken and duck samples withadulterants were the de trending (DT) pre-processed UVE-PLSR, with corresponding coefficients of determination (r-squaredprediction, ) and root mean square error (RMSEP) of 0.984 5 and 1.8651、 0.986 0 and 1.7711, respectively, and the bestprediction model for adulterated pork samples was RAW-CARS-PLSR, with corresponding and RMSEP of 0.975 1 and 2.366 5. Hyperspectral imaging combined with data fusion can effectively detect the adulterants in beef with high accuracy and speed. The intermediate-level data fusion can maximize the performance of the improved model.