Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen-thawed minced beef

This study aimed to evaluate the potential of near infrared spectroscopy (NIRS) as a fast and non-destructive tool for detecting and quantifying different adulterants in fresh and frozen-thawed minced beef. Partial least squares regression (PLSR) models were built under cross validation and tested with different independent data sets, yielding determination coefficients (R_P²) of 0.96, 0.94 and 0.95 with standard error of prediction (SEP) of 5.39, 5.12 and 2.08% (w/w) for minced beef adulterated by pork, fat trimming and offal, respectively. The performance of the developed models declined when the samples were in a frozen-thawed condition, yielding R_P² of 0.93, 0.82 and 0.95 with simultaneous augments in the SEP of 7.11, 9.10 and 2.38% (w/w), respectively. Linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA) and non-linear regression models (logistic, probit and exponential regression) were developed at the most relevant wavelengths to discriminate between the pure (unadulterated) and adulterated minced beef. The classification accuracy resulting from both types of models was quite high, especially the LDA, PLS-DA and exponential regression models which yielded 100% accuracy. The current study demonstrated that the VIS-NIR spectroscopy can be utilized securely to detect and quantify the amount of adulterants added to the minced beef with acceptable precision and accuracy.