Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning

Abstract

The main objective of this study was to evaluate the potential of visible near-infrared (VNIR) hyperspectral imaging (400–1000 nm) and machine learning to detect adulteration in fresh minced beef with chicken. Minced beef samples were adulterated with minced chicken in the range 0–50% (w/w) at approximately 2% intervals. Hyperspectral images were acquired in the reflectance (R) mode and then transformed into absorbance (A) and Kubelka–Munck (KM) units. Partial least squares regression (PLSR) models were developed to relate the three spectral profiles with the adulteration levels of the tested samples. These models were then validated using different independent data sets, and obtained the coefficient of determination (R2p) of 0.97, 0.97, and 0.96 with root mean square error in prediction (RMSEP) of 2.62, 2.45, and 3.18% (w/w) for R, A and KM spectra, respectively. To reduce the high dimensionality of the hyperspectral data, some important wavelengths were selected using stepwise regression. PLSR models were again created using these important wavelengths and the best model was then transferred in each pixel in the image to obtain prediction map. The results clearly ascertain that hyperspectral imaging coupled with machine learning can be used to detect, quantify and visualize the amount of chicken adulterant added to the minced beef.