ABSTRACT Minced meat for home cooking is typically made up of 40% mutton and 60% veal, providing a balanced flavor and texture. However, some producers reduce the mutton content to increase profits, leading to consumer dissatisfaction. This study aimed to develop a handheld Visible‐Near Infrared (VIS‐NIR) spectroscopy system coupled with artificial intelligence (AI) algorithms as a rapid, accurate, and non‐destructive method for predicting and grading the mutton‐veal composition of minced meat samples. The weight ratios of the veal in the compositions ranged from 0 to 100% at 2% intervals. Several chemometric and AI methods were used to analyze the spectral data of the prepared minced meat samples captured by a handheld Vis‐NIR spectrometer under LED and bulb light sources. The spectral data were divided for modeling using the stratified and Kennard‐Stone algorithms. Results indicated that the best model was Multi‐Layer Perceptron (MLP) as an artificial neural network algorithm, which achieved R 2 CV and R 2 P (0.879 and 0.916, respectively) for the best scenario of the samples' authenticity prediction. In quality grading, the MLP achieved the highest performance in classifying samples into three quality classes—excellent (Grade A) for 0%–28% veal, medium (Grade B) for 30%–58% veal, and poor (Grade C) for 60%–100% veal—with an accuracy of 92.4%, and precision, recall, and F1 score all reaching 96.2%. These findings confirm that the proposed approach provides a fast, reliable tool for quantifying veal content in minced meat, supporting the development of effective meat quality control systems.
Kazempour et al. (Sun,) studied this question.