Integration of Fluorescence Spectroscopy Along with Mathematical Modeling for Rapid Prediction of Adulteration in Cooked Minced Beef Meat

This study explores the potential of fluorescence spectroscopy (FS), coupled with principal component analysis (PCA) and partial least square regression (PLSR), to detect meat adulteration rapidly and non-destructively in cooked minced beef. We aimed at evaluating FS as a simple and efficient tool for identifying cheaper meat species, that is chicken, used as adulterants in beef. Fluorescence spectra were collected at one fixed emission wavelength (410 nm) and three excitation wavelengths (290, 322, and 340 nm) from both pure and adulterated cooked meat samples. Adulteration levels ranging from 10% to 90% were assessed by mixing chicken meat with beef, followed by fluorescence analysis. The results indicated that the PCA model explained 100% of the variance, with 96% accounted for by the first principal component, showing clear discrimination between pure and adulterated samples. PLSR models demonstrated excellent predictive accuracy, with cross-validated coefficients of determination of 0.95, highlighting FS's capability in distinguishing between pure and adulterated meats even after cooking. The cross-validated grouping success rate was ~97%, reinforcing the reliability of the technique. This study represents the first investigation using FS to predict adulteration in cooked meat, providing a benchmark for future research. The findings suggest that FS, in combination with mathematical modeling, holds great promise as a rapid, cost-effective, and nondestructive method for detecting meat adulteration, with significant potential for practical application in food industry quality control.