Authentication and quality assessment of whey protein-based sports supplements using portable near-infrared spectroscopy and hyperspectral imaging.

Whey protein supplements are gaining increasing popularity among health and fitness enthusiasts due to their ability to enhance protein anabolism and promote muscle recovery and building. The growing demand for whey protein supplements has led to a high incidence of food fraud, including the addition of cheap proteins and non-protein nitrogen sources, posing significant health risks and economic losses. This study presents the use of portable near-infrared (NIR) spectroscopy and visible near-infrared hyperspectral imaging (HSI) combined with machine learning to evaluate the quality and authenticity of whey protein supplements. Specifically, NIR and HSI data from 15 brands of whey protein concentration (WPC) samples were analysed using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and kernel extreme learning machine (K-ELM), demonstrating distinct class separability and excellent classification accuracy. The protein and carbohydrate contents of the samples were effectively quantified using partial least squares regression (PLSR) and K-ELM, yielding the lowest root mean square error (RMSE) of 0.023 for both predictions. Moreover, useful spectral fingerprints related to protein and carbohydrate contents were identified based on the regression coefficients. In addition, three common adulterants, including maltodextrin, wheat flour and milk powder, at concentrations ranging from 5% to 50% (w/w) in WPC, were accurately detected and quantified. The RMSE for quantifying adulterant levels ranged from 0.009 to 0.026. These results suggest that NIR spectroscopy and HSI, in combination with machine learning, can provide a reliable and practical solution for assessing the quality and authenticity of whey protein supplements.