Detection of whey protein concentrate adulteration using laser-induced breakdown spectroscopy combined with machine learning.

Abstract

In recent years, food fraud issues related to whey protein supplements have disrupted the market and caused significant concern among consumers. Conventional analytical methods such as HPLC and ion exchange chromatography are commonly used to detect adulteration in whey protein supplements. However, these methods are costly, time-consuming and require specialised operation, making them less suitable for a wider range of users. This study presents a rapid and reliable approach for verifying the authenticity of whey protein supplements using laser-induced breakdown spectroscopy (LIBS) and machine learning. Specifically, this approach is employed to identify 15 brands of whey protein concentration (WPC), quantify protein and carbohydrate concentrations, distinguish three types of adulterants, and predict the level of adulteration. The relationship between LIBS data and analyte labels is established using machine learning methods, including partial least squares regression (PLSR), partial least squares discriminant analysis (PLS-DA), and kernel extreme learning machine (K-ELM). The accuracy for identifying WPC brands was over 0.977, and the highest coefficient of determination (R²) for quantifying protein and carbohydrate contents was 0.984 and 0.978, respectively. In addition, different adulterants can be differentiated with accuracies exceeding 0.986, and the R² values for adulteration prediction are above 0.967 in most cases. These results suggest that LIBS combined with machine learning can serve as a viable and efficient solution for detecting adulteration in whey protein supplements.