Identification and quantification of adulterated collagen powder by fluorescence hyperspectral technology.

As a popular nutraceutical, collagen powder is the target of adulteration like other high-value food products, therefore detection of adulteration in collagen powder is of great practical significance to ensure market order and the health of the people. To achieve accurate detection of adulterants in collagen powder, a modeling method of fluorescence hyperspectral technology combined with machine learning algorithm was proposed. This study employed various preprocessing methods for denoising and spectral correction to enhance the effective spectral data. Principal component analysis was used to visualize the spectral data and initially revealed the spatial distribution of adulterated collagen powder. Genetic algorithm-k nearest neighbor, particle swarm optimization-support vector machine, and gradient boosting decision tree were used to construct classification models to identify adulterated collagen powder, with the best 2-class discriminant model accuracy, 4-class discriminant model accuracy, and 5-class discriminant model accuracy reaching 99%, 94%, and 98%, respectively. In the quantitative detection models of adulteration level, the random forest model performed best with correlation coefficient (R²) of 0.95 to 0.99. These results suggested that fluorescence hyperspectral technology combined with machine learning algorithm can be effectively used to detect adulterated collagen powder. [ABSTRACT FROM AUTHOR]