Identification and quantification of adulteration in collagen powder by terahertz spectroscopy - the effect of spectral characteristics on performance is considered.

Terahertz spectroscopy is an emerging rapid detection method that can be used to detect and analyze food quality issues. However, models developed based on various spectral characteristics of terahertz have shown different performances in food identification. Therefore, we preliminarily analyzed the effect of terahertz spectral characteristics on the identification and quantification of collagen powder adulterated with food powders (plant protein powder, corn starch, wheat flour) with the use of random forest (RF), linear discriminant analysis (LDA), and partial least squares regression (PLSR), and determined the spectral characteristics suitable for identification and quantitative analysis. Then, the selected spectral characteristics data were preprocessed using baseline correction (BC), gaussian filter (GF), moving average (MA), and savitzky-golay (SG). Feature variables were extracted from preprocessed spectral characteristics data using genetic algorithm (GA), random forest (RF), and least angle regression (LAR). The study indicated that the BC-GA-LDA classification model based on the absorption coefficient spectra achieved an accuracy of 96.96% in identifying adulterated collagen powder. Additionally, the GA-PLSR model developed based on the power spectra demonstrated excellent performance in predicting adulteration levels, with the coefficient of determination (Rp2) values ranging from 0.93 to 0.99. The results showed that the rational selection of terahertz spectral characteristics is highly feasible for the accurate detection of collagen powder adulteration.