Rapid determination of two illegal additives in wheat flour by near-infrared spectroscopy and different key wavelength selection algorithms

Abstract

Rapid and accurate testing of wheat flour for illegal additives is important for maintaining food safety. In this study, a combination of NIR spectroscopy and partial least squares regression (PLSR) was used to predict the levels of two illegal additives (talc powder and benzoyl peroxide) in wheat flour. Different critical wavelength selection algorithms (competitive adaptive reweighted sampling, CARS; random frog, RF; Monte Carlo uninformative variable elimination, MCUVE; CARS-RF; CARS-MCUVE) were used to filter key wavelengths. In our study, R2P for talc and benzoyl peroxide in the full-wavelength PLSR model was 0.995 and 0.964, respectively. The CARS-RF and RF were screened for 10 effective wavelengths. In wheat flour containing talc, the R2P and RPD of the model using the CARS-RF were 0.995 and 14.796, respectively. In wheat flour containing benzoyl peroxide, the R2P and RPD of the model using the RF were 0.923 and 3.697, respectively. CARS-RF and RF algorithms had great potential to accurately predict illegal additives.