Quantitative detection of restructured steak adulteration based on hyperspectral technology combined with a wavelength selection algorithm cascade strategy

Abstract

This study aimed to create simplified models to rapidly and non-destructively predict the content of adulterated meat in restructured steak based on hyperspectral technology. The hyperspectral data for restructured steaks mixed with different proportions of pork and duck were collected, and then six pre-treatment methods were used to pre-process the spectral data. Importantly, the wavelength selection algorithm cascade strategy, that is, combined the wavelength range selection and wavelength band selection methods including successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), variables combination population analysis (VCPA), interval random frog (iRF), iRF-SPA, iRF-CARS, and iRF-VCPA, were employed to establish a partial least squares (PLS) prediction model for adulterated content. The results showed that the best pre-treatment methods for beef adulterated with pork and duck were Mean centering (MC) and Savitzky-Golay (SG) respectively, and the corresponding best wavelength selection method was iRF-CARS.