Integration of spectra and image features of Vis/NIR hyperspectral imaging for prediction of deoxynivalenol contamination in whole wheat flour

Abstract

This study was conducted to investigate the feasibility of combining spectral with image information for prediction of deoxynivalenol (DON) contamination in whole wheat flour by Vis/NIR hyperspectral imaging. A total of 195 samples were collected from 13 cities in China with DON contents ranged from 0.0 mg kg−1 to 6.233 mg kg−1. Characteristic spectral variables and color parameters of samples were extracted from hyperspectral images and were integrated to chemometric analysis. Principal component analysis (PCA) indicated a certain clustering tendency between acceptable samples and infected samples when setting 1.0 mg kg−1 as the cutoff value. Subsequently, linear discriminant analysis (LDA) modeling based on spectral and color fusion features obtained correct classified rate of 96.92% for discrimination of acceptable and infected samples, which was much higher than single spectral or image feature. Microscopic analysis indicated that the higher the DON content, the looser the binding of wheat starch granules. However, quantification of DON was not satisfied by partial least squares regression (PLSR) modeling (Rp = 0.691, RMSEP = 0.707 mg kg−1). Overall, these findings verify the potential of hyperspectral imaging for rapid discrimination of DON contamination in whole wheat flour.