Rapid and nondestructive detection of sorghum adulteration using optimization algorithms and hyperspectral imaging

Abstract

This paper proposes a sorghum adulteration detection model using hyperspectral imaging technology (HSI), image processing technology, and multivariate analysis technology. The model used a watershed algorithm to extract hyperspectral data from sorghum grains. Principal component analysis (PCA) and clustering analysis (CA) were used to remove abnormal samples of sorghum. Partial least squares discriminant analysis (PLS-DA) was used to identify the variety of sample, and a sorghum distribution map and adulteration ratios were obtained by marking varieties with different colors. This paper presents, for the first time, HSI use for identification of adulteration in sorghum using PCA and CA. Accuracy of the model identification for the validation set reached 96%, and for the adulterated samples reached 91%, and comprehensive accuracy of the model could reach more than 90%. These results show that the model can rapidly and nondestructively detect sorghum adulteration.