Nondestructive discrimination of internal defects in jujube (Huizao) of Xinjiang based on visible and near-infrared spectroscopy

Abstract

During harvest and transport, defects are most likely to affect the interior of jujubes and thus shorten their storage period. This study applied visible and near-infrared transmission spectroscopy to detect such internal defects. Spectra were acquired on the equator area at 0, 90, 180, and 270 degrees of each sample, and a model was constructed to obtain three-dimensional damage and defect detection model. The first derivative, multiplicative scatter correction, standard normal variate, and median filtering were used for preprocessing. Modeling by mean spectra achieved a better effect than using unidirectional spectra. Then, naive Bayes classifier and support vector machine were employed for the model establishment at 600–950?nm and 680–950?nm bands, respectively, using mean spectra. Median filtering effectively improved the signal to noise ratio and the discrimination accuracy of the support vector machine model at 600–950?nm reached 96.77%, which was the best value among all models. This result indicates that the support vector machine model was the optimum model and 600–950?nm was a suitable data range for the detection of internal defects. This research confirms the feasibility of implementing visible and near-infrared spectroscopy for the detection of internal defects in jujubes.