Non-destructive evaluation of the edible rate for pomelo using X-ray imaging method.

Flesh content of pomelos sometimes does not match the price due to thick peel, which reduces their commercial value and consumer satisfaction. This study demonstrated the potential use of X-ray imaging method for non-destructive evaluation of the edible rate and the flesh content of 'Hongrou' pomelos and 'Guanxi' pomelos. An adaptive threshold segmentation method was used to segment the X-ray image into background, flesh region and peel region. Then, 2D edible rate and 2D flesh content were defined based on region area ratio and gray level logarithmic sum, respectively, and the multiple linear regression (MLR) models of edible rate and flesh content were established for quantitative analysis. The results showed that the residual predictive deviation (RPD) value of edible rate of 'Hongrou' and 'Guanxi' pomelos in prediction set were up to 2.78 and 2.82, respectively. The hybrid model based on both two cultivars pomelos also achieved good prediction accuracy (RPD = 2.83). In terms of flesh content prediction, the model prediction performance of 'Hongrou' pomelo (RPD = 2.92) was obviously better than that of 'Guanxi' pomelo (RPD = 2.05), and the RPD value of hybrid model in prediction set was 2.62. Further, both two-grade and three-grade linear discriminant analysis (LDA) classifiers were trained to explore the feasibility of using X-ray images to classify the edible rate of pomelos, and the classification accuracy of hybrid samples were 96.7% and 90.0%, respectively. Overall, the features extracted from X-ray image of pomelo could allow the non-destructive evaluation of the edible rate for pomelo. • Two features of X-ray images for predicting the edible rate of pomelo were proposed. • The classification accuracy of pomelo sorting by edible rate was no less than 90%. • X-ray images in different directions could predict the edible rate of pomelo better. • Combining X-ray images with the shape of pomelo could obtain a better model. [ABSTRACT FROM AUTHOR]