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 nondestructive 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.

Automated summary

The X-ray imaging method can be used to nondestructively evaluate the edible rate and flesh content of 'Hongrou' pomelos and 'Guanxi' pomelos. Multiple linear regression models were established using region area ratio and gray level logarithmic sum to quantify the edible rate and flesh content. The results showed high accuracy in predicting the edible rate and flesh content, as well as classifying the edible rate of pomelos using X-ray images.