Author List: Dongdong Du, Yongkai Ye, Dongfang Li, Jie Fan, Rob BN Scharff, Jun Wang, Fake Shan
Published in: Journal of Food Engineering
Current nondestructive methods for evaluating the texture quality of leafy vegetables have limitations due to their complicated leafy structure. In this study, a promising solution was proposed using 3D scanning technology to nondestructively assess the texture quality of leafy vegetables, and the harvested cabbages were chosen as the experimental samples. The cabbages were scanned to extract the morphological traits, especially for surface features of vein distribution. Results demonstrated that morphological traits exhibited better correlations with texture indices compared to traditional compression features. Texture indices were well predicted based on the XGBoostR algorithm with high R2 values of over 0.89 and low RMSE values. The texture quality of cabbages at different harvesting times analyzed by linear discrimination analysis also showed well-discriminative results with an accuracy exceeding 98.3%. These results successfully indicated that 3D scanning technology was effective in evaluating the texture quality of cabbages, showcasing its potential in the nondestructive texture evaluation of leafy vegetables.
