Synchronous Detection Method of Physical Quality for Korla Fragrant Pear with Different Damage Types During Storage

Mechanical damage reduces the marketability of Korla fragrant pears, severely restricting industry development. To enhance the commercial value of pears, this study investigated the effects of impact, compressive, and combined impact-compressive damage types on the weight loss rate, L*, a*, and b* of pears, and constructed a multi-output prediction model for the weight loss rate, L*, a*, and b* of damaged pears during storage by integrating partial least squares regression (PLSR), support vector regression (SVR), and long short-term memory (LSTM), from which the optimal prediction model was selected to achieve synchronous detection of the physical quality of damaged pears during storage. The results indicated that during storage, the weight loss rate, a*, and b* of pears subjected to different damage types gradually increased with prolonged storage time, while L* gradually decreased. Under the same damage volume situation, pears subjected to impact-static pressure combined action exhibited the fastest storage quality change speed, followed by impact action, static pressure action. The SVR multi-output model demonstrated optimal performance in predicting the weight loss rate, L*, a*, and b* of damaged pears during storage, achieving mean coefficient of determination R2, root mean square error (RMSE), and residual prediction deviation (RPD) values of 0.988, 0.513, and 10.072, respectively, for these four quality indicators. These results establish a theoretical foundation for the development of simultaneous monitoring techniques for fruit storage quality.