Abstract:The physiological characteristics of Fuji apples change during the post-ripening process of storage. If the storage time is too short, the best edible quality cannot be achieved. Excessive storage will seriously reduce the quality, then affects the quality of out-of-warehouse and the selling price. In order to make the fruits during the storage period with better quality for sale, the study on the quality prediction model of apple during storage was carried out, and on this basis, the out-of-warehouse quality of apple was evaluated and predicted. The near-infrared spectrum and quality indexes (soluble solid content (SSC), hardness and weight loss rate) of apple at different times during the whole storage period were collected. The variation rule of fruit diffuse reflectance spectrum and quality index during storage was analyzed. Partial least squares (PLS) and nonlinear autoregressive with external input (NARX) prediction model for apple quality during storage was established based on the diffuse reflectance spectrum in the wavelength range of 1000~2400nm, combined with pretreatment and feature wavelength extraction. According to apple industry standards, the judgment basis of apple out-of-warehouse quality was determined, and the TOPSIS method based on entropy weight was used to comprehensively evaluate the fruit out-of-warehouse quality, and realize the prediction of the quality score by PLS and the prediction of multiple quality indexes by NARX. The results showed that when predicting SSC, hardness and weight loss rate, the optimal models were CARS-SPA-PLS, CARS-NARX and SPA-NARX, respectively, the correlation coefficients were 0.914, 0.796 and 0.918, and the root mean square errors were 0.511°Brix, 0.475kg/cm and 0.682%. When predicting quality scores, the correlation coefficient and root mean square error of the PLS model were 0.896 and 0.0434, respectively, the correlation coefficient of the NARX multi-output model were 0.794, 0.785 and 0.905, and the root mean square errors were 0.308°Brix, 0.492kg/cm2 and 0.714%. The application of near-infrared spectroscopy technology can realize the detection of fruit storage quality and the screening of quality of out-of-warehouse, and the research result can provide a method for efficient storage management technology.
