关键词: 苹果, 脆片, 干制, 适宜性评价, BP神经网络

Abstract:

【Objective】The aim of the paper was to establish suitability evaluation model for apple chips-processing from different cultivars and to achieve the quality prediction of apple chips based on raw material indicators.【Method】34 fresh apple samples of 21 apple varieties from 7 major growing regions were selected as research objects. Factor analysis (FA) and analytic hierarchy process (AHP) were used to establish comprehensive quality evaluation model for chips, and Error Back Propagation (BP) artificial neural network was used to establish chips-processing suitability evaluation model for apple fruits. (1) Chips were prepared by instant controlled pressure drop (DIC, French for détente instantannée controlee, also known as explosion puffing) and 17 indicators were measured. The core indexes of chips were selected by FA and correlation analysis. The weights of the core indexes were determined by AHP, and then the comprehensive quality evaluation scores of chips were calculated. (2) 22 indicators of 34 fruit samples with different cultivars and regions were measured. Then the characteristic indicators of apple fruits related to chip qualities were screened out by correlation analysis between data groups of apple fruit indicators and chip core indexes. Learning model with input of fruit characteristic indicators and output of chip comprehensive evaluation scores was established by database of 29 apple samples. 5 apple samples were chosen as test samples to verify the prediction accuracy of the learning model. Modified leaning models from different sample groups were compared by prediction accuracy, which could be the evidence to evaluate rationality and stability for application of BP neural network in the present research.【Result】The results showed that L* value, brittleness, puffing degree, titratable acid, soluble sugar and crude protein of apple chip were determined as the core indexes which the weights were 0.3724, 0.2665, 0.1583, 0.0890, 0.0569 and 0.0569, respectively. The comprehensive quality scores of chips from 34 apple samples ranged from 0.2069 to 0.7933, indicating significant variation. The top 3 apple samples with high scores were Liaoning Huahong, Liaoning Huajin and Shandong Yanfu 6, and the final ranking for Shanxi Qinguan. Correlation analysis was performed between core indexes of chips and quality indicators of apple raw materials to achieve characteristic indicators of apple fruits, including the fruit shape index, a* value (pulp), pH value, titratable acid content, Vc content, proportion of core, protein content, b* value (pulp), density, soluble solids content, crude fiber content and total sugar content. Therefore, learning models were established with input layer of the characteristic indicators value of fruit and output layer of the comprehensive quality score of apple chip, which could predict the comprehensive quality of apple chips from indicators of raw materials. Moreover, the model showed high prediction accuracy. The relative errors between the predicted and actual values of the three learning models groups did not exceed 10%, and the coefficients of determination R ² of linear fitting were higher than 0.95.【Conclusion】Suitability evaluation of apple fruit for chips-processing could be evaluated by fruit shape index, a* value (pulp), pH value, titratable acid content, Vc content, proportion of core, protein content, b* value (pulp), density, soluble solids content, crude fiber content and total sugar content. The established model could be used to quantitatively predict apple fruit suitability for chips-processing based on the indicators of raw fruits.

Key words: apple, chips, dehydration, suitability evaluation, BP neural network