【作者】 张彪； 刘璇； 毕金峰； 吴昕烨； 金鑫； 李旋； 李潇；

【Author】 Zhang Biao;Liu Xuan;Bi Jinfeng;Wu Xinye;Jin Xin;Li Xuan;Li Xiao;Institute of Food Science and Technology Chinese Academy of Agricultural Science(CAAS),Key Laboratory of Agro-Products Processing,Ministry of Agriculture and Rural Affairs;

【机构】 中国农业科学院农产品加工研究所农业农村部农产品加工重点实验室；

【摘要】 目的:建立苹果原料制干适宜性评价模型,实现基于苹果原料指标预测干制品品质的目标,为苹果制干专用化原料的筛选提供方法依据,为明确苹果干制品品质形成的基础物质提供数据支持。方法:本文以全国主产区34份苹果样本为研究对象,分别测定22项鲜果指标与17项脆片指标,运用因子分析、层次分析建立苹果脆片品质综合评价模型,在此基础上运用BP人工神经网络构建苹果原料制干适宜性评价模型。结果:构建的苹果脆片综合评价模型为Y_综合得分=L*值×0.3724+脆度×0.2665+嘭化度×0.1583+可滴定酸含量×0.0890+可溶性糖含量×0.0569+粗蛋白含量×0.0569。34个苹果鲜果样本制得的脆片综合得分范围为0.2069～0.7933,存在较大差异。基于脆片核心指标与苹果果实品质指标相关性分析结果,筛选出苹果果实的果形指数、果肉a*值、pH、可滴定酸、Vc、果心大小、蛋白质、果肉b*值、密度、可溶性固形物、粗纤维、总糖12项指标作为果实制干适宜性评价的特征指标。以果实特征指标值为输入层,对应苹果脆片综合评分为输出层建立BP神经网络学习模型,可实现苹果原料制干适宜性的定量预测。该方法建立的学习模型有较高的预测准确性与稳定性,变换学习样本得到的三个学习模型的预测值与实际值相对误差均不超过10%,实际值与模型预测值线性拟合后决定系数R2均大于0.95。结论:苹果制干适宜性可由果实的果形指数、果肉a*值、pH、可滴定酸、Vc、果心大小、蛋白质、果肉b*值、密度、可溶性固形物、粗纤维、总糖12项指标进行评价,建立的苹果制干适宜性评价模型具有较高的预测准确性,可实现基于苹果原料指标定量预测其制干适宜性。更多还原

【Abstract】 Objective:The aim of the paper was to establish suitability evaluation model for apple chips-processing from different cultivars and achieve the quality prediction of apple chips based on raw material indicators.Method:34 fresh apple samples 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 BP artificial neural network was used to establish chips-processing suitability evaluation model for apple fruits.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.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,kernel size,protein,b * value(pulp),density,soluble solids content,crude fiber content and total sugar.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 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,kernel size,protein content,b * value(pulp),density,soluble solids content,crude fiber content,total sugar content.The established model can be used to quantitatively predict apple fruit suitability for chips-processing based on the indicators of raw fruits.更多还原