近红外光谱技术结合神经网络检测苹果咀嚼性  

Detecting chewiness of apple by near infrared spectroscopy technology combined artificial neural network

作　　者：曾祥媛[1] 赵武奇[1] 乔瑶瑶[1] 尹艺然 裴亚利[1] 霍瑶瑶 郭玉蓉[1] 

机构地区：[1]陕西师范大学食品工程与营养科学学院,陕西西安710119

出　　处：《食品与机械》2016年第6期37-40,共4页Food and Machinery

基　　金：农业部现代农业产业技术体系项目(编号:CARS-28)

摘　　要：以不同储藏期的135个苹果样品为研究对象,对其近红外光谱数据进行预处理并通过主成分分析法提取光谱特征,采用人工神经网络技术建立苹果咀嚼性的近红外光谱检测模型。结果表明,对苹果光谱咀嚼性的最佳光谱预处理方法是加权多元散射处理(WMSC)的光谱散射处理方法和"2441"的数学处理方法,通过主成分分析法提取3个主成分作为原始信息的特征变量,建立苹果咀嚼性检测的人工网络模型结构为3—16—1,模型对验证集预测的决定系数为0.992 4,均方根误差为0.000 108 2。近红外光谱技术能对苹果咀嚼性进行快速、无损预测。Near infrared spectroscopy data from 135 apple samples of different storage periods were determined,the charactar of them was extracted and analyzed using principal components analysis.Therefor an ANN model for detection of apple chewiness was established.Our results showed that the preprocessing of spectrum scattering was the weighted multiple scatter correction（WMSC）and mathematics processing was ＂2441＂.The structure of the artificial neural network mode was 3—16—1,established after extracting 3 principle component as the characteristic variables of the original information.The decision coefficient of our model on validation is 0.992 4,and the root mean square error is 0.000 108 2.Our results confirmed that the near infrared spectroscopy technology can use to detect the chewiness of apple rapidly,without forecast destructive.

关 键 词：苹果 无损检测 质构 咀嚼性 近红外光谱 神经网络 

分 类 号：TS255.7[轻工技术与工程—农产品加工及贮藏工程]