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食品与发酵工业  2021, Vol. 47 Issue (7): 211-216    DOI: 10.13995/j.cnki.11-1802/ts.025671
  分析与检测 本期目录 | 过刊浏览 | 高级检索 |
基于傅里叶近红外光谱和电子鼻技术的苹果霉心病无损检测
杨晨昱1, 袁鸿飞1,2, 马惠玲3, 任亚梅1*, 任小林4
1(西北农林科技大学 食品科学与工程学院,陕西 杨凌,712100)
2(河南省口岸食品检验检测所,河南 郑州,450003)
3(西北农林科技大学 生命科学学院,陕西 杨凌,712100)
4(西北农林科技大学 园艺学院,陕西 杨凌,712100)
Nondestructive detection of apple moldy core based on FT-NIR and electronic nose technology
YANG Chenyu1, YUAN Hongfei1,2, MA Huiling3, REN Yamei1*, REN Xiaolin4
1(College of Food Science and Engineering,Northwest A & F University,Yangling 712100,China)
2(Food Inspection and Testing Institute of Henan Province,Zhengzhou 450003,China)
3(College of Life Science,Northwest A & F University,Yangling 712100,China)
4(College of Horticulture,Northwest A & F University,Yangling 712100,China)
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摘要 为研究傅里叶近红外光谱技术(Fourier transform near infrared spectroscopy,FT-NIRS)和电子鼻技术分别结合化学计量学方法对苹果霉心病的判别效果,以“红富士”霉心病苹果和健康苹果为试材,利用近红外光谱技术,基于主成分分析建立Fisher判别和多层感知器(multi-layer perceptron,MLP)神经网络模型;同时利用电子鼻技术分别结合Fisher判别、MLP神经网络和径向基函数神经网络3种化学计量学的方法建立判别模型。根据建模集和验证集的预测准确率综合考虑,基于主成分分析建立的MLP神经网络模型和电子鼻结合MLP神经网络模型对苹果霉心病的判别效果最好,验证集中的正确判别率分别达到87.7%和86.2%。说明电子鼻和近红外光谱技术均可以较好地判别苹果霉心病。
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杨晨昱
袁鸿飞
马惠玲
任亚梅
任小林
关键词:  苹果  霉心病  近红外光谱  电子鼻  化学计量学    
Abstract: Fourier near-infrared spectroscopy (FT-NIRS) and electronic nose technique were used to identify the efficacy of apple moldy core in combination with chemometrics.With “Red Fuji” moldy core and healthy apples as raw materials, the near-infrared spectroscopy model of multi-layer perceptron (MLP) neural network and Fisher discriminant was established based on the principal component analysis.Meanwhile, a discriminant model was established by the electronic nose combined with Fisher discrimination, MLP neural network and radial basis function (RBF) neural network, respectively.According to the comprehensive consideration of the prediction accuracy of the modeling set and the verification set, the MLP neural network model based on principal component analysis and the electronic nose combined with the MLP neural network model had the best discriminating effect on the apple moldy core disease and the correct discriminating rate of verification set reached 87.7% and 86.2% respectively.It shows that the electronic nose and near-infrared spectroscopy used for distinguishing apple mold core are feasible.
Key words:  apple    moldy core    FT-NIRS    electronic nose    chemometric
收稿日期:  2020-09-15      修回日期:  2020-10-24           出版日期:  2021-04-15      发布日期:  2021-05-20      期的出版日期:  2021-04-15
基金资助: 现代农业产业技术体系建设专项项目(Z225020701);陕西省农业科技创新与攻关项目(2019NY-112)
作者简介:  硕士研究生(任亚梅副教授为通讯作者,E-mail:715189648@qq.com)
引用本文:    
杨晨昱,袁鸿飞,马惠玲,等. 基于傅里叶近红外光谱和电子鼻技术的苹果霉心病无损检测[J]. 食品与发酵工业, 2021, 47(7): 211-216.
YANG Chenyu,YUAN Hongfei,MA Huiling,et al. Nondestructive detection of apple moldy core based on FT-NIR and electronic nose technology[J]. Food and Fermentation Industries, 2021, 47(7): 211-216.
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http://sf1970.cnif.cn/CN/10.13995/j.cnki.11-1802/ts.025671  或          http://sf1970.cnif.cn/CN/Y2021/V47/I7/211
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