食品与发酵工业 >

2022 , Vol. 48 >Issue 2: 267 - 273

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.026717

分析与检测

基于电子鼻表征霉心病苹果特征气味及无损检测模型建立

  • 张建超 ,
  • 张鹏 ,
  • 薛友林 ,
  • 贾晓昱 ,
  • 李江阔
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  • 1(辽宁大学 轻型产业学院,辽宁 沈阳,110036)
    2(天津市农业科学院农产品保鲜与加工技术研究所,天津,300384)
    3(国家农产品保鲜工程技术研究中心(天津),农业农村部农产品贮藏保鲜重点实验室,天津市农产品采后生理与贮藏
    保鲜重点实验室,天津,300384)
硕士研究生(张鹏副研究员和李江阔研究员为共同通信作者,E-mail:zhangpeng811202@163.com;lijkuo@sina.com)

收稿日期: 2021-01-11

  修回日期: 2021-02-14

  网络出版日期: 2022-02-28

基金资助

兵团重点领域科技攻关项目(2019AB024)

收起

Characterization of characteristic odor and establishment of nondestructive detection model of core rot apples based on electronic nose

  • ZHANG Jianchao ,
  • ZHANG Peng ,
  • XUE Youlin ,
  • JIA Xiaoyu ,
  • LI Jiangkuo
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  • 1(College of Light Industry, Liaoning University, Shenyang 110036, China)
    2(Institute of Agricultural Products Preservation and Processing Technology, Tianjin Academy of Agricultural Sciences, Tianjin 300384, China)
    3(Tianjin Key Laboratory of Postharvest Physiology and Storage of Agricultural Products, Key Laboratory of Storage of Agricultural Products, Ministry of Agriculture and Rural Affairs, National Engineering and Technology Research Center for Preservation of Agricultural Products (Tianjin), Tianjin 300384, China)

Received date: 2021-01-11

  Revised date: 2021-02-14

  Online published: 2022-02-28

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摘要

为探究电子鼻检测技术对霉心病苹果的判别效果,以富士健康苹果和霉心病苹果为试材,基于SIMCA软件对采集的不同病变程度霉心病苹果的电子鼻信息进行表征,基于SPSS 23.0软件建立霉心病苹果Fisher函数、多层感知器神经网络(muhilayer perceptron neural network,MLPNN)和径向基函数神经网络(radial basis function neural network,RBFNN)判别模型。结果表明,健康果的特征传感器有W5C、W1C和W3C,重度果的特征传感器有W1S、W2S、W1W和W5S;MLPNN模型的判别效果最好,其对训练集和验证集的总体预测判别率分别为88.61%和88.46%;RBFNN模型的判别效果次之,其对训练集和测试集的总体预测判别率分别为93.50%和80.95%;Fisher判别函数判别效果最差,其对训练集和验证集的总体预测判别率分别为91.50%和79.27%。另外,3种判别模型对健康果和重度果都有很好的判别效果,对健康果和轻度果的判别效果不理想,需要在今后的研究中进一步优化。

关键词： 霉心病苹果; 无损检测模型; 电子鼻技术; 化学计量学方法

本文引用格式

张建超 , 张鹏 , 薛友林 , 贾晓昱 , 李江阔 . 基于电子鼻表征霉心病苹果特征气味及无损检测模型建立[J]. 食品与发酵工业, 2022 , 48(2) : 267 -273 . DOI: 10.13995/j.cnki.11-1802/ts.026717

Abstract

To explore the discrimination effect of the electronic nose on core rot apples, “Fuji” moldy core and healthy apples were used as test materials and the electronic nose information of core rot apples was characterized based on SIMCA software. In addition, the fisher discriminant function, multilayer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN) discriminant models of core rot apple was established. The results showed that W5C, W1C and W3C were the characteristic sensors of healthy fruit, while W1S, W2S, W1W and W5S were the characteristic sensors of severely diseased fruit. The model of MLPNN had the best discrimination effect, and the overall prediction accuracy for the modeling and validation was 88.61% and 88.46%, respectively. The model of RBFNN was secondary with the overall prediction accuracy for the modeling and validation was 93.50% and 80.95%, respectively. While, the model of fisher discriminant function had the worst discrimination effect, and the overall prediction accuracy for the modeling and validation was 91.50% and 79.27%, respectively. Even though the three discriminant models had good discriminant effect on healthy and severe fruit, and the discriminant effect on healthy and mild fruit was not ideal, which needs to be further optimized in future.

Key words： core rot apples; nondestructive testing model; electronic nose; chemometrics

参考文献

[1] 陈学森, 毛志泉, 沈向, 等.我国苹果产业节本增效关键技术Ⅰ:易着色功能型苹果新品种培育及无袋化栽培技术[J].中国果树, 2017(1):1-5.
CHEN X S, MAO Z Q, SHEN X, et al.The breeding of new variety of easy colouring functional apple and non-bag cultivation technology[J].China Fruits, 2017(1):1-5.
[2] 李瑾, 仇焕广, 蔡亚庆, 等.中国苹果产品出口现状、制约因素及其对策分析[J].世界农业, 2012(5):73-78.
LI J, CHOU H G, CAI Y Q, et al.Analysis on the current situation, restricting factors and countermeasures of China's apple products export[J].World Agriculture, 2012(5):73-78.
[3] 张党利. 陕西苹果产业的现状、问题与发展对策[J].广东农业科学, 2010, 37(6):371-373.
ZHANG D L.Current situation, problems and development countermeasures of apple industry in Shaanxi province[J].Guangdong Agricultural Sciences, 2010, 37(6):371-373.
[4] AG D, JONES A L, ALDWINCKLE H S.Compendium of apple and pear diseases[J].Mycologia, 1990, 82(6):802.
[5] NTASIOU P, MYRESIOTIS C, KONSTANTIONOU S, et al.Identification, characterization and mycotoxigenic ability of Alternaria spp.causing core rot of apple fruit in Greece[J].International Journal of Food Microbiology, 2015,197:22-29.
[6] SHTIENBERG D.Effects of host physiology on the development ofcore rot, caused by Alternaria alternata, in red delicious apples[J].Phytopathology, 2012, 102(8):769-778.
[7] CLARK C J, MCGLONE V A, JORDAN R B.Detection of brownheart in 'Braeburn' apple by transmission NIR spectroscopy[J].Postharvest Biology and Technology, 2003, 28(1):87-96.
[8] SHENDERY C, SHMULEVICH I, ALCHANATIS V.NIRS detection of moldy core in apples[J].Food and Bioprocess Technology, 2010, 3(1):79-86.
[9] 张建超, 张鹏, 田世平, 等. 苹果霉心病无损检测研究进展[J]. 包装工程, 2020, 41(1): 23-30.
ZHANG J C, ZHANG P, TIAN S P, et al. Research progress in nondestructive detection technology of apple core rot[J]. Packaging Engineering, 2020, 41(1): 23-30.
[10] GONZALEZ J J, VALLE R C, BOBROFF S.Detection and monitoring of internal browning developmenting Fuji apples using MRI[J].Postharvest Biology and Technology, 2001, (22):179-183.
[11] CHAYAPRASERT W, STROSHINE R.Rapid sensing of internal browning in whole apples using a low-cost, low-field proton magnetic resonance sensor[J].Postharvest Biology and Technology, 2005, 36(3):291-301.
[12] 李芳, 蔡骋, 马惠玲, 等.基于生物阻抗特性分析的苹果霉心病无损检测[J].食品科学, 2013, 34(18):197-202.
LI F, CAI C, MA H L, et al.Nondestructive detection of apple mouldy core based on bioimpedance properties[J].Food Science, 2013, 34(18):197-202.
[13] 张立彬, 胥芳, 贾灿纯, 等.苹果内部品质的电特性无损检测研究[J].农业工程学报, 2000, 16(3):104-106.
ZHANG L B, XU F, JIA C C, et al.Nondestructive measurement of internal quality of apples by dielectric properties[J].Transactions of the Chinese Society of Agricultural Engineering, 2000, 16(3):104-106.
[14] 杨亮亮. 基于机器视觉和X射线的苹果霉心病检测方法研究[D].杨凌:西北农林科技大学, 2009.
YANG L L.Research on detection method of apple mould heart disease based on machine vision and X-ray[D].Yangling:NorthWest Agriculture and Forestry University, 2009.
[15] 黄滔滔, 孙腾, 张京平.基于CT图像的苹果内部品质无损检测[J].浙江大学学报(农业与生命科学版), 2013, 39(1):92-97.
HUANG T T, SUN T, ZHANG J P.Non-destructive detection of internal quality of apple based on CT image[J].Journal of Zhejiang University (Agriculture & Life Sciences), 2013, 39(1):92-97.
[16] 张卫园. 基于密度特征的苹果霉心病无损检测方法研究[D].杨凌:西北农林科技大学, 2015.
ZHANG W Y.Reseach on nondenstrucyive of apple mould core disease based on density character[D].Yangling:NorthWest Agriculture and Forestry University, 2015.
[17] 王富春, 李军, 张润浩.基于计算机视觉的苹果霉心病病变程度测量方法[J].农机化研究, 2015 (6):189-193.
WANG F C, LI J, ZHANG R H.Measurement of the degree of apple mould core disease based on computer vision[J].Journal of Agricultural Mechanization Research, 2015 (6):189-193.
[18] 张鹏, 李江阔, 陈绍慧.基于电子鼻判别富士苹果货架期的研究[J].食品工业科技, 2015, 36(5):272-276.
ZHANG P, LI J K, CHEN S H.Discrimination of Fuji apples shelf life by electronic nose[J].Science and Technology of Food Industry, 2015, 36(5):272-276.
[19] 宋亮. 基于电子鼻和电子舌的黑果枸杞风味检测[D].天津:天津商业大学, 2018.
SONG L.Flavor detection of Lycium ruthenicum based on electronic nose and electronic tongue[D].Tianjin:Tianjin University of Commerce, 2018.
[20] 颜廷才, 邵丹, 李江阔, 等.基于电子鼻和GC-MS评价不同品种葡萄采后品质和挥发性物质的变化[J].现代食品科技, 2015, 31(11):290-297.
YAN T C, SHAN D, LI J K, et al.Changes in quality and volatile substances of different varieties of grapes as evaluated by electronic nose combined with GC-MS technology[J].Modern Food Science and Technology, 2015, 31(11):290-297.
[21] 赵策, 马飒飒, 张磊, 等.基于电子鼻技术的皇冠梨腐败等级分类研究[J].食品工业科技, 2020, 41(3):252-256;264.
ZHAO C, MA S S, ZHANG L, et al.Research on classification of rotten grades of Huangguan pears on electronih nose technology[J].Science and Technology of Food Industry, 2020, 41(3):252-256;264.
[22] 胡桂仙, ANTIHUS H G, 王俊, 等.电子鼻无损检测柑橘成熟度的实验研究[J].食品与发酵工业, 2005, 31(8):57-61.
HU J X, ANTIHUS H G, WANG J, et al.A Research on monitoring the orange maturity with the electronic nose[J].Food and Fermentation Industries, 2005, 31(8):57-61.
[23] 张鑫, 齐玉洁, 杨夏, 等.利用电子鼻技术评价桃果实成熟度的研究[J].华南农业大学学报, 2012, 33(1):23-27.
ZHANG X, QI Y J, YANG X, et al.Evaluation of maturity of peach by electronic nose[J].华南农业大学学报, 2012, 33(1):23-27.
[24] 陈辰, 鲁晓翔, 张鹏, 等.基于电子鼻技术的玫瑰香葡萄贮藏期快速判别[J].食品与机械, 2015, 31(6):137-141.
CHEN C, LU X X, ZHANG P, et al.Quick discrimination of storage periods for muscat grape based on electronic nose[J].Food and Machinery, 2015, 31(6):137-141.
[25] 朱娜, 毛淑波, 潘磊庆.电子鼻对草莓采后贮藏早期霉菌感染的检测[J].农业工程学报, 2013, 29(5):266-273.
ZHU N, MAO S B, PAN L Q.Early detection of fungal disease infection in strawberry fruits by E-nose during postharvest storage [J].Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(5):266-273.
[26] HERNANDEZ G A, WANG J, HU G X.Discrimination of storage shelf-life for mandarin by electronic nose technique[J].LWT - Food Science and Technology, 2007, 40(4):681-689.
[27] JIANG W, ZHU Z, MCGINLEY J N, et al.Identification of a molecular signature underlying inhibition of mammary carcinoma growth by dietary n-3 fatty acids[J].Cancer Research, 2012, 72(15):3 795-3 806.
[28] BAO J L, DING R B, JIA X J, et al.Fast identification of anticancer constituents in forsythiae fructus based on metabolomics approaches[J].Journal of Pharmaceutical and Biomedical Analysis, 2018, (154):312-320.
[29] WIKLUND S, JOHANSSON E, SJÖSTRÖM M L, et al.Visualization of gc/tof-ms-based metabolomics data for identification of biochemically interesting compounds using oPLSR class models[J].Analytcal Chemistry, 2008, 80(1):115-122.
[30] KHALIL M N A, FEKRY M I, FARAG M A.Metabolome based volatiles profiling in 13 date palm fruit varieties from Egypt via, SPME GC-MS and chemometrics[J].Food Chemistry, 2017, 217:171-181.
[31] HUANG X C, SU S L, DUAN J N, et al.Effects and mechanisms of shaofu-zhuyu decoction and its major bioactive component for cold-stagnation and blood-stasis primary dysmenorrhea rats[J].Journal of Ethnopharmacology, 2016, 186:234-243.
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