Food and Fermentation Industries >

2021 , Vol. 47 >Issue 7: 211 - 216

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.025671

Nondestructive detection of apple moldy core based on FT-NIR and electronic nose technology

  • YANG Chenyu ,
  • YUAN Hongfei ,
  • MA Huiling ,
  • REN Yamei ,
  • REN Xiaolin
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  • 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)

Received date: 2020-09-15

  Revised date: 2020-10-24

  Online published: 2021-05-20

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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

Cite this article

YANG Chenyu , YUAN Hongfei , MA Huiling , REN Yamei , REN Xiaolin . Nondestructive detection of apple moldy core based on FT-NIR and electronic nose technology[J]. Food and Fermentation Industries, 2021 , 47(7) : 211 -216 . DOI: 10.13995/j.cnki.11-1802/ts.025671

References

[1] REUVENI M,PRUSKY D.Improved control of moldy-core decay (Alternaria alternata) in red delicious apple fruit by mixtures of DMI fungicides and captan[J].European Journal of Plant Pathology,2007,118(4):349-357.
[2] REUVENI M.Inhibition of germination and growth of Alternaria alternata and mouldy-core development in red delicious apple fruit by bromuconazole and sygnum[J].Crop Protection,2006,25(3):253-258.
[3] NIEM J,MIYARA I,ETTEDGUI Y,et al.Core rot development in red delicious apples is affected by susceptibility of the seed locule to Alternaria alternata colonization[J].Phytopathology,2007,97(11):1 415-1 421.
[4] 党继玲, 马志超,张荣,等.不同药剂对苹果霉心病和心腐病菌室内抑菌效果评价[J].西北农林科技大学学报(自然科学版),2015,43(1):147-151;168.
DANG J L,MA Z C,ZHANG R,et al.Evaluation of three fungicides against apple moldy core and core rot disease[J].Journal of Northwest A & F University (Natural Science Edition),2015,43(1):147-151;168.
[5] 白小东, 牛黎莉,毕阳,等.18种盐对苹果霉心病的控制及部分机理[J].食品工业科技,2015,36(10):335-338.
BAI X D,NIU L L,BI Y,et al.18 Kinds of salt on apple of core rot control and part of the mechanism[J].Science and Technology of Food Industry,2015,36(10):335-338.
[6] 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.
[7] 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.
[8] 王富春, 李军,张润浩,等.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,2015,37(6):189-193.
WANG F C,LI J,ZHANG R H,et al.Measurement of the degree of apple mould core disease based on computer vision[J].Journal of Agricultural Mechanization Research,2015,37(6):189-193.
[9] 刘思伽, 田有文,张芳,等.采用二次连续投影法和BP人工神经网络的寒富苹果病害高光谱图像无损检测[J].食品科学,2017,38(8):277-282.
LIU S J,TIAN Y W,ZHANG F,et al.Hyperspectral imaging for nondestructive detection of Hanfu apple diseases using successive projections algorithm and BP neural network[J].Food Science,2017,38(8):277-282.
[10] CLARK C J,MCGLONE V A,JORDAN R B.Detection of brown heart in ‘Braeburn’ apple by transmission NIR spectroscopy[J].Postharvest Biology and Technology,2003,28(1):87-96.
[11] MCGLONE V A,MARTINSEN P J,CLARK C J,et al.On-line detection of brown heart in Braeburn apples using near infrared transmission measurements[J].Postharvest Biology & Technology,2005,37(2):142-151.
[12] 韩东海, 刘新鑫,鲁超,等.苹果内部褐变的光学无损伤检测研究[J].农业机械学报,2006,37(6):86-88.
HAN D H,LIU X X,LU C,et al.Study on optical-nondestructive detection of breakdown apples[J].Transactions of the Chinese Society for Agricultural Machinery,2006,37(6):86-88.
[13] SHENDEREY C,SHMULEVICH I,ALCHANATIS V,et al.NIRS detection of moldy core in apples[J].Food and Bioprocess Technology,2010,3(1):79-86.
[14] VANOLI M,RIZZOLO A,GRASSI M,et al.Studies on classification models to discriminate ‘Braeburn’ apples affected by internal browning using the optical properties measured by time-resolved reflectance spectroscopy[J].Postharvest Biology and Technology,2014,91:112-121.
[15] 苏东, 张海辉,陈克涛,等.基于透射光谱的苹果霉心病多因子无损检测[J].食品科学,2016,37(8):207-211.
SU D,ZHANG H H,CHEN K T,et al.Multiple-factor nondestructive detection of moldy core in apples based on transmission spectra[J].Food Science,2016,37(8):207-211.
[16] 雷雨, 何东健,周兆永,等.苹果霉心病可见/近红外透射能量光谱识别方法[J].农业机械学报,2016,47(4):193-200.
LEI Y,HE D J,ZHOU Z Y,et al.Detection of moldy core of apples based on visible/near infrared transmission energy spectroscopy[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(4):193-200.
[17] 李顺峰, 张丽华,刘兴华,等.基于主成分分析的苹果霉心病近红外漫反射光谱判别[J].农业机械学报,2011,42(10):158-161.
LI S F,ZHANG L H,LIU X H,et al.Discriminant analysis of apple moldy core using near infrared diffuse reflectance spectroscopy based on principal component analysis[J].Transactions of the Chinese Society for Agricultural Machinery,2011,42(10):158-161.
[18] INFANTE R,RUBIO P,MENESES C,et al.Ripe nectarines segregated through sensory quality evaluation and electronic nose assessment[J].Fruits,2011,66(2):109-119.
[19] 宋小青, 任亚梅,张艳宜,等.电子鼻对低温贮藏猕猴桃品质的预测[J].食品科学,2014,35(20):230-235.
SONG X Q,REN Y M,ZHANG Y Y,et al.Prediction of kiwifruit quality during cold storage by electronic nose[J].Food Science,2014,35(20):230-235.
[20] ALEIXANDRE M,SANTOS J P,SAYAGO I,et al.A wireless and portable electronic nose to differentiate musts of different ripeness degree and grape varieties[J].Sensors,2015,15(4):8 429-8 443.
[21] 傅均, 黄灿钦,章铁飞.便携式智能电子鼻系统及其葡萄货架期评价研究[J].传感技术学报,2017,30(5):782-788.
FU J,HUANG C Q,ZHANG T F.A portable intelligent electronic nose system and its application in grape shelf life evaluation[J].Chinese Journal of Sensors and Actuators,2017,30(5):782-788.
[22] 徐赛, 陆华忠,周志艳,等.基于高光谱与电子鼻融合的番石榴机械损伤识别方法[J].农业机械学报,2015,46(7):214-219.
XU S,LU H Z,ZHOU Z Y,et al.Identification for guava mechanical damage based on combined hyper—spectrometer and electronic nose[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(7):214-219.
[23] SANAEIFAR A,MOHTASEBI S S,GHASEMI-VARNAMKHASTI M,et al.Application of MOS based electronic nose for the prediction of banana quality properties[J].Measurement,2016,82:105-114.
[24] 江琳琳, 潘磊庆,杨虹贤,等.电子鼻在果蔬品质检测中的研究进展[J].安徽农业科学,2010,38(23):12 918-12 920.
JIANG L L,PAN L Q,YANG H X,et al.Progress on the fruits and vegetables quality detection by electronic nose[J].Journal of Anhui Agricultural Sciences,2010,38(23):12 918-12 920.
[25] ADAR M F,YUMUSAK N.Classification of E-Nose aroma data of four fruit types by ABC-based neural network[J].Sensors,2016,16(3):1-13.
[26] PAN L,ZHANG W,ZHU N,et al.Early detection and classification of pathogenic fungal disease in post-harvest strawberry fruit by electronic nose and gas chromatography-mass spectrometry[J].Food Research International,2014,62(8):162-168.
[27] 惠国华, 厉鹏,吴玉玲,等.基于电子鼻系统的水果腐败过程表征方法[J].农业工程学报,2012,28(6):264-268.
HUI G H,LI P,WU Y L,et al.Characterization method of fruit decay procedure using electronic nose system[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(6):264-268.
[28] 李琦, 杨艳菊.基于人工神经网络的苹果气体识别方法研究[J].传感器与微系统,2007,26(9):61-63;66.
LI Q,YANG Y J.Research on method of apple gases recognition based on ANN[J].Transducer and Microsystem Technologies,2007,26(9):61-63;66.
[29] 张鹏, 刘振通,李江阔,等.不同气调元件对软枣猕猴桃冷藏期保鲜品质及电子鼻判别的影响[J].食品与发酵工业,2017,43(12):130-136.
ZHANG P,LIU Z T,LI J K,et al.Effects of different air combination on the preservation quality and electronic eose identification of kiwi Actinidia arguta during cold storage[J].Food and Fermentation Industries,2017,43(12):130-136.
[30] 何金鑫, 郜海燕,穆宏磊,等.山核桃氧化过程中品质指标变化的电子鼻快速检测[J].农业工程学报,2017,33(14):284-291.
HE J X,GAO H Y,MU H L,et al.Rapid detection of quality parameters change in hickory oxidation process by electronic nose[J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(14):284-291.
[31] 李光辉, 任亚梅,任小林,等.苹果品种及损伤苹果的FT-NIR鉴别研究[J].食品科学,2012,33(16):251-256.
LI G H,REN Y M,REN X L,et al.Discrimination and identification of bruised apples and apple varieties by FT-NIR[J].Food Science,2012,33(16):251-256.
[32] SAEVELS S,LAMMERTYN J,BERNA A Z,et al.Electronic nose as a non-destructive tool to evaluate the optimal harvest date of apples[J].Postharvest Biology and Technology,2003,30(1):3-14.
[33] 袁鸿飞, 胡馨木,杨军林,等.基于FT-NIR和电子鼻的苹果水心病无损检测[J].食品科学,2018,39(16):306-310.
YUAN H F,HU X M,YANG J L,et al.Nondestructive detection of apple watercore based on FT-NIR and electronic nose[J].Food Science,2018,39(16):306-310.
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