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Odor Change of Citrus Juice During Storage Based on Electronic Nose Technology

  • Xue Jiang
  • Pengfei JiaEmail author
  • Siqi Qiao
  • Shukai Duan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

Abstract

In order to master the law of citrus juice odor components changes during the storing process, electronic nose composed of metal-oxide semiconductor (MOS) sensors array is used to monitor the odor during valencia oranges juice storing process. A self-made electronic nose system and experiment are described in detail, after data preprocessing, extreme learning machine (ELM) is used for analysis on samples. Analysis result indicates that the odor synthesized curve derived from the electronic nose technology can reflect overall trend of odor during valencia oranges juice storing process truly and effectively, and the experimental results prove that the E-nose can correctly distinguish the current stage of the stored valencia oranges juice and the classification accuracy of test data set is 96.29% when ELM is used as the classifier, which shows that the E-nose can be successfully applied to the qualitative analysis of citrus.

Keywords

Electronic nose Citrus juice Odor Extreme learning machine 

References

  1. 1.
    Hofsommer, H.J.: New technological aspects, pt. 5: quality aspects of concentrated orange juice. Honeybee Neurobiol. Behav. pp. 117–122 (1992)Google Scholar
  2. 2.
    Goodner, K.L., Jella, P., Rouseff, R.L.: Determination of vanillin in orange, grapefruit, tangerine, lemon, and lime juices using GC-olfactometry and GC-MS/MS. J. Agric. Food Chem. 48(7), 2882–2886 (2000)CrossRefGoogle Scholar
  3. 3.
    Loutfi, A., Coradeschi, S., Mani, G.K., et al.: Electronic noses for food quality: a review. J. Food Eng. 144, 103–111 (2015)CrossRefGoogle Scholar
  4. 4.
    Chapman, E.A., Thomas, P.S., Stone, E., Lewis, C., Yates, D.H.: A breath test for malignant mesothelioma using an electronic nose. Eur. Respir. J. 40(2), 448 (2012)CrossRefGoogle Scholar
  5. 5.
    Romain, A.C., Nicolas, J.: Long term stability of metal oxide-based gas sensors for E-nose environmental applications: an overview. Sens. Actuators B: Chem. 146(2), 502–506 (2010)CrossRefGoogle Scholar
  6. 6.
    Norman, A., Stam, F., Morrissey, A., et al.: Packaging effects of a novel explosion-proof gas sensor. Sens. Actuators B: Chem. 95(1), 287–290 (2003)CrossRefGoogle Scholar
  7. 7.
    Young, R.C., Buttner, W.J., Linnell, B.R., Ramesham, R.: Electronic nose for space program applications. Sens. Actuators B: Chem. 93, 7–16 (2003)CrossRefGoogle Scholar
  8. 8.
    Oshita, S., Shima, S., Haruta, T., et al.: Discrimination of odors emanating from ‘La France’ pear by semi-conducting polymer sensors. Comput. Electron. Agric. 26(2), 209–216 (2000)CrossRefGoogle Scholar
  9. 9.
    Natale, D., Macagnano, C.A., Martinelli, E., et al.: The evaluation of quality of post harvest orange and apples by means of an electronic nose. Sens. Actuators B: Chem. 78(1), 26–31 (2001)CrossRefGoogle Scholar
  10. 10.
    Moshonas, M.G., Shaw, P.E.: Quantitative determination of 46 volatile constituents in fresh unpasteurized orange juice using dynamic headspace gas chromatography. J. Agric. Food Chem. 42(7), 1525–1528 (1994)CrossRefGoogle Scholar
  11. 11.
    Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of 2004 IEEE International Joint Conference Neural Networks, vol. 2, pp. 985–990. Budapest, Hungary (2004)Google Scholar
  12. 12.
    Sun, Z.L., Choi, T.M., Au, K.F., et al.: Sales forecasting using extreme learning machine with applications in fashion retailing. Decis. Support Syst. 46(1), 411–419 (2009)CrossRefGoogle Scholar
  13. 13.
    Liang, N.Y., Saratchandran, P., Huang, G.B.: Classification of mental tasks from EEG signals using extreme learning machine. Int. J. Neural Syst. 16(1), 29–38 (2006)CrossRefGoogle Scholar
  14. 14.
    Mohammed, A.A., Minhas, R., Wu, Q.M.J., et al.: Human face recognition based on multidimensional PCA and extreme learning machine. Pattern Recogn. 44(10–11), 2588–2597 (2011)CrossRefzbMATHGoogle Scholar
  15. 15.
    Qiu, S., Gao, L., Wang, J.: Classification and regression of ELM, LVQ and SVM for E-nose data of strawberry juice. J. Food Eng. 144, 77–85 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Xue Jiang
    • 1
  • Pengfei Jia
    • 1
    Email author
  • Siqi Qiao
    • 1
  • Shukai Duan
    • 1
  1. 1.Southwest UniversityChongqingChina

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