Classification of Different Iranian Rice Varieties and Frauded Rice Based on Volatile Compounds Detected by Electronic Nose Method

Document Type : Research Paper

Authors

1 M.Sc. Graduated, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran

2 Professor, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran

Abstract

Rice aroma is one of the important features of rice quality which affects its marketability. In this study, an electronic system consisting of six semiconductor metal oxide sensors was used as a non-destructive method for the separation of Iranian rice varieties and a frauded rice sample, which is a kind of common fraud in rice supply. Analysis of PCA with two main components covered 89% of the variance (variation) of the data for five original rice samples. Also, described 96% of the variance of data for four rice samples, which included two varieties of rice and two fraud samples using LDA method with the accuracy of 100%. The precision of the ANN method was obtained as 98.6% for separation of the two groups of Iranian varieties and the frauded samples.

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References

Champagne, E. T. (2008). Rice aroma and flavor: a literature review. Cereal Chemistry, 85(4), 445-454.
Choudhury, P. R., Kohli, S., Srinivasan, K., Mohapatra, T., & Sharma, R. P. (2001). Identification and classification of aromatic rices based on DNA fingerprinting. Euphytica, 118(3), 243-251.
Doleman, B. J., & Lewis, N. S. (2001). Comparison of odor detection thresholds and odor discriminability of a conducting polymer composite electronic nose versus mammalian olfaction. Sensors and Actuators B: Chemical, 72(1), 41-50.
Dorosh, P. A., & Wailes, E. J. (2010). The international rice trade: structure, conduct, and performance. S. Pandey, D, Byerlee, D, Dawe, A. Dobermann, S. Mohanty, S. Rozelle & B. Hardy, eds. Rice in the global economy: strategic research and policy issues for food security, 359-378.
Fenaille, F., Visani, P., Fumeaux, R., Milo, C., & Guy, P. A. (2003). Comparison of mass spectrometry-based electronic nose and solid phase micro extraction gas chromatography mass spectrometry technique to assess infant formula oxidation. Journal of agricultural and food chemistry, 51(9), 2790-2796.
Ghasemi-Varnamkhasti. M .2011. Design, development and implementation of a metallic oxidation semi-metallic oxide (MOS) bioelectronics machine, in order to reveal a change in the quality of light, in combination with patterns of pattern recognition analysis. Ph.D. Thesis. Department of Agricultural Machinery Engineering, Collage Agricultural and Natural Resources. University of Tehran, 187 pages.
Ghasemi-Varnamkhasti, M., Mohtasebi, S. S., Siadat, M., Razavi, S. H., Ahmadi, H., & Dicko, A. (2012). Discriminatory power assessment of the sensor array of an electronic nose system for the detection of non-alcoholic beer aging. Czech J. Food Science, 30(3), 236-240.
Haugen, J. E., & Kvaal, K. (1998). Electronic nose and artificial neural network. Meat Science, 49, S273-S286.
Hu, G., Wang, J., Wang, J. J., & Wang, X. L. (2011). Detection for rice odors and identification of varieties based on electronic nose technique. J. Zhejiang Univ.(Agric. Life Sci.), 6, 13.
Korel, F., & Balaban, M. Ö. (2008). Electronic nose technology in food analysis. Handbook of Food Analysis Instruments. doi, 10, 9781420045673.
Lau, K. T., McAlernon, P., & Slater, J. M. (2000). Discrimination of chemically similar organic vapours and vapour mixtures using the Kohonen networkPresented at SAC 99, Dublin, Ireland, July 25–30, 1999. Analyst, 125(1), 65-70.
Mannino, S., Benedetti, S., Buratti, S., & Cosio, M. (2006). The application of intelligent sensor array for air pollution control in the food industry. Odors in the Food Industry, 47-56.
Monsoor, M. A., & Proctor, A. (2004). Volatile component analysis of commercially milled head and broken rice. Journal of food science, 69(8).
Pearce, T. C., Schiffman, S. S., Nagle, H. T., & Gardner, J. W. (Eds.). (2006). Handbook of machine olfaction: electronic nose technology. John Wiley & Sons.
Pearce, T.C., Schiffman, S.S., Nagle, H.T. and Gardner, J.W. (2003), Handbook of machine olfaction: electronic nose technology. Wiley-VCH, Weinheim.
Perriere, G., & Thioulouse, J. (2003). Use of correspondence discriminant analysis to predict the subcellular location of bacterial proteins. Computer Methods and Programs in Biomedicine, 70(2), 99-105.
Strike, D. J., Meijerink, M. G. H., & Koudelka-Hep, M. (1999). Electronic noses–A mini-review. Fresenius' journal of analytical chemistry, 364(6), 499-505.
Timsorn, K., Lorjaroenphon, Y., & Wongchoosuk, C. (2017). Identification of adulteration in uncooked Jasmine rice by a portable low-cost artificial olfactory system. Measurement, 108, 67-76.
Tohidi, m. Ghasemi Varnamkhasti, M. Ghaffarinia.V., Mohtasebi, S.,S. Bonyadian , M. The development and development of an olfactory machine system in combination with pattern recognition methods for detecting formalin cheating on raw milk. Biotechnology Engineering of Iran, Volume 47, Issue 4, Winter 2016 (p. 770-761)
Wailes, E. J. (2005). Rice: Global trade, protectionist policies, and the impact of trade liberalization. M. Ataman Aksoy and John C. Beghin, 2(576), 177..
Yu, H., Wang, J., & Xu, Y. (2007). Identification of adulterated milk using electronic nose. Sensors and Materials, 19(5), 275-285.
Zheng, X. Z., Lan, Y. B., Zhu, J. M., Westbrook, J., Hoffmann, W. C., & Lacey, R. E. (2009). Rapid identification of rice samples using an electronic nose. Journal of Bionic Engineering, 6(3), 290-297.
Iranian Journal of Biosystems Engineering
Volume 50, Issue 3
November 2019
Pages 595-606

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History

  • Receive Date: 16 August 2018
  • Revise Date: 01 April 2019
  • Accept Date: 28 April 2019

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