Rapid Identification of Rice Samples Using an Electronic Nose

doi:10.1016/S1672-6529(08)60122-5

J4 ›› 2009, Vol. 6 ›› Issue (3): 290-297.doi: 10.1016/S1672-6529(08)60122-5

Rapid Identification of Rice Samples Using an Electronic Nose

Xian-zhe Zheng¹, Yu-bin Lan², Jian-min Zhu³, John Westbrook², W. C. Hoffmann², R. E. Lacey⁴

1. College of Engineering, Northeast Agricultural University, Harbin 150030, P. R. China
2. USDA-ARS, College Station, TX 77845, USA
3. Department of Mathematics and Computer Science, Fort Valley State University, GA 31030, USA
4. Department of Biological and Agricultural Engineering, Texas A&M University, College Station, Texas 77843-2117, USA

出版日期:2009-09-30
通讯作者: Yu-bin Lan E-mail: Yubin.lan@ars.usda.gov E-mail:Yubin.lan@ars.usda.gov

Rapid Identification of Rice Samples Using an Electronic Nose

Xian-zhe Zheng¹, Yu-bin Lan², Jian-min Zhu³, John Westbrook², W. C. Hoffmann², R. E. Lacey⁴

1. College of Engineering, Northeast Agricultural University, Harbin 150030, P. R. China
2. USDA-ARS, College Station, TX 77845, USA
3. Department of Mathematics and Computer Science, Fort Valley State University, GA 31030, USA
4. Department of Biological and Agricultural Engineering, Texas A&M University, College Station, Texas 77843-2117, USA

Online:2009-09-30
Contact: Yu-bin Lan E-mail: Yubin.lan@ars.usda.gov E-mail:Yubin.lan@ars.usda.gov

摘要/Abstract

摘要：

Four rice samples of long grain type were tested using an electronic nose (Cyranose-320). Samples of 5 g of each variety of rice were placed individually in vials and were analyzed with the electronic nose unit consisting of 32 polymer sensors. The Cyranose-320 was able to differentiate between varieties of rice. The chemical composition of the rice odors for differentiating rice samples needs to be investigated. The optimum parameter settings should be considered during the Cyranose-320 training process especially for multiple samples, which are helpful for obtaining an accurate training model to improve identification capability. Further, it is necessary to investigate the E-nose sensor selection for obtaining better classification accuracy. A re-duced number of sensors could potentially shorten the data processing time, and could be used to establish an application pro-cedure and reduce the cost for a specific electronic nose. Further research is needed for developing analytical procedures that adapt the Cyranose-320 as a tool for testing rice quality.

关键词: rice grain, identification, electronic nose, data analysis, pattern recognition

Abstract:

Key words: rice grain, identification, electronic nose, data analysis, pattern recognition

Xian-zhe Zheng, Yu-bin Lan, Jian-min Zhu, John Westbrook, W. C. Hoffmann, R. E. Lacey. Rapid Identification of Rice Samples Using an Electronic Nose[J]. J4, 2009, 6(3): 290-297.

参考文献

[1] Monscoor M A, Proctorc A. Volatile component analysis of commercially milled head and broken rice. Journal of Food Science, 2004, 69, 632–636.

[2] Borjesson T, EklövT, Jonsson A, Sundgren H, Schnürer J. Electronic nose for odor classification of grains. Cereal Chem, 1996, 73, 457–461.

[3] Saverio M, Simona B, Susanna B, Maria S C. The Application of Intelligent Sensor Array for Air Pollution Control in the Food Industry, Springer US, New York. 2006.

[4] Srujan K D. Electrochemically Controlled Patterning for Biosensor Arrays, PhD Dissertation, University at Rovira I Virgili, Tarragona, Spain, 2006.

[5] Haugen J-E, Kvaal K. Electronic nose and artificial neural network. Meat Science, 1998, 49, S273–S286.

[6] Stetter U, Aspelmeicr A, Baberschke K. The initial AC susceptibility of Gd/W(110) films in UHV: A new method to investigate magnetism in ultra thin films. Journal of Magnetism and Magnetic Materials, 1992, 117, 183–189.

[7] Rylander R. Dose-response relationships for traffic noise and annoyance. Archives of Environmental Health, 1986, 41, 7–10.

[8] Bush R, Portnoy J, Saxon A, Terr A, Wood R. The medical effects of mold exposure. Journal of Allergy and Clinical Immunology, 2006, 117, 326–333.

[9] Olsson J, Börjesson T, Lundstedt T, Schnürer J. Detection and quantification of ochratoxin A and deoxynivalenol in barley grains by GC-MS and electronic nose. International Journal of Food Microbiology, 2002, 72, 203–214.

[10] Ahlstrom U, Borjesson E. Segregation of motion structure from random visual noise. Perception, 1996, 25, 279–292.

[11] Patrycja C, Wojciech W. The analysis of sensor array data with various pattern recognition techniques. Sensors and Actuators B: Chemical, 2006, 114, 85–93.

[12] Schulbach K F, Rouseff R L, Sims C A. Relating descriptive sensory analysis to gas chromatography/olfactometry ratings of fresh strawberries using partial least squares regression. Journal of Food Science, 2004, 69, 273–277.

[13] Yoav S. Potential applications of artificial olfactory sensing for quality evaluation of fresh produce. Journal of Agricultural Engineering Research, 2000, 77, 239–258.

[14] Shi Z B, Yu T, Zhao Q, Li Y, Lan Y B. Comparison of algorithms for an electronic nose in identifying liquors. Journal of Bionic Engineering, 2008, 5, 253–257.

[15] Lan Y B, Zheng X Z, Wesbrook, J K, Lopez J, Lacey R, Hoffmann W C. Identification of stink bugs using an electronic nose. Journal of Bionic Engineering, 2008, 5, S172–S180.

Zhang Z, Tong J, Chen D H, Lan Y B. Electronic nose with an air sensor matrix for detecting beef freshness. Journal of Bionic Engineering, 2008, 5, 67–73.

Rapid Identification of Rice Samples Using an Electronic Nose

Rapid Identification of Rice Samples Using an Electronic Nose

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

Metrics

本文评价

推荐阅读 0

[1]	Zhiyong Chang1,2,3, Youhong Sun3,4*, Yuchen Zhang1,2 et al.. Bionic Optimization Design of Electronic Nose Chamber for Oil and Gas Detection[J]. 仿生工程学报, 2018, 15(3): 533-544.
[2]	Nianfeng Wang, Kunyi Lao, Xianmin Zhang. Design and Myoelectric Control of an Anthropomorphic Prosthetic Hand[J]. 仿生工程学报, 2017, 14(1): 47-59.
[3]	Qi Bian, Kairui Zhao, Xinmin Wang, Rong Xie. System Identification Method for Small Unmanned Helicopter Based on Improved Particle Swarm Optimization[J]. J4, 2016, 13(3): 504-514.
[4]	Ricardo Lo′ pez1, Hipo′ lito Aguilar2, Sergio Salazar1\| Rogelio Lozano2. Adaptive Control for Passive Kinesiotherapy ELLTIO[J]. J4, 2014, 11(4): 581-588.
[5]	Xusheng Lei, Kexin Guo. The Model Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Ant Colony Algorithm[J]. J4, 2012, 9(4): 508-514.
[6]	Charles P.-C. Suh??Ningye Ding??Yubin Lan?. Using an Electronic Nose to Rapidly Assess Grandlure Content in Boll Weevil Pheromone Lures[J]. J4, 2011, 8(4): 449-454.
[7]	Xusheng Lei, Yuhu Du. A Linear Domain System Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Genetic Algorithm[J]. J4, 2010, 7(2): 142-149.
[8]	Gigun Lee, Kwanwoo You, Taesam Kang, Kwang Joon Yoon, Jeong Oog Lee, Jung Keun Park. Modeling and Design of H-Infinity Controller for Piezoelectric Actuator LIPCA[J]. J4, 2010, 7(2): 168-174.