Quartz crystal microbalance based electronic nose system implemented on Field Programmable Gate Array (original) (raw)

Abstract

Nowadays, an electronic nose becomes an important tool for detecting gas. The electronic nose consists of gas sensor array combined with neural networks to recognize patterns of the sensor array. Currently, the implementation of the neural network on the electronic nose systems still use personal computer so that less flexible or not portable. This paper discusses the electronic nose system implemented in a Field Programmable Gate Array (FPGA). The sensor array consists of eight Quartz Crystal Microbalance (QCM) coated with chemical materials. The eight channel-frequency counter is used to measure the frequency change of the sensor due to the presence of gas adsorbed to the surfaces. The bipolar sigmoid activation function used in the neuron model is approximated by a second order equation. The experimental result showed that the electronic nose system could recognize all the types of gas with 92% success rate.

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References (16)

  1. Radi, Rivai M, Purnomo MH. Study on Electronic-Nose-Based Quality Monitoring System for Coffee Under Roasting. Journal of Circuits, Systems, and Computers. 2016; 25(10): 1650116. Available from:http://www.worldscientific.com/doi/abs/10.1142/S0218126616501164.
  2. Cellini A, Blasioli S, Biondi E, Bertaccini A, Braschi I, Spinelli F. Potential Applications and Limitations of Electronic Nose Devices for Plant Disease Diagnosis. Sensors. 2017; 17(11): 2596. Available from: http://www.mdpi.com/1424-8220/17/11/2596.
  3. Adak M, Yumusak N. Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network. Sensors. 2016; 16(3): 304. Available from: http://www.mdpi.com/1424-8220/16/3/304.
  4. He J, Xu L, Wang P, Wang Q. A high precise E-nose for daily indoor air quality monitoring in living environment. Integration. 2017; 58: 286-294. Available from: https://www.sciencedirect.com/science/article/pii/S0167926016301985.
  5. Xu M, Wang J, Gu S. Rapid identification of tea quality by E-nose and computer vision combining with a synergetic data fusion strategy. J Food Eng. 2019; 241: 10-17. Available from:https://www.sciencedirect.com/science/article/pii/S0260877418303091.
  6. Fitriyana F, Kurniawan F. Polyaniline-Invertase-Gold Nanoparticles Modified Gold Electrode for Sucrose Detection. Indones J Chem. 2015; 15(3): 226-233. Available from: http://pdm-mipa.ugm.ac.id/ojs/index.php/ijc/article/view/ 1000.
  7. Monroy JG, Gonzalez Jimenez J. Gas classification in motion: An experimental analysis. Sensors Actuators, B Chem. 2017; 240: 1205-1215. Available from: http:// dx.doi.org/10.1016/j.snb.2016.09.013.
  8. Sinha SK. Growth and ammonia sensing properties of Zn1-xSnxO nanofibers. Sensors Actuators B Chem. 2015; 219: 192-198. Available from: https://www.sciencedirect.com/ science/article/pii/S092540051500619X?via%3Dihub.
  9. Jazan E, Mirzaei H. Direct analysis of human breath ammonia using corona discharge ion mobility spectrometry. J Pharm Biomed Anal. 2014; 88: 315-320. Available from:https://www.sciencedirect.com/science/article/abs/pii/ S073170851300410X.
  10. AL Khalaf, FS Mohamad, et al. Room temperature ammonia sensor using side-polished optical fiber coated with graphene/polyaniline Nanocomposite. Optical Materials Express. 2017; 7(6): 1858-1870.
  11. Ibrahim SA, Rahman NA, Abu Bakar MH, Girei SH, Yaacob MH, Ahmad H, et al. Room temperature ammonia sensing using tapered multimode fiber coated with polyaniline nanofibers. Opt Express. 2015; 23(3): 2837. Available from: https:// www.osapublishing.org/abstract.cfm?URI=oe-23-3-2837.
  12. Bearzotti A, Macagnano A, Papa P, Venditti I, Zampetti E. A study of a QCM sensor based on pentacene for the detection of BTX vapors in air. Sensors Actuators, B Chem. 2017; 240: 1160-1164. Available from: http://dx.doi.org/10.1016/j.snb.2016.09.097
  13. Wang L, Wang Z, Xiang Q, Chen Y, Duan Z, Xu J. High performance formaldehyde detection based on a novel copper (II) complex functionalized QCM gas sensor. Sensors Actuators, B Chem. 2017; 248: 820-8. Available from: http://dx.doi.org/10.1016/j.snb.2016.12.015.
  14. Bernard Schwarz M, Zwick W, Wenzel L, Klier J, Gröschl M. Field programmable gate array-assigned complex-valued computation and its limits. Rev Sci Instrum. 2014; 85(9): 93104. Available from: http://aip.scitation.org/doi/10.1063/1.4894211
  15. Schwettmann A, Sedlacek J, Shaffer JP. Field-programmable gate array based locking circuit for external cavity diode laser frequency stabilization. Rev Sci Instrum. 2011; 82(10): 103103. Available from: http://aip.scitation.org/doi/10.1063/1.3646477.
  16. Jinghong L, Kai L, Peng Y. Design and implementation of flame combustion state detection system based on FPGA. In: The 27th Chinese Control and Decision Conference (2015 CCDC). IEEE; 2015: 1392-1396. Available from: http://ieeexplore.ieee.org/document/7162136/