Classification and Identification of Volatile Organic Solvents based on Functional Groups using Electronic Nose
Keywords:
e-nose, MOS gas sensors, carbonyl compounds, esters, chlorinated compounds, alcohols, PCA, kNNAbstract
The Metal Oxide Semiconductor gas sensors based on SnO2 indicate cross sensitivity to many volatile organic compounds. Therefore, this study is focused on developing a methodology to distinguish organic solvents based on the functional groups present using an array of Metal Oxide Semiconductor gas sensors. Here, representative compounds for aliphatic, aromatic hydrocarbons, carbonyl groups, esters, alcohols and dichloromethane were used to evaluate gas sensors. Then data were analyzed using Principal Component Analysis and k-Nearest Neighbor methods. Finally, k-Nearest Neighbor best model was developed to predict the chemicals based on the sensor data. The overall results of this study sufficiently explain that the developed electronic nose system can distinguish the chemicals tested with Principal Component Analysis (96.6 percentage) and can predict with k-Nearest Neighbor (k=5) (90 percentage) the chemicals based on the sensor responses. These results demonstrate that the developed electronic nose can be used to classify and identify chemicals based in different functional groups.
References
. Hurot, Charlotte, Natale, S., Arnaud, B., and Hou,Y. Bio-Inspired Strategies for Improving the Selectivity and Sensitivity of Arti_cial Noses: A Review. In Sensors., 2020, 20(6),1803.
. Hu, Wenwen, Wan, L., Jian,Y., Ren,C., Jin,K., Su,X.,Bai,X., Haick,H., Yao,M., and Wu,W. Electronic noses: from advanced materials to sensors aided with data processing. In Advanced Materials Technologies ,2019, 4(2),1800488.
. Xu, Sai, Sun,X., Lu,H., and Zhang,Q. Detection of type, blended ratio, and mixed ratio of pu'er tea by using electronic nose and visible/near infrared spectrometer. In Sensors, 2019,19(10), 2359.
. Wu, Zhiyuan,Wang,H.,Wang,X., Zheng,H., Chen,Z., and Meng,C. Development of Electronic Nose for Qualitative and Quantitative Monitoring of Volatile Flammable Liquids. In Sensors, 2020, 20(7), 1817.
. Mahmoudi, Esmaeil. Electronic nose technology and its applications. In Sensors and Transducers,2009, 107(8), 17.
. Wilson, Alphus D. Diverse applications of electronic-nose technologies in agriculture and forestry. In Sensors,2013,13(2),2295-2348.
. Win,Tin,D. The electronic nose{a big part of our future. In AU JT,2005, 9(1), 1-8.
. Dutta, Ritaban, K. R. Kashwan, M. Bhuyan, Evor L. Hines, and J. W. Gardner. Electronic nose based tea quality standardization. In Neural Networks, 2003,16(5-6),847-853. https://doi.org/10.1016/S0893-6080(03)00092-3
. Zhou, B., Wang, J., and Qi, J. Identification of different wheat seeds by electronic nose. In International Agrophysics, 2012, 26(4), 413{418. https://doi.org/10.2478/v10247-012-0058-y
. Ghasemi-Varnamkhasti, M., Mohtasebi, S. S., Siadat, M., and Balasubramanian, S. Meat quality assessment by electronic nose (Machine Olfaction Technology). In Sensors., 2003, 9(8), 6058{6083. https://doi.org/10.3390/s90806058
. Xu, M., Wang, J., and Gu, S. Rapid identi_cation of tea quality by E-nose and computer vision combining with a synergetic data fusion strategy. In Journal of Food Engineering, 2019, 241, 10-17. https://doi.org/10.1016/j.jfoodeng.2018.07.020
. Wilson, Alphus, D., and Baietto, M. Advances in electronic-nose technologies developed for bio medical applications. In Sensors, 2011, 11(1), 1105{1176. https://doi.org/10.3390/s110101105
. Wilson, Alphus D., and Baietto, M. Advances in electronic-nose technologies developed for biomedical applications. In Sensors,2009, 9(7), 5099-5148.
. Pavlou, A., Turner,A.P.F., and Magan,N. Recognition of anaerobic bacterial isolates in vitro using electronic nose technology. In Letters in Applied Microbiology,2002, 35(5), 366-369.
. McWilliams, Annette,Beigi,P.,Srinidhi,A., Lam,S., and Calum E. MacAulay. Sex and smoking status effects on the early detection of early lung cancer in high-risk smokers using an electronic nose. In IEEE Transactions on Biomedical Engineering, 2015, 62(8),2044-2054.
. Guntner, Andreas T., Koren ,V., Chikkadi, K., Righettoni,M., and Sotiris E. Pratsinis.E-nose sensing of low-ppb formaldehyde in gas mixtures at high relative humidity for breath screening of lung cancer.In ACS Sensors, 2016, 1(5),528-535.
. Hockstein, Neil G., Erica R. Thaler, Torigian,D.Wallace T. M. Jr, De_enderfer,O. and C. William H.Diagnosis of pneumonia with an electronic nose: correlation of vapor signature with chest computed tomography scan _ndings. In The Laryngoscope ,2004,114 (10), 1701-1705.
. Pobkrut, Theerapat,Eamsa-Ard,T., and Kerdcharoen,T. Sensor drone for aerial odor mapping for agriculture and security services. In 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI- CON)IEEE,2016,1-5
. Wilson, Alphus D. Review of electronic-nose technologies and algorithms to detect hazardous chemicals in the environment.In Procedia Technology, 2012,453-463.
. De Vito, S., E. Massera, G. Burrasca, A. Di Girolamo, M. Miglietta, G. Di Francia, and D. Della Sala. TinyNose: Developing a wireless e-nose platform for distributed air quality monitoring applications. In SENSORS, 2008, IEEE,701-704.
. Ta_stan, Mehmet, and Gokozan, H. Real-time monitoring of indoor air quality with internet of things-based E-nose. In Applied Sciences,2019,9(16),3435.
. Fine, George, F., Leon, M. Cavanagh, Afonja, A.,and Binions,R. Metal oxide semi-conductor gas sensors in environmental monitoring. In sensors,2010, 10(6) ,5469-5502.
. Korotcenkov, Ghenadii. Handbook of gas sensor materials. In Conventional Approaches, 2013,1, Springer-Verlag.
. Wetchakun, K., T. Samerjai, N. Tamaekong, C. Liewhiran, C. Siriwong, V. Kruefu, A. Wisitsoraat, A. Tuantranont, and S. Phanichphant. Semiconducting metal oxides as sensors for environmentally hazardous gases.In Sensors and Actuators B: Chemical, 2011, 160(1),580- 591.
. Eranna, G., B. C. Joshi, D. P. Runthala, and R. P. Gupta. Oxide materials for development of integrated gas sensors|a comprehensive review.In Critical Reviews in Solid State and Materials Sciences,2004, 29(3-4), 111-188.
. [26] Korotcenkov, Ghenadii. Metal oxides for solid-state gas sensors: What determines our choice. In Materials Science and Engineering,2007,139(1), 1-23.
. Liu, Xiao, Cheng,S.,Liu,H., Hu,S., Zhang,D., and Ning,H. A survey on gas sensing technology. In Sensors,2012, 12(7),9635-9665.
. TECHNICAL DATA MQ-2,3,4 and 5 GAS SENSOR. available in https://www.pololu. com/file/0J309/MQ2.pdf, https://www.pololu.com/file/0J310/MQ3.pdf, https://www. pololu.com/file/0J311/MQ4.pdf, https://www.parallax.com/sites/default/files/downloads/ 605-00009-MQ-5-Datasheet.pdf
. Wu, Zhiyuan,Wang,H.,Wang,X.,Zheng,H.,Chen,Z., and Meng,C. Development of Electronic Nose for Qualitative and Quantitative Monitoring of Volatile Flammable Liquids. In Sensors, 2020,20(7), 1817
. Park, Yun,S.,Kim,Y., Kim,T., Eom,T.H., Kim,S.Y., and Jang,H.W., Chemoresistive materials for electronic nose: Progress, perspectives, and challenges. In InfoMat,2019, 1(3), 289-316.
. Wang, Chengxiang, Yin,L., Zhang,L., Xiang,D., and Gao,R. Metal oxide gas sensors: sensitivity and inuencing factors. In Sensors,2010,10(3), 2088-2106.
. Kanan, So_an, M., Oussama M. El-Kadri, Imad A. Abu-Yousef, and Marsha C. Kanan. Semiconducting metal oxide based sensors for selective gas pollutant detection. In Sensors,2009,9(10), 8158-8196.
. AWS. Amazon Machine Learning: Developer Guide. https://docs.aws.amazon.com/ machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html, 2020.
. S. M. Scott, D. James, and Z. Ali, “Data analysis for electronic nose systems,” Microchim. Acta, vol. 156, no. 3–4, pp. 183–207, 2006.
Downloads
Published
How to Cite
Issue
Section
License
Authors who submit papers with this journal agree to the following terms.
