{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T20:40:31Z","timestamp":1760647231478,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T00:00:00Z","timestamp":1588118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper deals with the classification of stenches, which can stimulate olfactory organs to discomfort people and pollute the environment. In China, the triangle odor bag method, which only depends on the state of the panelist, is widely used in determining odor concentration. In this paper, we propose a stenches detection system composed of an electronic nose and machine learning algorithms to discriminate five typical stenches. These five chemicals producing stenches are 2-phenylethyl alcohol, isovaleric acid, methylcyclopentanone, \u03b3-undecalactone, and 2-methylindole. We will use random forest, support vector machines, backpropagation neural network, principal components analysis (PCA), and linear discriminant analysis (LDA) in this paper. The result shows that LDA (support vector machine (SVM)) has better performance in detecting the stenches considered in this paper.<\/jats:p>","DOI":"10.3390\/s20092514","type":"journal-article","created":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T13:23:45Z","timestamp":1588166625000},"page":"2514","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Five Typical Stenches Detection Using an Electronic Nose"],"prefix":"10.3390","volume":"20","author":[{"given":"Wei","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daqi","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1016\/S0140-6736(02)11274-8","article-title":"Air pollution and health","volume":"360","author":"Brunekreef","year":"2002","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.2166\/wst.2009.112","article-title":"Difference in the odor concentrations measured by the triangle odor bag method and dynamic olfactometry","volume":"59","author":"Ueno","year":"2009","journal-title":"Water Sci. Technol."},{"key":"ref_3","unstructured":"GBT (2020, April 09). 14675-1993 Air Quality-Determination of Odor-Triangle Odor Bag Method. Available online: https:\/\/www.codeofchina.com\/standard\/GBT14675-1993.html."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1021\/cr068121q","article-title":"Electronic nose: Current status and future trends","volume":"108","author":"Rock","year":"2008","journal-title":"Chem. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, J., Gu, J.H., Zhang, R., Mao, Y.Z., and Tian, S.Y. (2019). Freshness evaluation of three kinds of meats based on the electronic nose. Sensors, 19.","DOI":"10.3390\/s19030605"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.measurement.2019.06.005","article-title":"Freshness assessment of broccoli using electronic nose","volume":"145","author":"Ezhilan","year":"2019","journal-title":"Measurement"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2226","DOI":"10.1007\/s12161-019-01552-1","article-title":"Advances in electronic nose development for application to agricultural products","volume":"12","author":"Jia","year":"2019","journal-title":"Food Anal. Methods"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2916","DOI":"10.1007\/s12161-018-1283-1","article-title":"Electronic noses as a powerful tool for assessing meat quality: A mini review","volume":"11","author":"Jia","year":"2018","journal-title":"Food Anal. Methods"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"127250","DOI":"10.1016\/j.snb.2019.127250","article-title":"Electronic nose combined with chemometric approaches to assess authenticity and adulteration of sausages by soy protein","volume":"303","author":"Kalinichenko","year":"2020","journal-title":"Sens. Actuators B Chem."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"111058","DOI":"10.1016\/j.postharvbio.2019.111058","article-title":"Fast tool based on electronic nose to predict olive fruit quality after harvest","volume":"160","author":"Gila","year":"2020","journal-title":"Postharvest Biol. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108879","DOI":"10.1016\/j.scienta.2019.108879","article-title":"Collaborative analysis on difference of apple fruits flavour using electronic nose and electronic tongue","volume":"260","author":"Zhu","year":"2020","journal-title":"Sci. Hortic."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.lwt.2019.03.074","article-title":"Wine quality rapid detection using a compact electronic nose system: Application focused on spoilage thresholds by acetic acid","volume":"108","author":"Gamboa","year":"2019","journal-title":"Lwt Food Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, H.X., Li, Q., Yan, B., Zhang, L., and Gu, Y. (2019). Bionic electronic nose based on MOS sensors array and machine learning algorithms used for wine properties detection. Sensors, 19.","DOI":"10.3390\/s19010045"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Voss, H.G.J., Mendes Junior, J.J.A., Farinelli, M.E., and Stevan, S.L. (2019). A prototype to detect the alcohol content of beers based on an electronic nose. Sensors, 19.","DOI":"10.3390\/s19112646"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.compag.2019.01.001","article-title":"Noise filtering framework for electronic nose signals: An application for beef quality monitoring","volume":"157","author":"Wijaya","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"127062","DOI":"10.1016\/j.snb.2019.127062","article-title":"Chemoresistive sensors for colorectal cancer preventive screening through fecal odor: Double-blind approach","volume":"301","author":"Zonta","year":"2019","journal-title":"Sens. Actuators B Chem."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jia, W.S., Liang, G., Tian, H., Sun, J., and Wan, C.H. (2019). Electronic nose-based technique for rapid detection and recognition of moldy apples. Sensors, 19.","DOI":"10.20944\/preprints201903.0008.v1"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8010","DOI":"10.1109\/JSEN.2016.2606163","article-title":"A new method combining KECA-LDA with ELM for classification of Chinese liquors using electronic nose","volume":"16","author":"Jia","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Men, H., Fu, S.L., Yang, J.L., Cheng, M.Q., Shi, Y., and Liu, J.J. (2018). Comparison of SVM, RF and ELM on an electronic nose for the intelligent evaluation of paraffin samples. Sensors, 18.","DOI":"10.3390\/s18010285"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6426","DOI":"10.1021\/jf501468b","article-title":"Discrimination and characterization of strawberry juice based on electronic nose and tongue: Comparison of different juice processing approaches by LDA, PLSR, RF, and SVM","volume":"62","author":"Qiu","year":"2014","journal-title":"J. Agric. Food Chem."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"105103","DOI":"10.1088\/0957-0233\/23\/10\/105103","article-title":"Quantitative analysis of different volatile organic compounds using an improved electronic nose","volume":"23","author":"Gao","year":"2012","journal-title":"Meas. Sci. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1016\/j.snb.2011.11.003","article-title":"Quantitative analysis of multiple kinds of volatile organic compounds using hierarchical models with an electronic nose","volume":"161","author":"Gao","year":"2012","journal-title":"Sens. Actuators B Chem."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"085005","DOI":"10.1088\/0957-0233\/26\/8\/085005","article-title":"Quality-grade evaluation of petroleum waxes using an electronic nose with a TGS gas sensor array","volume":"26","author":"Wang","year":"2015","journal-title":"Meas. Sci. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.physa.2018.10.060","article-title":"SVM and KNN ensemble learning for traffic incident detection","volume":"517","author":"Xiao","year":"2019","journal-title":"Phys. A Stat. Mech. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2514\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:32:15Z","timestamp":1760362335000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2514"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,29]]},"references-count":24,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20092514"],"URL":"https:\/\/doi.org\/10.3390\/s20092514","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,4,29]]}}}