{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T08:01:13Z","timestamp":1783929673626,"version":"3.55.0"},"reference-count":41,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,20]],"date-time":"2022-11-20T00:00:00Z","timestamp":1668902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61803230"],"award-info":[{"award-number":["61803230"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019KJN023"],"award-info":[{"award-number":["2019KJN023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022YK060"],"award-info":[{"award-number":["2022YK060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Projects of Shandong Province College Youth Innovation Technology Support Program","award":["61803230"],"award-info":[{"award-number":["61803230"]}]},{"name":"Projects of Shandong Province College Youth Innovation Technology Support Program","award":["2019KJN023"],"award-info":[{"award-number":["2019KJN023"]}]},{"name":"Projects of Shandong Province College Youth Innovation Technology Support Program","award":["2022YK060"],"award-info":[{"award-number":["2022YK060"]}]},{"name":"Graduate\u2019s Scientific Research Foundation of Shandong Jiaotong University","award":["61803230"],"award-info":[{"award-number":["61803230"]}]},{"name":"Graduate\u2019s Scientific Research Foundation of Shandong Jiaotong University","award":["2019KJN023"],"award-info":[{"award-number":["2019KJN023"]}]},{"name":"Graduate\u2019s Scientific Research Foundation of Shandong Jiaotong University","award":["2022YK060"],"award-info":[{"award-number":["2022YK060"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To solve the problem of the low recognition rate of mixed gases and consider the phenomenon of low prediction accuracy when traditional gas-concentration-prediction methods deal with nonlinear data, this paper proposes a mixed-gas identification and gas-concentration-prediction method based on a support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Principal component analysis (PCA) is applied to perform data dimensionality reduction on the input data, and SSA is adopted to optimize the SVM hyperparameters to improve the recognition rate and gas-concentration-prediction accuracy of mixed gases. For the mixed-gas identification, the classification accuracy is significantly improved under the proposed SSA optimization SVM method (SSA-SVM), compared with random forest (RF), extreme-learning machine (ELM), and BP neural network methods. With respect to gas-concentration prediction, the maximum fitting degrees reached 99.34% for single gas-concentration prediction and 97.55% for mixed-gas-concentration prediction. The experimental results show that the SSA-SVM method had a high recognition rate and high concentration-prediction accuracy in gas-mixture detection.<\/jats:p>","DOI":"10.3390\/s22228977","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T04:39:59Z","timestamp":1669005599000},"page":"8977","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A New Mixed-Gas-Detection Method Based on a Support Vector Machine Optimized by a Sparrow Search Algorithm"],"prefix":"10.3390","volume":"22","author":[{"given":"Haitao","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaozhen","family":"Han","sequence":"additional","affiliation":[{"name":"School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.tifs.2020.02.028","article-title":"Principles and recent advances in electronic nose for quality inspection of agricultural and food products","volume":"99","author":"Ali","year":"2020","journal-title":"Trends Food Sci. Technol."},{"key":"ref_2","first-page":"1","article-title":"SVM-based compliance discrepancies detection using remote sensing for organic farms","volume":"14","author":"Sharma","year":"2021","journal-title":"Arab. J. Geosci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Arroyo, P., Herrero, J.L., Su\u00e1rez, J.I., and Lozano, J. (2019). Wireless sensor network combined with cloud computing for air quality monitoring. Sensors, 19.","DOI":"10.3390\/s19030691"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"34","DOI":"10.36548\/jaicn.2021.1.003","article-title":"Artificial intelligence algorithm with SVM classification using dermascopic images for melanoma diagnosis","volume":"3","author":"Balasubramaniam","year":"2021","journal-title":"J. Artif. Intell. Capsul. Netw."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6065","DOI":"10.1007\/s10489-021-02761-0","article-title":"A temporal-based SVM approach for the detection and identification of pollutant gases in a gas mixture","volume":"52","author":"Djeziri","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.snb.2018.11.086","article-title":"Highly sensitive gas sensor based on Er-doped SnO2 nanostructures and its temperature dependent selectivity towards hydrogen and ethanol","volume":"282","author":"Singh","year":"2019","journal-title":"Sensors Actuators Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"128882","DOI":"10.1016\/j.snb.2020.128882","article-title":"Selective and sensitive detection of Cr (VI) pollution in waste water via polyaniline\/sulfated zirconium dioxide\/multi walled carbon nanotubes nanocomposite based electrochemical sensor","volume":"327","author":"Motaghedifard","year":"2021","journal-title":"Sensors Actuators B Chem."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.cclet.2017.06.021","article-title":"Ordered porous metal oxide semiconductors for gas sensing","volume":"29","author":"Zhou","year":"2018","journal-title":"Chin. Chem. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine learning: Algorithms, real-world applications and research directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Benos, L., Tagarakis, A.C., Dolias, G., Berruto, R., Kateris, D., and Bochtis, D. (2021). Machine learning in agriculture: A comprehensive updated review. Sensors, 21.","DOI":"10.3390\/s21113758"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"116452","DOI":"10.1016\/j.apenergy.2021.116452","article-title":"A review of machine learning in building load prediction","volume":"285","author":"Zhang","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Peng, P., Zhao, X., Pan, X., and Ye, W. (2018). Gas classification using deep convolutional neural networks. Sensors, 18.","DOI":"10.3390\/s18010157"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"128822","DOI":"10.1016\/j.snb.2020.128822","article-title":"A miniaturized electronic nose with artificial neural network for anti-interference detection of mixed indoor hazardous gases","volume":"326","author":"Zhang","year":"2021","journal-title":"Sensors Actuators B Chem."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wei, G., Zhao, J., Yu, Z., Feng, Y., Li, G., and Sun, X. (2018, January 28\u201331). An effective gas sensor array optimization method based on random forest. Proceedings of the 2018 IEEE SENSORS, New Delhi, India.","DOI":"10.1109\/ICSENS.2018.8589580"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xu, Y., Zhao, X., Chen, Y., and Zhao, W. (2018). Research on a mixed gas recognition and concentration detection algorithm based on a metal oxide semiconductor olfactory system sensor array. Sensors, 18.","DOI":"10.3390\/s18103264"},{"key":"ref_16","first-page":"341","article-title":"Basic tenets of classification algorithms K-nearest-neighbor, support vector machine, random forest and neural network: A review","volume":"8","author":"Boateng","year":"2020","journal-title":"J. Data Anal. Inf. Process."},{"key":"ref_17","first-page":"1","article-title":"Survey on SVM and their application in image classification","volume":"13","author":"Chandra","year":"2021","journal-title":"Int. J. Inf. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.measurement.2019.02.006","article-title":"Digital image recognition based on Fractional-order-PCA-SVM coupling algorithm","volume":"145","author":"Hu","year":"2019","journal-title":"Measurement"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1007\/s11633-015-0912-z","article-title":"A novel active learning method using SVM for text classification","volume":"15","author":"Goudjil","year":"2018","journal-title":"Int. J. Autom. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.measurement.2019.01.020","article-title":"Detection and identification of windmill bearing faults using a one-class support vector machine (SVM)","volume":"137","author":"Saari","year":"2019","journal-title":"Measurement"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1109\/JSEN.2017.2771226","article-title":"Fault detection in wireless sensor networks through SVM classifier","volume":"18","author":"Zidi","year":"2017","journal-title":"IEEE Sensors J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Laref, R., Losson, E., Sava, A., Adjallah, K., and Siadat, M. (2018, January 19\u201322). A comparison between SVM and PLS for E-nose based gas concentration monitoring. Proceedings of the 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, France.","DOI":"10.1109\/ICIT.2018.8352372"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6081","DOI":"10.1109\/JSEN.2016.2574460","article-title":"Detection of formaldehyde in mixed VOCs gases using sensor array with neural networks","volume":"16","author":"Zhao","year":"2016","journal-title":"IEEE Sensors J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"147519","DOI":"10.1149\/1945-7111\/abc83c","article-title":"Detection of Hazardous Gas Mixtures in the Smart Kitchen Using an Electronic Nose with Support Vector Machine","volume":"167","author":"Zhang","year":"2020","journal-title":"J. Electrochem. Soc."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.chemolab.2018.11.011","article-title":"On the optimization of the support vector machine regression hyperparameters setting for gas sensors array applications","volume":"184","author":"Laref","year":"2019","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.asoc.2018.11.001","article-title":"GA-SVM based feature selection and parameter optimization in hospitalization expense modeling","volume":"75","author":"Tao","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"124687","DOI":"10.1016\/j.jhydrol.2020.124687","article-title":"Quantitative contribution of climate change and human activities to vegetation cover variations based on GA-SVM model","volume":"584","author":"Huang","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3069","DOI":"10.1007\/s00366-021-01299-6","article-title":"An efficient approach for damage identification based on improved machine learning using PSO-SVM","volume":"38","author":"Khatir","year":"2022","journal-title":"Eng. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s10346-020-01426-2","article-title":"PSO-SVM-based deep displacement prediction of Majiagou landslide considering the deformation hysteresis effect","volume":"18","author":"Zhang","year":"2021","journal-title":"Landslides"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"123948","DOI":"10.1016\/j.jclepro.2020.123948","article-title":"Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization","volume":"277","author":"Pan","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1007\/s00170-019-03906-9","article-title":"Tool wear state recognition based on GWO\u2013SVM with feature selection of genetic algorithm","volume":"104","author":"Liao","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fan, S., Li, Z., Xia, K., and Hao, D. (2019). Quantitative and qualitative analysis of multicomponent gas using sensor array. Sensors, 19.","DOI":"10.3390\/s19183917"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"104439","DOI":"10.1016\/j.jlp.2021.104439","article-title":"Correction model for CO detection in the coal combustion loss process in mines based on GWO-SVM","volume":"71","author":"Deng","year":"2021","journal-title":"J. Loss Prev. Process Ind."},{"key":"ref_34","first-page":"1272","article-title":"New method for predicting coal seam gas content","volume":"41","author":"Li","year":"2019","journal-title":"Energy Sources Part A Recover. Util. Environ. Eff."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","article-title":"A novel swarm intelligence optimization approach: Sparrow search algorithm","volume":"8","author":"Xue","year":"2020","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_36","first-page":"012053","article-title":"Optimal configuration of distributed generation based on sparrow search algorithm","volume":"Volume 647","author":"Wang","year":"2021","journal-title":"Proceedings of the IOP Conference Series: Earth and Environmental Science"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-021-09879-x","article-title":"Comprehensive water quality evaluation based on kernel extreme learning machine optimized with the sparrow search algorithm in Luoyang River Basin, China","volume":"80","author":"Song","year":"2021","journal-title":"Environ. Earth Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, G., Shu, C., Liang, Z., Peng, B., and Cheng, L. (2021). A modified sparrow search algorithm with application in 3d route planning for UAV. Sensors, 21.","DOI":"10.3390\/s21041224"},{"key":"ref_39","first-page":"1","article-title":"Threshold image segmentation based on improved sparrow search algorithm","volume":"81","author":"Wu","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_40","first-page":"18","article-title":"Semi-supervised ensemble classifier with improved sparrow search algorithm and its application in pulmonary nodule detection","volume":"2021","author":"Zhang","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1016\/j.snb.2015.03.028","article-title":"Reservoir computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring","volume":"215","author":"Fonollosa","year":"2015","journal-title":"Sensors Actuators B Chem."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8977\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:22:15Z","timestamp":1760145735000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8977"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,20]]},"references-count":41,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22228977"],"URL":"https:\/\/doi.org\/10.3390\/s22228977","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,20]]}}}