{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T10:48:21Z","timestamp":1776941301008,"version":"3.51.4"},"reference-count":56,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,7,9]],"date-time":"2019-07-09T00:00:00Z","timestamp":1562630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010447","name":"Ministry of Research, Technology and Higher Education of the Republic of Indonesia","doi-asserted-by":"publisher","award":["2688\/UN1.DITLIT\/DIT-LIT\/LT\/2019"],"award-info":[{"award-number":["2688\/UN1.DITLIT\/DIT-LIT\/LT\/2019"]}],"id":[{"id":"10.13039\/501100010447","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Chemosensors"],"abstract":"<jats:p>An electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas sensors, was used in situ for real-time classification of black tea according to its quality level. Principal component analysis (PCA) coupled with signal preprocessing techniques (i.e., time set value preprocessing, F1; area under curve preprocessing, F2; and maximum value preprocessing, F3), allowed grouping the samples from seven brands according to the quality level. The E-nose performance was further checked using multivariate supervised statistical methods, namely, the linear and quadratic discriminant analysis, support vector machine together with linear or radial kernels (SVM-linear and SVM-radial, respectively). For this purpose, the experimental dataset was split into two subsets, one used for model training and internal validation using a repeated K-fold cross-validation procedure (containing the samples collected during the first three days of tea production); and the other, for external validation purpose (i.e., test dataset, containing the samples collected during the 4th and 5th production days). The results pointed out that the E-nose-SVM-linear model together with the F3 signal preprocessing method was the most accurate, allowing 100% of correct predictive classifications (external-validation data subset) of the samples according to their quality levels. So, the E-nose-chemometric approach could be foreseen has a practical and feasible classification tool for assessing the black tea quality level, even when applied in-situ, at the harsh industrial environment, requiring a minimum and simple sample preparation. The proposed approach is a cost-effective and fast, green procedure that could be implemented in the near future by the tea industry.<\/jats:p>","DOI":"10.3390\/chemosensors7030029","type":"journal-article","created":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T03:05:26Z","timestamp":1562727926000},"page":"29","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["The Electronic Nose Coupled with Chemometric Tools for Discriminating the Quality of Black Tea Samples In Situ"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1602-9783","authenticated-orcid":false,"given":"Shidiq Nur","family":"Hidayat","sequence":"first","affiliation":[{"name":"Department of Physics, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1466-4364","authenticated-orcid":false,"given":"Kuwat","family":"Triyana","sequence":"additional","affiliation":[{"name":"Department of Physics, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia"},{"name":"Institute of Halal Industry and System (IHIS), Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281, Indonesia"}]},{"given":"Inggrit","family":"Fauzan","sequence":"additional","affiliation":[{"name":"Computer and Electronic Department, Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0265-1806","authenticated-orcid":false,"given":"Trisna","family":"Julian","sequence":"additional","affiliation":[{"name":"Department of Physics, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia"}]},{"given":"Danang","family":"Lelono","sequence":"additional","affiliation":[{"name":"Computer and Electronic Department, Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9104-9333","authenticated-orcid":false,"given":"Yusril","family":"Yusuf","sequence":"additional","affiliation":[{"name":"Department of Physics, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia"}]},{"given":"N.","family":"Ngadiman","sequence":"additional","affiliation":[{"name":"Agricultural Microbiology Department, Universitas Gadjah Mada, Bulaksumur, Yogyakarta 55281, Indonesia"},{"name":"PT. Pagilaran, Jl. Faridan M. Noto. 11 Kotabaru, Yogyakarta 55281, Indonesia"}]},{"given":"Ana C.A.","family":"Veloso","sequence":"additional","affiliation":[{"name":"Instituto Polit\u00e9cnico de Coimbra, ISEC, DEQB, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal"},{"name":"CEB\u2014Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6595-9165","authenticated-orcid":false,"given":"Ant\u00f3nio M.","family":"Peres","sequence":"additional","affiliation":[{"name":"Centro de Investiga\u00e7\u00e3o de Montanha (CIMO), ESA, Instituto Polit\u00e9cnico de Bragan\u00e7a, Campus Sta Apol\u00f3nia, 5300-253 Bragan\u00e7a, Portugal"},{"name":"Laboratory of Separation and Reaction Engineering\u2014Laboratory of Catalysis and Materials (LSRE-LCM), ESA, Instituto Polit\u00e9cnico de Bragan\u00e7a, Campus Santa Apol\u00f3nia, 5300-253 Bragan\u00e7a, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"352","DOI":"10.18517\/ijaseit.7.2.1659","article-title":"Development of Electronic Nose with High Stable Sample Heater to Classify Quality Levels of Local Black Tea","volume":"7","author":"Lelono","year":"2017","journal-title":"Int. J. Adv. Sci. Eng. Inf. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"020003","DOI":"10.1063\/1.4958468","article-title":"Classification of Indonesia black teas based on quality by using electronic nose and principal component analysis","volume":"Volume 1755","author":"Lelono","year":"2016","journal-title":"AIP Conference Proceedings"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhi, R., Zhao, L., and Zhang, D. (2017). A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment. Sensors, 17.","DOI":"10.3390\/s17051007"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.compag.2013.07.014","article-title":"Determination of dry matter content of tea by near and middle infrared spectroscopy coupled with wavelet-based data mining algorithms","volume":"98","author":"Li","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10942912.2017.1354021","article-title":"Colorimetric sensor array-based artificial olfactory system for sensing Chinese green tea\u2019s quality: A method of fabrication","volume":"20","author":"Li","year":"2017","journal-title":"Int. J. Food Prop."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.jfoodeng.2014.07.019","article-title":"Electronic noses for food quality: A review","volume":"144","author":"Loutfi","year":"2015","journal-title":"J. Food Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"S1107","DOI":"10.1080\/10942912.2017.1336719","article-title":"Identification of the aroma-active compounds in Longjing tea characterized by odor activity value, gas chromatography-olfactometry, and aroma recombination","volume":"20","author":"Gong","year":"2017","journal-title":"Int. J. Food Prop."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e13348","DOI":"10.1111\/jfpp.13348","article-title":"Monitoring black tea fermentation using a colorimetric sensor array-based artificial olfaction system","volume":"42","author":"Li","year":"2018","journal-title":"J. Food Process. Preserv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5960","DOI":"10.1021\/jf070601a","article-title":"Differentiation of Green, White, Black, Oolong, and Pu-erh Teas According to Their Free Amino Acids Content","volume":"55","author":"Ballesteros","year":"2007","journal-title":"J. Agric. Food Chem."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.compag.2017.11.007","article-title":"Discrimination among tea plants either with different invasive severities or different invasive times using MOS electronic nose combined with a new feature extraction method","volume":"143","author":"Sun","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","unstructured":"GB\/T 23776-2009 (2019, July 08). Methodology of Sensory Evaluation of Tea. Available online: https:\/\/webstore.ansi.org\/standards\/spc\/gb237762009."},{"key":"ref_12","unstructured":"(2019, July 08). Badan Standarisasi Nasional SNI: 1902:2016. Available online: https:\/\/www.scribd.com\/document\/322613336\/SNI-1902-2016."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.compag.2015.01.005","article-title":"Determination of tea polyphenols content by infrared spectroscopy coupled with iPLS and random frog techniques","volume":"112","author":"Li","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","first-page":"1","article-title":"A recurrent Elman network in conjunction with an electronic nose for fast prediction of optimum fermentation time of black tea","volume":"31","author":"Ghosh","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1007\/s11694-018-9855-8","article-title":"Dairy products discrimination according to the milk type using an electrochemical multisensor device coupled with chemometric tools","volume":"12","author":"Tazi","year":"2018","journal-title":"J. Food Meas. Charact."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9879","DOI":"10.1021\/acs.analchem.8b02036","article-title":"Highly-Selective Optoelectronic Nose Based on Surface Plasmon Resonance Imaging for Sensing Volatile Organic Compounds","volume":"90","author":"Brenet","year":"2018","journal-title":"Anal. Chem."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hidayat, S.N., and Triyana, K. (2016). Optimized back-propagation combined with radial basic neural network for improving performance of the electronic nose: Case study on the fermentation process of tempeh. AIP Conference Proceedings, AIP Publishing.","DOI":"10.1063\/1.4958466"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"50","DOI":"10.4028\/www.scientific.net\/AMM.771.50","article-title":"Development of Electronic Nose with Low-Cost Dynamic Headspace for Classifying Vegetable Oils and Animal Fats","volume":"771","author":"Triyana","year":"2015","journal-title":"Appl. Mech. Mater."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hidayat, S.N., Nuringtyas, T.R., and Triyana, K. (2018, January 7\u20138). Electronic Nose Coupled with Chemometrics for Monitoring of Tempeh Fermentation Process. Proceedings of the 4th International Conference on Science and Technology (ICST), Yogyakarta, Indonesia.","DOI":"10.1109\/ICSTC.2018.8528580"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Julian, T., Hidayat, S.N., and Triyana, K. (2018, January 7\u20138). Metal Oxide Semiconductor Based Electronic Nose as Classification and Prediction Instrument for Nicotine Concentration in Unflavoured Electronic Juice. Proceedings of the 4th International Conference on Science and Technology (ICST), Yogyakarta, Indonesia.","DOI":"10.1109\/ICSTC.2018.8528686"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Arshak, K., Moore, E., Lyons, G.M., Harris, J., and Clifford, S. (2004). A Review of Gas Sensors Employed in Electronic Nose Applications, Emerald Group Publishing.","DOI":"10.1108\/02602280410525977"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Biswas, P., Chatterjee, S., Kumar, N., Singh, M., Basu Majumder, A., and Bera, B. (2014). Integrated Determination of Tea Quality Based on Taster\u2019s Evaluation, Biochemical Characterization and Use of Electronics. Smart Sensors, Measurement and Instrumentation, Springer.","DOI":"10.1007\/978-3-319-02315-1_5"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Saha, P., Ghorai, S., Tudu, B., Bandyopadhyay, R., and Bhattacharyya, N. (2014). Multiclass Kernel Classifiers for Quality Estimation of Black Tea Using Electronic Nose. Smart Sensors, Measurement and Instrumentation, Springer.","DOI":"10.1007\/978-3-319-02315-1_7"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Saha, P., Ghorai, S., Tudu, B., Bandyopadhyay, R., and Bhattacharyya, N. (2014). Optimization of Sensor Array in Electronic Nose by Combinational Feature Selection Method. Smart Sensors, Measurement and Instrumentation, Springer.","DOI":"10.1007\/978-3-319-02315-1_9"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.jpba.2013.05.046","article-title":"Classification of tea category using a portable electronic nose based on an odor imaging sensor array","volume":"84","author":"Chen","year":"2013","journal-title":"J. Pharm. Biomed. Anal."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.snb.2011.07.009","article-title":"Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools","volume":"159","author":"Chen","year":"2011","journal-title":"Sensors Actuators B Chem."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.snb.2009.05.008","article-title":"Quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals","volume":"140","author":"Yu","year":"2009","journal-title":"Sens. Actuators B Chem."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.snb.2007.07.048","article-title":"Identification of green tea grade using different feature of response signal from E-nose sensors","volume":"128","author":"Yu","year":"2008","journal-title":"Sens. Actuators B Chem."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.snb.2006.05.019","article-title":"Discrimination of LongJing green-tea grade by electronic nose","volume":"122","author":"Yu","year":"2007","journal-title":"Sensors Actuators B Chem."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.chemolab.2015.03.010","article-title":"Longjing tea quality classification by fusion of features collected from E-nose","volume":"144","author":"Dai","year":"2015","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"012004","DOI":"10.1088\/1755-1315\/131\/1\/012004","article-title":"Detecting aroma changes of local flavored green tea (Camellia sinensis) using electronic nose","volume":"131","author":"Ralisnawati","year":"2018","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.jfoodeng.2011.12.037","article-title":"Instrumental testing of tea by combining the responses of electronic nose and tongue","volume":"110","author":"Tudu","year":"2012","journal-title":"J. Food Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.snb.2012.02.067","article-title":"Enhancing electronic nose performance: A novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze)","volume":"166\u2013167","author":"Kaur","year":"2012","journal-title":"Sens. Actuators B Chem."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.jfoodeng.2014.06.004","article-title":"Artificial flavor perception of black tea using fusion of electronic nose and tongue response: A Bayesian statistical approach","volume":"142","author":"Chattopadhyay","year":"2014","journal-title":"J. Food Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"12233","DOI":"10.3390\/s140712233","article-title":"A Bio-Inspired Herbal Tea Flavour Assessment Technique","volume":"14","author":"Zakaria","year":"2014","journal-title":"Sensors"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Banerjee, M.B., Roy, R.B., Tudu, B., Bandyopadhyay, R., and Bhattacharyya, N. (2017). Cross-Perception Fusion Model of Electronic Nose and Electronic Tongue for Black Tea Classification. Communications in Computer and Information Science, Springer.","DOI":"10.1007\/978-981-10-6427-2_33"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.jfoodeng.2018.07.020","article-title":"Rapid identification of tea quality by E-nose and computer vision combining with a synergetic data fusion strategy","volume":"241","author":"Xu","year":"2019","journal-title":"J. Food Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jfoodeng.2018.09.022","article-title":"Black tea classification employing feature fusion of E-Nose and E-Tongue responses","volume":"244","author":"Banerjee","year":"2019","journal-title":"J. Food Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4983","DOI":"10.1109\/TIE.2017.2772184","article-title":"A Novel Low-Cost Hand-Held Tea Flavor Estimation System","volume":"65","author":"Dutta","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Modak, A., Roy, R.B., Tudu, B., Bandyopadhyay, R., and Bhattacharyya, N. (2016, January 8\u201310). A novel fuzzy based signal analysis technique in electronic nose and electronic tongue for black tea quality analysis. Proceedings of the 2016 IEEE First International Conference on Control, Measurement and Instrumentation (CMI), Kolkata, India.","DOI":"10.1109\/CMI.2016.7413755"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.foodchem.2017.11.013","article-title":"Electronic noses in classification and quality control of edible oils: A review","volume":"246","author":"Majchrzak","year":"2018","journal-title":"Food Chem."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wojnowski, W., Majchrzak, T., Dymerski, T., G\u0119bicki, J., and Namie\u015bnik, J. (2017). Portable electronic nose based on electrochemical sensors for food quality assessment. Sensors, 17.","DOI":"10.3390\/s17122715"},{"key":"ref_43","unstructured":"Figaro Engineering Inc. (2014). TGS 2620 for the Detection of Solvent Vapors, Figaro Engineering Inc."},{"key":"ref_44","unstructured":"Figaro Engineering Inc. (2014). TGS 2612-for the Detection of Methane and LP Gas, Figaro Engineering Inc."},{"key":"ref_45","unstructured":"Figaro Engineering Inc. (2012). TGS 832-A00-for the Detection of Chlorofluorocarbons, Figaro Engineering Inc."},{"key":"ref_46","unstructured":"Figaro Engineering Inc. (2002). TGS 822-for the Detection of Organic Solvent Vapors, Figaro Engineering Inc."},{"key":"ref_47","unstructured":"Figaro Engineering Inc. (2014). TGS 2603-for Detection of Odor and Air Contaminants, Figaro Engineering Inc."},{"key":"ref_48","unstructured":"Figaro Engineering Inc. (2015). TGS 2600-for the Detection of Air Contaminants, Figaro Engineering Inc."},{"key":"ref_49","unstructured":"Figaro Engineering Inc. (2002). TGS 813-for the Detection of Combustible Gases, Figaro Engineering Inc."},{"key":"ref_50","unstructured":"Figaro Engineering Inc. (2014). TGS 826-Ammonia Sensor-MOX Sensor, Figaro Engineering Inc."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1049\/ip-cds:19990670","article-title":"Electronic noses: A review of signal processing techniques","volume":"146","author":"Hines","year":"1999","journal-title":"IEE Proc. Circuits Devices Syst."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"27804","DOI":"10.3390\/s151127804","article-title":"Electronic Nose Feature Extraction Methods: A Review","volume":"15","author":"Yan","year":"2015","journal-title":"Sensors"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_54","first-page":"1","article-title":"Others Caret package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_55","unstructured":"(2019, July 08). Package \u2018MASS\u2019. Available online: https:\/\/cran.r-project.org\/web\/packages\/MASS\/MASS.pdf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v011.i09","article-title":"Kernlab-an S4 package for kernel methods in R","volume":"11","author":"Karatzoglou","year":"2004","journal-title":"J. Stat. Softw."}],"container-title":["Chemosensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9040\/7\/3\/29\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:03:56Z","timestamp":1760187836000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9040\/7\/3\/29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,9]]},"references-count":56,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["chemosensors7030029"],"URL":"https:\/\/doi.org\/10.3390\/chemosensors7030029","relation":{},"ISSN":["2227-9040"],"issn-type":[{"value":"2227-9040","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,9]]}}}