{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T19:59:34Z","timestamp":1780775974177,"version":"3.54.1"},"reference-count":4,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T00:00:00Z","timestamp":1725321600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T00:00:00Z","timestamp":1725321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Badan Riset dan Inovasi Nasional (BRIN) and Lembaga Pengelola Dana Pendidikan (LPDP) Republik Indonesia for Program Riset dan Inovasi untuk Indonesia Maju"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Res Notes"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objectives<\/jats:title>\n                <jats:p>In recent years, there has been much discussion and research on electronic nose (e-nose). This topic has developed mainly in the medical and food fields. Typically, e-nose is combined with machine learning algorithms to predict or detect multiple sensory classes in each tea sample. Therefore, in e-nose systems, e-nose signal processing is an important part. In many situations, a comprehensive set of experiments is required to ensure the prediction model can be generalized well. This data set specifically focuses on two main goals such as classification of green tea quality and prediction of organoleptic score. In this experiment, Gambung dry green tea samples were used. The challenge is that dry tea does not emit as strong an aroma as tea infusions, making it more difficult for the e-nose system to detect and identify the aromas. This data set offers a valuable resource for researchers and developers to conduct investigations and experiments by classifying and detecting organoleptic scores that aim to categorize and identify organoleptic ratings. This enables a deeper understanding of the quality of dry green tea and encourages further integration of e-nose technology in the tea industry.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Data description<\/jats:title>\n                <jats:p>This experiment focused on analyzing green tea aroma using six gas sensors. Seventy-eight green tea samples were tested, each observed three times, using a tea chamber connected to a sensor chamber via a hose and an intake micro air pump. Air flowed from the tea chamber to the sensor chamber for 60\u00a0s, followed by 60\u00a0s of aroma data recording. This data was saved into CSV files and labeled according to the Indonesian National Standard (SNI) 3945:2016, which includes special and general requirements for green tea quality. An organoleptic test by a tea tester further labeled the data set into \u201cgood\u201d or \u201cquality defect\u201d for classification and provided organoleptic scores based on dry appearance, brew color, taste, aroma, and dregs of brewing for continuous label.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s13104-024-06905-6","type":"journal-article","created":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T12:02:20Z","timestamp":1725364940000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Data set for Gambung green tea aroma using on electronic nose"],"prefix":"10.1186","volume":"17","author":[{"given":"Dedy Rahman","family":"Wijaya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rini","family":"Handayani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Dzakyyuddin","family":"Badri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shabri","family":"Shabri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vitria Puspitasari","family":"Rahadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,3]]},"reference":[{"key":"6905_CR1","doi-asserted-by":"publisher","first-page":"1880","DOI":"10.1109\/JSEN.2023.3337264","volume":"24","author":"DR Wijaya","year":"2024","unstructured":"Wijaya DR, Handayani R, Fahrudin T, Kusuma GP, Afianti F. Electronic nose and Optimized Machine Learning Algorithms for Noninfused Aroma-based quality identification of Gambung Green Tea. IEEE Sens J. 2024;24:1880\u201393.","journal-title":"IEEE Sens J"},{"key":"6905_CR2","unstructured":"Badan S. Nasional. 2016."},{"key":"6905_CR3","unstructured":"Nabil A, Winarso M, Khais, Prayoga. Deskripsi dan Karakteristik Klon Teh Seri GMB. Deskripsi dan Karakteristik Klon Teh Seri GMB. 2021. https:\/\/iritc.org\/artikelilmiah\/karakteristik-klon-seri-gmb\/. Accessed 7 Aug 2024."},{"key":"6905_CR4","doi-asserted-by":"publisher","unstructured":"Wijaya DR. Data set for non-infused aroma-based quality identification of Gambung green tea using electronic nose. Harvard Dataverse. 2023. https:\/\/doi.org\/10.7910\/DVN\/BGIVM8. Accessed 26 Oct 2023.","DOI":"10.7910\/DVN\/BGIVM8"}],"container-title":["BMC Research Notes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13104-024-06905-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13104-024-06905-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13104-024-06905-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T12:08:52Z","timestamp":1725365332000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcresnotes.biomedcentral.com\/articles\/10.1186\/s13104-024-06905-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,3]]},"references-count":4,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["6905"],"URL":"https:\/\/doi.org\/10.1186\/s13104-024-06905-6","relation":{},"ISSN":["1756-0500"],"issn-type":[{"value":"1756-0500","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,3]]},"assertion":[{"value":"12 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"244"}}