Performance of electronic nose based on gas sensor-partition column for synthetic flavor classification
Electronic nose (e-nose) has been developed and implemented in a wide area, included in food industries. This study was conducted to investigate the performance of an e-nose that utilizes a packed gas chromatography column and a gas sensor for classification of synthetic flavor products. There were...
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Universitas Ahmad Dahlan
2022
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id-ugm-repo.2827532024-01-02T03:36:01Z https://repository.ugm.ac.id/282753/ Performance of electronic nose based on gas sensor-partition column for synthetic flavor classification Radi, Radi Putro, Joko Purwo Leksono Yuroto Adhityamurti, Muhammad Danu Barokah, Barokah Zamzami, Luthfi Fadillah Setiawan, Andi Sustainable Agricultural Development Food Engineering Food technology Electronic nose (e-nose) has been developed and implemented in a wide area, included in food industries. This study was conducted to investigate the performance of an e-nose that utilizes a packed gas chromatography column and a gas sensor for classification of synthetic flavor products. There were six aroma variants of synthetic flavor evaluated, namely durian, jackfruit, ambonese banana, melon, orange and lemon. The e-nose was designed with four main parts, namely aroma provider, column and detector room, microcontroller, and data acquisition system. The device was operated automatically at a stable temperature of 60 °C. Collected data consisted of ten data of each sample was preprocessed by baseline equalization and normalization, extracted its distinctive feature and then were analyzed through pattern recognition analysis. There were two kinds of methods used to analyzed the patterns of the data, namely a fuzzy c-means clustering and an artificial neural network (ANN). With the fuzzy c-means clustering, the result was six data clusters with an unbalanced number of members, indicated that this analysis could not classify samples properly. Meanwhile, analysis with the ANN could classify properly the samples with the level of accuracy of 70. © This is an open access article under the CC BY-SA license. Universitas Ahmad Dahlan 2022-10-05 Article PeerReviewed application/pdf en https://repository.ugm.ac.id/282753/1/22358-64122-1-PB.pdf Radi, Radi and Putro, Joko Purwo Leksono Yuroto and Adhityamurti, Muhammad Danu and Barokah, Barokah and Zamzami, Luthfi Fadillah and Setiawan, Andi (2022) Performance of electronic nose based on gas sensor-partition column for synthetic flavor classification. Telkomnika (Telecommunication Computing Electronics and Control), 20 (5). pp. 1146-1158. ISSN 16936930 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136522420&doi=10.12928%2fTELKOMNIKA.v20i5.22358&partnerID=40&md5=f795f45a592aba7e7b97d7147a811f8e 10.12928/TELKOMNIKA.v20i5.22358 |
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Sustainable Agricultural Development Food Engineering Food technology Radi, Radi Putro, Joko Purwo Leksono Yuroto Adhityamurti, Muhammad Danu Barokah, Barokah Zamzami, Luthfi Fadillah Setiawan, Andi Performance of electronic nose based on gas sensor-partition column for synthetic flavor classification |
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Electronic nose (e-nose) has been developed and implemented in a wide area, included in food industries. This study was conducted to investigate the performance of an e-nose that utilizes a packed gas chromatography column and a gas sensor for classification of synthetic flavor products. There were six aroma variants of synthetic flavor evaluated, namely durian, jackfruit, ambonese banana, melon, orange and lemon. The e-nose was designed with four main parts, namely aroma provider, column and detector room, microcontroller, and data acquisition system. The device was operated automatically at a stable temperature of 60 °C. Collected data consisted of ten data of each sample was preprocessed by baseline equalization and normalization, extracted its distinctive feature and then were analyzed through pattern recognition analysis. There were two kinds of methods used to analyzed the patterns of the data, namely a fuzzy c-means clustering and an artificial neural network (ANN). With the fuzzy c-means clustering, the result was six data clusters with an unbalanced number of members, indicated that this analysis could not classify samples properly. Meanwhile, analysis with the ANN could classify properly the samples with the level of accuracy of 70. © This is an open access article under the CC BY-SA license. |
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Article PeerReviewed |
author |
Radi, Radi Putro, Joko Purwo Leksono Yuroto Adhityamurti, Muhammad Danu Barokah, Barokah Zamzami, Luthfi Fadillah Setiawan, Andi |
author_facet |
Radi, Radi Putro, Joko Purwo Leksono Yuroto Adhityamurti, Muhammad Danu Barokah, Barokah Zamzami, Luthfi Fadillah Setiawan, Andi |
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Radi, Radi |
title |
Performance of electronic nose based on gas sensor-partition column for synthetic flavor classification |
title_short |
Performance of electronic nose based on gas sensor-partition column for synthetic flavor classification |
title_full |
Performance of electronic nose based on gas sensor-partition column for synthetic flavor classification |
title_fullStr |
Performance of electronic nose based on gas sensor-partition column for synthetic flavor classification |
title_full_unstemmed |
Performance of electronic nose based on gas sensor-partition column for synthetic flavor classification |
title_sort |
performance of electronic nose based on gas sensor-partition column for synthetic flavor classification |
publisher |
Universitas Ahmad Dahlan |
publishDate |
2022 |
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https://repository.ugm.ac.id/282753/1/22358-64122-1-PB.pdf https://repository.ugm.ac.id/282753/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136522420&doi=10.12928%2fTELKOMNIKA.v20i5.22358&partnerID=40&md5=f795f45a592aba7e7b97d7147a811f8e |
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