Sensor Array System Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural Network
The categorization of odors utilizing gas sensor arrays with various meatball borax concentrations has been studied. The samples included meatballs with a borax content of 0.05%, 0.10%, 0.15%, 0.20%, and 0.25% (%mm) and meatballs without any borax. Six TGS gas sensors with a baseline of 10 seconds,...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/8847929 |
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| author | Anak Agung Surya Pradhana Suryani Dyah Astuti null Fauziah Perwira Annissa Dyah Permatasari Riskia Agustina Ahmad Khalil Yaqubi Harsasi Setyawati null Winarno Cendra Devayana Putra |
| author_facet | Anak Agung Surya Pradhana Suryani Dyah Astuti null Fauziah Perwira Annissa Dyah Permatasari Riskia Agustina Ahmad Khalil Yaqubi Harsasi Setyawati null Winarno Cendra Devayana Putra |
| author_sort | Anak Agung Surya Pradhana |
| collection | DOAJ |
| description | The categorization of odors utilizing gas sensor arrays with various meatball borax concentrations has been studied. The samples included meatballs with a borax content of 0.05%, 0.10%, 0.15%, 0.20%, and 0.25% (%mm) and meatballs without any borax. Six TGS gas sensors with a baseline of 10 seconds, a detecting period of 120 seconds, and a purging period of 250 seconds make up the gas sensor array used in this work. Artificial neural networks (ANNs) and principal component analysis (PCA), which are beneficial for feature extraction and classification, are used to handle the collected data based on machine learning approaches. Two models were produced by the data analysis: model 1, which only used the PCA approach, and model 2, which only used the ANN methodology. 90.33% is the total variance value of PC from model 1. In addition, the multilayer perceptron artificial neural network (ANN-MLP) technique for model 2 yielded accuracy values of 95%. |
| format | Article |
| id | doaj-art-c72c0951a9b84365bf87c1947d1c7edf |
| institution | DOAJ |
| issn | 2090-0155 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-c72c0951a9b84365bf87c1947d1c7edf2025-08-20T03:05:14ZengWileyJournal of Electrical and Computer Engineering2090-01552023-01-01202310.1155/2023/8847929Sensor Array System Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural NetworkAnak Agung Surya Pradhana0Suryani Dyah Astuti1null Fauziah2Perwira Annissa Dyah Permatasari3Riskia Agustina4Ahmad Khalil Yaqubi5Harsasi Setyawati6null Winarno7Cendra Devayana Putra8Department of Computer SystemDepartment of PhysicsDepartment of PhysicsDepartment of MathematicsMagister of Forensic ScienceFaculty of Science and TechnologyDepartment of ChemistryDepartment of PhysicsInstitute of Information ManagementThe categorization of odors utilizing gas sensor arrays with various meatball borax concentrations has been studied. The samples included meatballs with a borax content of 0.05%, 0.10%, 0.15%, 0.20%, and 0.25% (%mm) and meatballs without any borax. Six TGS gas sensors with a baseline of 10 seconds, a detecting period of 120 seconds, and a purging period of 250 seconds make up the gas sensor array used in this work. Artificial neural networks (ANNs) and principal component analysis (PCA), which are beneficial for feature extraction and classification, are used to handle the collected data based on machine learning approaches. Two models were produced by the data analysis: model 1, which only used the PCA approach, and model 2, which only used the ANN methodology. 90.33% is the total variance value of PC from model 1. In addition, the multilayer perceptron artificial neural network (ANN-MLP) technique for model 2 yielded accuracy values of 95%.http://dx.doi.org/10.1155/2023/8847929 |
| spellingShingle | Anak Agung Surya Pradhana Suryani Dyah Astuti null Fauziah Perwira Annissa Dyah Permatasari Riskia Agustina Ahmad Khalil Yaqubi Harsasi Setyawati null Winarno Cendra Devayana Putra Sensor Array System Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural Network Journal of Electrical and Computer Engineering |
| title | Sensor Array System Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural Network |
| title_full | Sensor Array System Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural Network |
| title_fullStr | Sensor Array System Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural Network |
| title_full_unstemmed | Sensor Array System Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural Network |
| title_short | Sensor Array System Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural Network |
| title_sort | sensor array system based on electronic nose to detect borax in meatballs with artificial neural network |
| url | http://dx.doi.org/10.1155/2023/8847929 |
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