Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization
The fisheries sector holds great importance in the Indonesian economy, particularly in terms of its contribution to growth and development. Both the fisheries and marine industries play significant roles in driving economic activities. The rising consumption of marine fishery products has resulted i...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10154464/ |
| _version_ | 1826938505478012928 |
|---|---|
| author | Dedy Rahman Wijaya Nailatul Fadhilah Syarwan Muhammad Agus Nugraha Dahliar Ananda Tora Fahrudin Rini Handayani |
| author_facet | Dedy Rahman Wijaya Nailatul Fadhilah Syarwan Muhammad Agus Nugraha Dahliar Ananda Tora Fahrudin Rini Handayani |
| author_sort | Dedy Rahman Wijaya |
| collection | DOAJ |
| description | The fisheries sector holds great importance in the Indonesian economy, particularly in terms of its contribution to growth and development. Both the fisheries and marine industries play significant roles in driving economic activities. The rising consumption of marine fishery products has resulted in a growing market demand for high-quality and safe products. Meeting this demand necessitates the maintenance of freshness in marine fishery products. Thus, this research aims to develop a fast, cheap, accurate method utilizing an electronic nose (e-nose) and machine learning algorithms as an alternative method for assessing the freshness quality of marine fishery products (seafood). This experiment employs seven algorithms with hyperparameter optimization to obtain the best performance. Machine learning algorithms are used for classification and regression tasks. The objective is to detect the freshness quality of marine fishery products accurately (classification task) while also identifying microbial populations present in the seafood (regression task). Through extensive investigations, the classification and regression models, specifically employing the k-Nearest Neighbors algorithm, demonstrated remarkable performance, achieving a very high accuracy score. Furthermore, the regression model yielded an RMSE value of 0.03 and an R<sup>2</sup> value of 0.995, indicating the effectiveness of the approach in assessing and quantifying the quality attributes of marine fishery products. |
| first_indexed | 2024-03-13T02:57:25Z |
| format | Article |
| id | doaj.art-3f067f5db4b44169b9a3ef315ed9b5c3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2025-02-17T18:58:01Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3f067f5db4b44169b9a3ef315ed9b5c32024-12-11T00:01:20ZengIEEEIEEE Access2169-35362023-01-0111624846249510.1109/ACCESS.2023.328698010154464Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter OptimizationDedy Rahman Wijaya0https://orcid.org/0000-0003-0351-7331Nailatul Fadhilah Syarwan1Muhammad Agus Nugraha2Dahliar Ananda3Tora Fahrudin4Rini Handayani5School of Applied Science, Telkom University, Bandung, IndonesiaSchool of Applied Science, Telkom University, Bandung, IndonesiaSchool of Applied Science, Telkom University, Bandung, IndonesiaDepartment of Software Engineering, Surabaya Telkom Institute of Technology, Surabaya, IndonesiaSchool of Applied Science, Telkom University, Bandung, IndonesiaSchool of Applied Science, Telkom University, Bandung, IndonesiaThe fisheries sector holds great importance in the Indonesian economy, particularly in terms of its contribution to growth and development. Both the fisheries and marine industries play significant roles in driving economic activities. The rising consumption of marine fishery products has resulted in a growing market demand for high-quality and safe products. Meeting this demand necessitates the maintenance of freshness in marine fishery products. Thus, this research aims to develop a fast, cheap, accurate method utilizing an electronic nose (e-nose) and machine learning algorithms as an alternative method for assessing the freshness quality of marine fishery products (seafood). This experiment employs seven algorithms with hyperparameter optimization to obtain the best performance. Machine learning algorithms are used for classification and regression tasks. The objective is to detect the freshness quality of marine fishery products accurately (classification task) while also identifying microbial populations present in the seafood (regression task). Through extensive investigations, the classification and regression models, specifically employing the k-Nearest Neighbors algorithm, demonstrated remarkable performance, achieving a very high accuracy score. Furthermore, the regression model yielded an RMSE value of 0.03 and an R<sup>2</sup> value of 0.995, indicating the effectiveness of the approach in assessing and quantifying the quality attributes of marine fishery products.https://ieeexplore.ieee.org/document/10154464/Seafoodelectronic nosemachine learning algorithmshyperparameter optimizationclassificationregression |
| spellingShingle | Dedy Rahman Wijaya Nailatul Fadhilah Syarwan Muhammad Agus Nugraha Dahliar Ananda Tora Fahrudin Rini Handayani Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization IEEE Access Seafood electronic nose machine learning algorithms hyperparameter optimization classification regression |
| title | Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization |
| title_full | Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization |
| title_fullStr | Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization |
| title_full_unstemmed | Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization |
| title_short | Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization |
| title_sort | seafood quality detection using electronic nose and machine learning algorithms with hyperparameter optimization |
| topic | Seafood electronic nose machine learning algorithms hyperparameter optimization classification regression |
| url | https://ieeexplore.ieee.org/document/10154464/ |
| work_keys_str_mv | AT dedyrahmanwijaya seafoodqualitydetectionusingelectronicnoseandmachinelearningalgorithmswithhyperparameteroptimization AT nailatulfadhilahsyarwan seafoodqualitydetectionusingelectronicnoseandmachinelearningalgorithmswithhyperparameteroptimization AT muhammadagusnugraha seafoodqualitydetectionusingelectronicnoseandmachinelearningalgorithmswithhyperparameteroptimization AT dahliarananda seafoodqualitydetectionusingelectronicnoseandmachinelearningalgorithmswithhyperparameteroptimization AT torafahrudin seafoodqualitydetectionusingelectronicnoseandmachinelearningalgorithmswithhyperparameteroptimization AT rinihandayani seafoodqualitydetectionusingelectronicnoseandmachinelearningalgorithmswithhyperparameteroptimization |