Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples
Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraff...
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| Format: | Article |
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MDPI AG
2018-01-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/1/285 |
| _version_ | 1798034990168014848 |
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| author | Hong Men Songlin Fu Jialin Yang Meiqi Cheng Yan Shi Jingjing Liu |
| author_facet | Hong Men Songlin Fu Jialin Yang Meiqi Cheng Yan Shi Jingjing Liu |
| author_sort | Hong Men |
| collection | DOAJ |
| description | Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33–100%, and ELM, with an accuracy rate of 98.01–100%. For level assessment, the R2 related to the training set was above 0.97 and the R2 related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016–0.3494, lower than the error of 0.5–1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level. |
| first_indexed | 2024-04-11T20:50:58Z |
| format | Article |
| id | doaj.art-6a3a2ff2b27a45028f5634aafc0e0621 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T20:50:58Z |
| publishDate | 2018-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-6a3a2ff2b27a45028f5634aafc0e06212022-12-22T04:03:50ZengMDPI AGSensors1424-82202018-01-0118128510.3390/s18010285s18010285Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin SamplesHong Men0Songlin Fu1Jialin Yang2Meiqi Cheng3Yan Shi4Jingjing Liu5School of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaSchool of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaSchool of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaSchool of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaSchool of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaSchool of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaParaffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33–100%, and ELM, with an accuracy rate of 98.01–100%. For level assessment, the R2 related to the training set was above 0.97 and the R2 related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016–0.3494, lower than the error of 0.5–1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level.http://www.mdpi.com/1424-8220/18/1/285paraffinparaffin odor analysis systemlevelclassifygrade |
| spellingShingle | Hong Men Songlin Fu Jialin Yang Meiqi Cheng Yan Shi Jingjing Liu Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples Sensors paraffin paraffin odor analysis system level classify grade |
| title | Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples |
| title_full | Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples |
| title_fullStr | Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples |
| title_full_unstemmed | Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples |
| title_short | Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples |
| title_sort | comparison of svm rf and elm on an electronic nose for the intelligent evaluation of paraffin samples |
| topic | paraffin paraffin odor analysis system level classify grade |
| url | http://www.mdpi.com/1424-8220/18/1/285 |
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