Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory Evaluation
In this paper, we aim to use odor fingerprint analysis to identify and detect various odors. We obtained the olfactory sensory evaluation of eight different brands of Chinese liquor by a lab-developed intelligent nose. From the respective combination of the time domain and frequency domain, we extra...
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doaj-3d09feb482dd41248e6b0aaa8d54e7a42020-11-24T23:03:48ZengMDPI AGSensors1424-82202018-10-011810338710.3390/s18103387s18103387Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory EvaluationHong Men0Yanan Jiao1Yan Shi2Furong Gong3Yizhou Chen4Hairui Fang5Jingjing Liu6Advanced Sensor Technology Institute, College of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaAdvanced Sensor Technology Institute, College of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaAdvanced Sensor Technology Institute, College of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaAdvanced Sensor Technology Institute, College of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaDepartment of Neurobiology and Behavior, University of California, Irvine, CA 92697, USAAdvanced Sensor Technology Institute, College of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaAdvanced Sensor Technology Institute, College of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaIn this paper, we aim to use odor fingerprint analysis to identify and detect various odors. We obtained the olfactory sensory evaluation of eight different brands of Chinese liquor by a lab-developed intelligent nose. From the respective combination of the time domain and frequency domain, we extract features to reflect the samples comprehensively. However, the extracted feature combined time domain and frequency domain will bring redundant information that affects performance. Therefore, we proposed data by Principal Component Analysis (PCA) and Variable Importance Projection (VIP) to delete redundant information to construct a more precise odor fingerprint. Then, Random Forest (RF) and Probabilistic Neural Network (PNN) were built based on the above. Results showed that the VIP-based models achieved better classification performance than PCA-based models. In addition, the peak performance (92.5%) of the VIP-RF model had a higher classification rate than the VIP-PNN model (90%). In conclusion, odor fingerprint analysis using a feature mining method based on the olfactory sensory evaluation can be applied to monitor product quality in the actual process of industrialization.http://www.mdpi.com/1424-8220/18/10/3387odor fingerprint analysisfeature mining methodolfactory sensory evaluationtime domainfrequency domainintelligent noseChinese liquor |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hong Men Yanan Jiao Yan Shi Furong Gong Yizhou Chen Hairui Fang Jingjing Liu |
spellingShingle |
Hong Men Yanan Jiao Yan Shi Furong Gong Yizhou Chen Hairui Fang Jingjing Liu Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory Evaluation Sensors odor fingerprint analysis feature mining method olfactory sensory evaluation time domain frequency domain intelligent nose Chinese liquor |
author_facet |
Hong Men Yanan Jiao Yan Shi Furong Gong Yizhou Chen Hairui Fang Jingjing Liu |
author_sort |
Hong Men |
title |
Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory Evaluation |
title_short |
Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory Evaluation |
title_full |
Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory Evaluation |
title_fullStr |
Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory Evaluation |
title_full_unstemmed |
Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory Evaluation |
title_sort |
odor fingerprint analysis using feature mining method based on olfactory sensory evaluation |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2018-10-01 |
description |
In this paper, we aim to use odor fingerprint analysis to identify and detect various odors. We obtained the olfactory sensory evaluation of eight different brands of Chinese liquor by a lab-developed intelligent nose. From the respective combination of the time domain and frequency domain, we extract features to reflect the samples comprehensively. However, the extracted feature combined time domain and frequency domain will bring redundant information that affects performance. Therefore, we proposed data by Principal Component Analysis (PCA) and Variable Importance Projection (VIP) to delete redundant information to construct a more precise odor fingerprint. Then, Random Forest (RF) and Probabilistic Neural Network (PNN) were built based on the above. Results showed that the VIP-based models achieved better classification performance than PCA-based models. In addition, the peak performance (92.5%) of the VIP-RF model had a higher classification rate than the VIP-PNN model (90%). In conclusion, odor fingerprint analysis using a feature mining method based on the olfactory sensory evaluation can be applied to monitor product quality in the actual process of industrialization. |
topic |
odor fingerprint analysis feature mining method olfactory sensory evaluation time domain frequency domain intelligent nose Chinese liquor |
url |
http://www.mdpi.com/1424-8220/18/10/3387 |
work_keys_str_mv |
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1725632018336710656 |