Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform
In this study, a portable electronic nose (E-nose) was self-developed to identify rice wines with different marked ages—all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and the obtained response value...
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doaj-4d227244f9fc4ec3860d761b35eb26af2020-11-25T00:15:36ZengMDPI AGSensors1424-82202017-10-011711250010.3390/s17112500s17112500Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage PlatformZhebo Wei0Xize Xiao1Jun Wang2Hui Wang3Department of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, ChinaDepartment of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, ChinaDepartment of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, ChinaDepartment of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, ChinaIn this study, a portable electronic nose (E-nose) was self-developed to identify rice wines with different marked ages—all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and the obtained response values were transmitted to the Smartphone thorough a wireless communication module. Then, Aliyun worked as a cloud storage platform for the storage of responses and identification models. The measurement of the E-nose was composed of the taste information obtained phase (TIOP) and the aftertaste information obtained phase (AIOP). The area feature data obtained from the TIOP and the feature data obtained from the TIOP-AIOP were applied to identify rice wines by using pattern recognition methods. Principal component analysis (PCA), locally linear embedding (LLE) and linear discriminant analysis (LDA) were applied for the classification of those wine samples. LDA based on the area feature data obtained from the TIOP-AIOP proved a powerful tool and showed the best classification results. Partial least-squares regression (PLSR) and support vector machine (SVM) were applied for the predictions of marked ages and SVM (R2 = 0.9942) worked much better than PLSR.https://www.mdpi.com/1424-8220/17/11/2500rice winemarked ageSmartphoneelectronic nose |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhebo Wei Xize Xiao Jun Wang Hui Wang |
spellingShingle |
Zhebo Wei Xize Xiao Jun Wang Hui Wang Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform Sensors rice wine marked age Smartphone electronic nose |
author_facet |
Zhebo Wei Xize Xiao Jun Wang Hui Wang |
author_sort |
Zhebo Wei |
title |
Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform |
title_short |
Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform |
title_full |
Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform |
title_fullStr |
Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform |
title_full_unstemmed |
Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform |
title_sort |
identification of the rice wines with different marked ages by electronic nose coupled with smartphone and cloud storage platform |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2017-10-01 |
description |
In this study, a portable electronic nose (E-nose) was self-developed to identify rice wines with different marked ages—all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and the obtained response values were transmitted to the Smartphone thorough a wireless communication module. Then, Aliyun worked as a cloud storage platform for the storage of responses and identification models. The measurement of the E-nose was composed of the taste information obtained phase (TIOP) and the aftertaste information obtained phase (AIOP). The area feature data obtained from the TIOP and the feature data obtained from the TIOP-AIOP were applied to identify rice wines by using pattern recognition methods. Principal component analysis (PCA), locally linear embedding (LLE) and linear discriminant analysis (LDA) were applied for the classification of those wine samples. LDA based on the area feature data obtained from the TIOP-AIOP proved a powerful tool and showed the best classification results. Partial least-squares regression (PLSR) and support vector machine (SVM) were applied for the predictions of marked ages and SVM (R2 = 0.9942) worked much better than PLSR. |
topic |
rice wine marked age Smartphone electronic nose |
url |
https://www.mdpi.com/1424-8220/17/11/2500 |
work_keys_str_mv |
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