ArticleSensors (Basel, Switzerland)2017
Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Intelligent Biosensors Promise Smarter Solutions in Food Safety 4.0.Foods (Basel, Switzerland) · 2024Review
- Detection of the Inoculated Fermentation Process of Apo Pickle Based on a Colorimetric Sensor Array Method.Foods (Basel, Switzerland) · 2022Article
- Volatile Organic Compounds, Evaluation Methods and Processing Properties for Cooked Rice Flavor.Rice (New York, N.Y.) · 2022Review
- Application and Advances in Radiographic and Novel Technologies Used for Non-Intrusive Object Inspection.Sensors (Basel, Switzerland) · 2022Review
- Non-Destructive Detection of Damaged Strawberries after Impact Based on Analyzing Volatile Organic Compounds.Sensors (Basel, Switzerland) · 2022Article
- Odor Detection Using an E-Nose With a Reduced Sensor Array.Sensors (Basel, Switzerland) · 2020Article
- A Prototype to Detect the Alcohol Content of Beers Based on an Electronic Nose.Sensors (Basel, Switzerland) · 2019Article
- Freshness Evaluation of Three Kinds of Meats Based on the Electronic Nose.Sensors (Basel, Switzerland) · 2019Article
- An Electronic Nose Based Method for the Discrimination of Weathered Petroleum-Derived Products.Sensors (Basel, Switzerland) · 2018Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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