ArticleSensors (Basel, Switzerland)2017
Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose.
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 11 papers.
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Who cites it
11 citing papers in PubMed.
- Geographical Origin Traceability of Atractylodis Macrocephalae Rhizoma Based on Chemical Composition, Chromaticity, and Electronic Nose.Molecules (Basel, Switzerland) · 2024Article
- Research Progress of Machine Learning in Extending and Regulating the Shelf Life of Fruits and Vegetables.Foods (Basel, Switzerland) · 2024Review
- A Preliminary Study on the Effect of Adding Sugarcane Syrup on the Flavor of Barley Lager Fermentation.Foods (Basel, Switzerland) · 2024Article
- Evaluation of a Voltametric E-Tongue Combined with Data Preprocessing for Fast and Effective Machine Learning-Based Classification of Tomato Purées by Cultivar.Sensors (Basel, Switzerland) · 2024Article
- Nanotechnology and E-Sensing for Food Chain Quality and Safety.Sensors (Basel, Switzerland) · 2023Review
- Application of Multiple-Source Data Fusion for the Discrimination of Two Botanical Origins of Magnolia Officinalis Cortex Based on E-Nose Measurements, E-Tongue Measurements, and Chemical Analysis.Molecules (Basel, Switzerland) · 2022Article
- Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors.Sensors (Basel, Switzerland) · 2021Review
- Classification forSensors (Basel, Switzerland) · 2020Article
- Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model.Sensors (Basel, Switzerland) · 2020Article
- Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches.Sensors (Basel, Switzerland) · 2019Article
- Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory Evaluation.Sensors (Basel, Switzerland) · 2018Article
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Authors and funding
6 authors.
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Abstract
Multi-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining method based on variable accumulation to find the best expression form and variables' behavior affecting beer flavor. First, e-tongue and e-nose were used to gather the taste and olfactory information of beer, respectively. Second, principal component analysis (PCA), genetic algorithm-partial least squares (GA-PLS), and variable importance of projection (VIP) scores were applied to select feature variables of the original fusion set. Finally, the classification models based on support vector machine (SVM), random forests (RF), and extreme learning machine (ELM) were established to evaluate the efficiency of the feature-mining method. The result shows that the feature-mining method based on variable accumulation obtains the main feature affecting beer flavor information, and the best classification performance for the SVM, RF, and ELM models with 96.67%, 94.44%, and 98.33% prediction accuracy, respectively.
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