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Yanan Jiao
Advanced Sensor Technology Institute, College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China;(Y.J.);(Y.S.);(F.G.);(H.F.)

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Journal article
Published: 10 October 2018 in Sensors
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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.

ACS Style

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 2018, 18, 3387 .

AMA Style

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. 2018; 18 (10):3387.

Chicago/Turabian Style

Hong Men; Yanan Jiao; Yan Shi; Furong Gong; Yizhou Chen; Hairui Fang; Jingjing Liu. 2018. "Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory Evaluation." Sensors 18, no. 10: 3387.

Journals
Published: 03 April 2018 in Analytical Methods
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A synergetic strategy to extract and select the effective information of sensor signal for e-nose.

ACS Style

Hong Men; Yan Shi; Yanan Jiao; Furong Gong; Jingjing Liu. Electronic nose sensors data feature mining: a synergetic strategy for the classification of beer. Analytical Methods 2018, 10, 2016 -2025.

AMA Style

Hong Men, Yan Shi, Yanan Jiao, Furong Gong, Jingjing Liu. Electronic nose sensors data feature mining: a synergetic strategy for the classification of beer. Analytical Methods. 2018; 10 (17):2016-2025.

Chicago/Turabian Style

Hong Men; Yan Shi; Yanan Jiao; Furong Gong; Jingjing Liu. 2018. "Electronic nose sensors data feature mining: a synergetic strategy for the classification of beer." Analytical Methods 10, no. 17: 2016-2025.

Article
Published: 19 July 2017 in Sensors
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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.

ACS Style

Hong Men; Yan Shi; Songlin Fu; Yanan Jiao; Yu Qiao; Jingjing Liu. Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose. Sensors 2017, 17, 1656 .

AMA Style

Hong Men, Yan Shi, Songlin Fu, Yanan Jiao, Yu Qiao, Jingjing Liu. Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose. Sensors. 2017; 17 (7):1656.

Chicago/Turabian Style

Hong Men; Yan Shi; Songlin Fu; Yanan Jiao; Yu Qiao; Jingjing Liu. 2017. "Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose." Sensors 17, no. 7: 1656.