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Yan Shi; Xiaofei Jia; Hangcheng Yuan; Shuyue Jia; Jingjing Liu; Hong Men. Origin traceability of rice based on an electronic nose coupled with a feature reduction strategy. Measurement Science and Technology 2020, 32, 025107 .
AMA StyleYan Shi, Xiaofei Jia, Hangcheng Yuan, Shuyue Jia, Jingjing Liu, Hong Men. Origin traceability of rice based on an electronic nose coupled with a feature reduction strategy. Measurement Science and Technology. 2020; 32 (2):025107.
Chicago/Turabian StyleYan Shi; Xiaofei Jia; Hangcheng Yuan; Shuyue Jia; Jingjing Liu; Hong Men. 2020. "Origin traceability of rice based on an electronic nose coupled with a feature reduction strategy." Measurement Science and Technology 32, no. 2: 025107.
As an engineering plastic, acrylonitrile butadiene styrene (ABS) has been widely used in the automobile trims. The odor intensity of ABS can be considered as an important reference for the quality of in-vehicle air. Currently, many automobile manufacturers employ their own testing methods to measure the odor intensity of the trims. Different rules lead that the market lacks a unified standard to evaluate the odor intensity. In this paper, a novel odor evaluation system was proposed to measure the odor intensity of ABS. According to coefficient of variation (CV), analysis of variance (ANOVA), and principal component analysis (PCA), eight sensors were selected to compose an array with stability, repeatability, and selectivity. By means of the pretreatment and the feature extraction, the odor features were quantified by grey relation analysis (GRA). Then, the regression models were constructed by extreme learning machine (ELM), random forest (RF), and support vector machine (SVM) to predict the odor intensity. The results indicated that the quantified data could describe the odor intensity accurately and be predicted well by three models. This study demonstrated that the system achieved perception and quantification of the odor intensity of ABS. In conclusion, a self-developed system was put forward, offering a new technique to evaluate the odor intensity, prospective to replace the manual testing.
Hong Men; Chongbo Yin; Yan Shi; Xiaotong Liu; Hairui Fang; Xiaoju Han; Jingjing Liu. Quantification of Acrylonitrile Butadiene Styrene Odor Intensity Based on a Novel Odor Assessment System With a Sensor Array. IEEE Access 2020, 8, 33237 -33249.
AMA StyleHong Men, Chongbo Yin, Yan Shi, Xiaotong Liu, Hairui Fang, Xiaoju Han, Jingjing Liu. Quantification of Acrylonitrile Butadiene Styrene Odor Intensity Based on a Novel Odor Assessment System With a Sensor Array. IEEE Access. 2020; 8 (99):33237-33249.
Chicago/Turabian StyleHong Men; Chongbo Yin; Yan Shi; Xiaotong Liu; Hairui Fang; Xiaoju Han; Jingjing Liu. 2020. "Quantification of Acrylonitrile Butadiene Styrene Odor Intensity Based on a Novel Odor Assessment System With a Sensor Array." IEEE Access 8, no. 99: 33237-33249.
A novel Kohonen one-class method for quality control of tea.
Yan Shi; Xiaotong Liu; Chongbo Yin; Jingjing Liu; Hong Men. A novel Kohonen one-class method for quality control of tea coupled with artificial lipid membrane taste sensors. Analytical Methods 2020, 12, 1460 -1468.
AMA StyleYan Shi, Xiaotong Liu, Chongbo Yin, Jingjing Liu, Hong Men. A novel Kohonen one-class method for quality control of tea coupled with artificial lipid membrane taste sensors. Analytical Methods. 2020; 12 (11):1460-1468.
Chicago/Turabian StyleYan Shi; Xiaotong Liu; Chongbo Yin; Jingjing Liu; Hong Men. 2020. "A novel Kohonen one-class method for quality control of tea coupled with artificial lipid membrane taste sensors." Analytical Methods 12, no. 11: 1460-1468.
As a taste bionic system, electronic tongues can be used to derive taste information for different types of food. On this basis, we have carried forward the work by making it, in addition to the ability of accurately distinguish samples, be more expressive by speaking evaluative language like human beings. Thus, this paper demonstrates the correlation between the qualitative digital output of the taste bionic system and the fuzzy evaluation language that conform to the human perception mode. First, through principal component analysis (PCA), backward cloud generator and forward cloud generator, two-dimensional cloud droplet groups of different flavor information were established by using liquor taste data collected by electronic tongue. Second, the frequency and order of the evaluation words for different flavor of liquor were obtained by counting and analyzing the data appeared in the artificial sensory evaluation experiment. According to the frequency and order of words, the cloud droplet range corresponding to each word was calculated in the cloud drop group. Finally, the fuzzy evaluations that originated from the eight groups of liquor data with different flavor were compared with the artificial sense, and the results indicated that the model developed in this work is capable of outputting fuzzy evaluation that is consistent with human perception rather than digital output. To sum up, this method enabled the electronic tongue system to generate an output, which conforms to human’s descriptive language, making food detection technology a step closer to human perception.
Jingjing Liu; Mingxu Zuo; Sze Shin Low; Ning Xu; Zhiqing Chen; Chuang Lv; Ying Cui; Yan Shi; Hong Men. Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model. Sensors 2020, 20, 686 .
AMA StyleJingjing Liu, Mingxu Zuo, Sze Shin Low, Ning Xu, Zhiqing Chen, Chuang Lv, Ying Cui, Yan Shi, Hong Men. Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model. Sensors. 2020; 20 (3):686.
Chicago/Turabian StyleJingjing Liu; Mingxu Zuo; Sze Shin Low; Ning Xu; Zhiqing Chen; Chuang Lv; Ying Cui; Yan Shi; Hong Men. 2020. "Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model." Sensors 20, no. 3: 686.
In this work, a deep feature mining method for electronic nose (E-nose) sensor data based on the convolutional neural network (CNN) was proposed in combination with a support vector machine (SVM) to identify beer olfactory information. According to the characteristics of E-nose sensor data, the structure and parameters of the CNN was designed. By means of convolution and pooling operations, the beer olfaction features were extracted automatically. Meanwhile, the SVM replaced the full connection layer of the CNN to enhance the generalization ability of the model, and two important parameters affecting the classification performance of the SVM were optimized based on an improved particle swarm optimization (PSO). The results indicated that the CNN-SVM model achieved deep feature automatic extraction of beer olfactory information, and a good classification performance of 96.67% was obtained in the testing set. This study shows that the CNN-SVM can be used as an effective tool for high precision intelligent identification of beer olfactory information.
Yan Shi; Furong Gong; Mingyang Wang; Jingjing Liu; Yinong Wu; Hong Men. A deep feature mining method of electronic nose sensor data for identifying beer olfactory information. Journal of Food Engineering 2019, 263, 437 -445.
AMA StyleYan Shi, Furong Gong, Mingyang Wang, Jingjing Liu, Yinong Wu, Hong Men. A deep feature mining method of electronic nose sensor data for identifying beer olfactory information. Journal of Food Engineering. 2019; 263 ():437-445.
Chicago/Turabian StyleYan Shi; Furong Gong; Mingyang Wang; Jingjing Liu; Yinong Wu; Hong Men. 2019. "A deep feature mining method of electronic nose sensor data for identifying beer olfactory information." Journal of Food Engineering 263, no. : 437-445.
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.
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 StyleHong 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 StyleHong 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.
A synergetic strategy to extract and select the effective information of sensor signal for e-nose.
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 StyleHong 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 StyleHong 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.
Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33–100%, and ELM, with an accuracy rate of 98.01–100%. For level assessment, the R2 related to the training set was above 0.97 and the R2 related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016–0.3494, lower than the error of 0.5–1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level.
Hong Men; Songlin Fu; Jialin Yang; Meiqi Cheng; Yan Shi; Jingjing Liu. Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples. Sensors 2018, 18, 285 .
AMA StyleHong Men, Songlin Fu, Jialin Yang, Meiqi Cheng, Yan Shi, Jingjing Liu. Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples. Sensors. 2018; 18 (1):285.
Chicago/Turabian StyleHong Men; Songlin Fu; Jialin Yang; Meiqi Cheng; Yan Shi; Jingjing Liu. 2018. "Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples." Sensors 18, no. 1: 285.
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.
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 StyleHong 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 StyleHong 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.