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Hong Men
School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China

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Journal article
Published: 15 July 2021 in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
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In this work, a neural network framework for hyperspectral information recognition was proposed, combined with residual block and convolutional block attention module (CBAM) to enhance the detection performance of hyperspectral for tracing the rice quality. Firstly, the hyperspectral image system was used to obtain the hyperspectral information of the rice. Secondly, due to the small data set, the structure of the residual network was designed based on the characteristics of the hyperspectral information to prevent overfitting the model. Finally, the CBAM was introduced to calculate the channel and spatial attention to redistribute the weight parameter and enhance the classification performance of the model. The results showed that our (Res-CBAM) model had better classification performance than other classification methods. The classification accuracy of the rice was 96.33%. This study provided a strategy to enhance the detection performance of hyperspectral, and an intelligent technology to trace the rice quality.

ACS Style

Hong Men; Hangcheng Yuan; Yan Shi; Mei Liu; Qiuping Wang; Jingjing Liu. A Residual Network with Attention Module for Hyperspectral Information of Recognition to Trace the Origin of Rice. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2021, 263, 120155 .

AMA Style

Hong Men, Hangcheng Yuan, Yan Shi, Mei Liu, Qiuping Wang, Jingjing Liu. A Residual Network with Attention Module for Hyperspectral Information of Recognition to Trace the Origin of Rice. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2021; 263 ():120155.

Chicago/Turabian Style

Hong Men; Hangcheng Yuan; Yan Shi; Mei Liu; Qiuping Wang; Jingjing Liu. 2021. "A Residual Network with Attention Module for Hyperspectral Information of Recognition to Trace the Origin of Rice." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 263, no. : 120155.

Journal article
Published: 12 May 2021 in IEEE Sensors Journal
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The quality of rice produced in different origins is different, and the gas reflects the external sensory information of rice. Based on the electronic nose (e-nose) instrument, the gas information of rice from different origins is obtained. An effective feature processing method is a key issue to improve the detection performance of e-nose. In this work, a fast pearson graph convolutional network (FPGCN) is proposed to identify the features extracted by the e-nose sensors and realize the origin tracking of rice. Based on the pearson correlation coefficient (PCC) value, the correlation between the features is quantified to construct the graph Laplacian matrix of graph convolutional network (GCN). The Chebyshev polynomial is introduced to reduce the computational complexity and parameters of GCN, and combine the binary tree method to speed up the pooling calculation. A multi-layer structure of FPGCN is designed to achieve the gas identification of rice. Compared with the traditional feature processing method, the FPGCN has a better classification result of 98.28%, the best F1-score is 0.9829, and the best Kappa coefficient is 0.9799. In conclusion, the FPGCN provides an effective theoretical method to improve the detection performance of e-nose and a new technology to track the rice quality.

ACS Style

Yan Shi; Mei Liu; Ao Sun; Jingjing Liu; Hong Men. A Fast Pearson Graph Convolutional Network Combined with Electronic Nose to Identify the Origin of Rice. IEEE Sensors Journal 2021, PP, 1 -1.

AMA Style

Yan Shi, Mei Liu, Ao Sun, Jingjing Liu, Hong Men. A Fast Pearson Graph Convolutional Network Combined with Electronic Nose to Identify the Origin of Rice. IEEE Sensors Journal. 2021; PP (99):1-1.

Chicago/Turabian Style

Yan Shi; Mei Liu; Ao Sun; Jingjing Liu; Hong Men. 2021. "A Fast Pearson Graph Convolutional Network Combined with Electronic Nose to Identify the Origin of Rice." IEEE Sensors Journal PP, no. 99: 1-1.

Journal article
Published: 28 January 2021 in Sensors and Actuators B: Chemical
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Although multi-sensor system can obtain the comprehensive information of detected object from different information sources, the direct fusion of multi-data contains a lot of redundant information, which will reduce the detection accuracy. In this work, a collaborative strategy was proposed to track the quality difference of rice, it combined the deep learning and machine learning theory to improve the detection performance of fusion system. Firstly, the quality information of rice was collected based on the electronic nose (e-nose) and hyperspectral imaging system. Secondly, a new structure of convolutional neural network (CNN) was designed to extract the features of fusion data based on the convolution and pooling processes. Finally, a novel global extension extreme learning machine (GE-ELM) was proposed, which combined the dragging factor and global identification coefficients to expand and balance the differences between classes, thereby improving the identification ability and enhancing the stability. Compared with the traditional feature mining and recognition methods, CNN extracted the fusion features effectively, an excellent classification performance of 98.07 % was obtained based on the GE-ELM. In conclusion, CNN-GE-ELM was demonstrated as an effective analytical technique to improve the detection performance of fusion system and achieve the high-precision recognition for the quality difference of rice.

ACS Style

Yan Shi; Hangcheng Yuan; Chenao Xiong; Qi Zhang; Shuyue Jia; Jingjing Liu; Hong Men. Improving performance: A collaborative strategy for the multi-data fusion of electronic nose and hyperspectral to track the quality difference of rice. Sensors and Actuators B: Chemical 2021, 333, 129546 .

AMA Style

Yan Shi, Hangcheng Yuan, Chenao Xiong, Qi Zhang, Shuyue Jia, Jingjing Liu, Hong Men. Improving performance: A collaborative strategy for the multi-data fusion of electronic nose and hyperspectral to track the quality difference of rice. Sensors and Actuators B: Chemical. 2021; 333 ():129546.

Chicago/Turabian Style

Yan Shi; Hangcheng Yuan; Chenao Xiong; Qi Zhang; Shuyue Jia; Jingjing Liu; Hong Men. 2021. "Improving performance: A collaborative strategy for the multi-data fusion of electronic nose and hyperspectral to track the quality difference of rice." Sensors and Actuators B: Chemical 333, no. : 129546.

Journal article
Published: 26 November 2020 in Measurement Science and Technology
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ACS Style

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 Style

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 (2):025107.

Chicago/Turabian Style

Yan 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.

Journal article
Published: 09 November 2020 in Sensors and Actuators A: Physical
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Polypropylene (PP), as a common automotive plastic, enhances the performance of parts, but emits odorous volatile compounds and increases the risk of exposure to air pollutants. The organic compounds and carbonyl compounds emitted can induce respiratory diseases and even cancers. However, the measurement of odor intensity is regarded as a challenge for the vehicle market in order to meet the increasingly strict environment regulations. This study aimed to numerically express the odor intensity of volatile compounds from automotive polypropylene. The samples with different odor intensities were prepared by the thermostatic heater, and the electronic nose composed of ten various sensors was employed to acquire the odor information. The factor analysis was implemented to analyze the odor data of different intensities. The results showed that the odor intensity values of four materials were around 1.3 at 50 ℃, which increased to 1.8, 2.0, 2.3, and 1.9 respectively at 80 ℃. Besides, grey relation analysis was introduced to verify the results of numerical expression, and the corresponding relation degree in accord with the odor intensity value. This study shows the numerical expression could be used as an effective alternative to rapidly and objectively describe the odor intensity in vehicles.

ACS Style

Hong Men; Chongbo Yin; Yan Shi; Yanwei Wang; Jingjing Liu. Numerical expression of odor intensity of volatile compounds from automotive polypropylene. Sensors and Actuators A: Physical 2020, 321, 112426 .

AMA Style

Hong Men, Chongbo Yin, Yan Shi, Yanwei Wang, Jingjing Liu. Numerical expression of odor intensity of volatile compounds from automotive polypropylene. Sensors and Actuators A: Physical. 2020; 321 ():112426.

Chicago/Turabian Style

Hong Men; Chongbo Yin; Yan Shi; Yanwei Wang; Jingjing Liu. 2020. "Numerical expression of odor intensity of volatile compounds from automotive polypropylene." Sensors and Actuators A: Physical 321, no. : 112426.

Journal article
Published: 28 April 2020 in Mathematical Problems in Engineering
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Aiming at the optimization layout of distributed low-impact development (LID) practices in the sponge city, a new mathematical method combining Stormwater Management Model (SWMM) and preference-inspired co-evolutionary algorithm using goal vectors (PICEA-g) was developed and was applied in the Ximen waterlogged area of Pingxiang City. Firstly, a block-scaled rainfall-runoff model was built in the study area by using SWMM. Then, an LIDs area optimization model was established by linking the SWMM and the PICEA-g based on the Matlab platform, which took the area ratios of various LIDs in each block as decision variables and took the total runoff, peak flow, suspended substance (SS) pollutant, and LIDs cost as objective functions. Thus, the problem of LIDs layout was turned into a mathematical optimization issue. So the cost-benefit optimal solutions with different emphases were found by using this algorithm, and the LIDs layout optimal scheme for this area was further analysed and verified by rainfall-runoff model. The results show that the total runoff reduction rates of the system reach a maximum of 21.8%, the peak flow reduction rates of the system are more than 10%, and the SS pollutant reduction rates are reduced by about 30% compared with before LIDs under the design storms of different return periods. The reduction rates of each runoff index are higher than the nondominated sorting genetic algorithm II (NSGA-II) method, and decision-makers can more effectively analyse the cost-benefit optimal solution from the Pareto solution sets. Therefore, the LIDs layout optimization method proposed in this paper has obvious advantages in solving similar many-objective optimization problems (MOOPs) in sponge city construction.

ACS Style

Hong Men; Hao Lu; Wenjuan Jiang; Duo Xu. Mathematical Optimization Method of Low-Impact Development Layout in the Sponge City. Mathematical Problems in Engineering 2020, 2020, 1 -17.

AMA Style

Hong Men, Hao Lu, Wenjuan Jiang, Duo Xu. Mathematical Optimization Method of Low-Impact Development Layout in the Sponge City. Mathematical Problems in Engineering. 2020; 2020 ():1-17.

Chicago/Turabian Style

Hong Men; Hao Lu; Wenjuan Jiang; Duo Xu. 2020. "Mathematical Optimization Method of Low-Impact Development Layout in the Sponge City." Mathematical Problems in Engineering 2020, no. : 1-17.

Journal article
Published: 21 April 2020 in International Journal of Food Engineering
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Precise classification of seeds is important for agriculture. Due to the slight physical and chemical difference between different types of wheat and high correlation between bands of images, it is easy to fall into the local optimum when selecting the characteristic band of using the spectral average only. In this paper, in order to solve this problem, a new variable fusion strategy was proposed based on successive projection algorithm and the variable importance in projection algorithm to obtain a comprehensive and representative variable feature for higher classification accuracy, within spectral mean and spectral standard deviation, so the 25 feature bands obtained are classified by support vector machine, and the classification accuracy rate reached 83.3%. It indicates that the new fusion strategy can mine the effective features of hyperspectral data better to improve the accuracy of the model and it can provide a theoretical basis for the hyperspectral classification of tiny kernels.

ACS Style

Jingjing Liu; Simeng Liu; Tie Shi; Xiaonan Wang; Yizhou Chen; Fulong Liu; Hong Men. A modified feature fusion method for distinguishing seed strains using hyperspectral data. International Journal of Food Engineering 2020, 16, 1 .

AMA Style

Jingjing Liu, Simeng Liu, Tie Shi, Xiaonan Wang, Yizhou Chen, Fulong Liu, Hong Men. A modified feature fusion method for distinguishing seed strains using hyperspectral data. International Journal of Food Engineering. 2020; 16 (7):1.

Chicago/Turabian Style

Jingjing Liu; Simeng Liu; Tie Shi; Xiaonan Wang; Yizhou Chen; Fulong Liu; Hong Men. 2020. "A modified feature fusion method for distinguishing seed strains using hyperspectral data." International Journal of Food Engineering 16, no. 7: 1.

Journal article
Published: 05 March 2020 in Sensors
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Cortisol is commonly used as a significant biomarker of psychological or physical stress. With the accelerated pace of life, non-invasive cortisol detection at the point of care (POC) is in high demand for personal health monitoring. In this paper, an ultrasensitive immunosensor using gold nanoparticles/molybdenum disulfide/gold nanoparticles (AuNPs/MoS2/AuNPs) as transducer was explored for non-invasive salivary cortisol monitoring at POC with the miniaturized differential pulse voltammetry (DPV) system based on a smartphone. Covalent binding of cortisol antibody (CORT-Ab) onto the AuNPs/MoS2/AuNPs transducer was achieved through the self-assembled monolayer of specially designed polyethylene glycol (PEG, SH-PEG-COOH). Non-specific binding was avoided by passivating the surface with ethanolamine. The miniaturized portable DPV system was utilized for human salivary cortisol detection. A series current response of different cortisol concentrations decreased and exhibited a linear range of 0.5–200 nM, the detection limit of 0.11 nM, and high sensitivity of 30 μA M−1 with a regression coefficient of 0.9947. Cortisol was also distinguished successfully from the other substances in saliva. The recovery ratio of spiked human salivary cortisol and the variation of salivary cortisol level during one day indicated the practicability of the immunosensor based on the portable system. The results demonstrated the excellent performance of the smartphone-based immunosensor system and its great potential application for non-invasive human salivary cortisol detection at POC.

ACS Style

Jingjing Liu; Ning Xu; Hong Men; Shuang Li; Yanli Lu; Sze Shin Low; Xin Li; Lihang Zhu; Chen Cheng; Gang Xu; Qingjun Liu. Salivary Cortisol Determination on Smartphone-Based Differential Pulse Voltammetry System. Sensors 2020, 20, 1422 .

AMA Style

Jingjing Liu, Ning Xu, Hong Men, Shuang Li, Yanli Lu, Sze Shin Low, Xin Li, Lihang Zhu, Chen Cheng, Gang Xu, Qingjun Liu. Salivary Cortisol Determination on Smartphone-Based Differential Pulse Voltammetry System. Sensors. 2020; 20 (5):1422.

Chicago/Turabian Style

Jingjing Liu; Ning Xu; Hong Men; Shuang Li; Yanli Lu; Sze Shin Low; Xin Li; Lihang Zhu; Chen Cheng; Gang Xu; Qingjun Liu. 2020. "Salivary Cortisol Determination on Smartphone-Based Differential Pulse Voltammetry System." Sensors 20, no. 5: 1422.

Journal article
Published: 13 February 2020 in IEEE Access
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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.

ACS Style

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 Style

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 (99):33237-33249.

Chicago/Turabian Style

Hong 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.

Paper
Published: 12 February 2020 in Analytical Methods
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A novel Kohonen one-class method for quality control of tea.

ACS Style

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 Style

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 (11):1460-1468.

Chicago/Turabian Style

Yan 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.

Journal article
Published: 27 January 2020 in Sensors
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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.

ACS Style

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 Style

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 (3):686.

Chicago/Turabian Style

Jingjing 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.

Journal article
Published: 29 July 2019 in Journal of Food Engineering
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Yan 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.

Journal article
Published: 03 December 2018 in Sensors
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In this study, to obtain a texture perception that is closer to the human sense, we designed eight bionic tongue indenters based on the law of the physiology of mandibular movements and tongue movements features, set up a bionic tongue distributed mechanical testing device, performed in vitro simulations to obtain the distributed mechanical information over the tongue surface, and preliminarily constructed a food fineness perception evaluation model. By capturing a large number of tongue movements during chewing, we analyzed and simulated four representative tongue movement states including the tiled state, sunken state, raised state, and overturned state of the tongue. By analyzing curvature parameters and the Gauss curvature of the tongue surface, we selected the regional circle of interest. With that, eight bionic tongue indenters with different curvatures over the tongue surface were designed. Together with an arrayed film pressure sensor, we set up a bionic tongue distributed mechanical testing device, which was used to do contact pressure experiments on three kinds of cookies—WZ Cookie, ZL Cookie and JSL Cookie—with different fineness texture characteristics. Based on the distributed mechanical information perceived by the surface of the bionic tongue indenter, we established a food fineness perception evaluation model by defining three indicators, including gradient, stress change rate and areal density. The correlation between the sensory assessment and model result was analyzed. The results showed that the average values of correlation coefficients among the three kinds of food with the eight bionic tongue indenters reached 0.887, 0.865, and 0.870, respectively, that is, a significant correlation was achieved. The results illustrate that the food fineness perception evaluation model is effective, and the bionic tongue distributed mechanical testing device has a good practical significance for obtaining food texture mouthfeel information.

ACS Style

Jingjing Liu; Ying Cui; Yizhou Chen; Wei Wang; Yuanyuan Tang; Hong Men. Evaluation of Food Fineness by the Bionic Tongue Distributed Mechanical Testing Device. Sensors 2018, 18, 4250 .

AMA Style

Jingjing Liu, Ying Cui, Yizhou Chen, Wei Wang, Yuanyuan Tang, Hong Men. Evaluation of Food Fineness by the Bionic Tongue Distributed Mechanical Testing Device. Sensors. 2018; 18 (12):4250.

Chicago/Turabian Style

Jingjing Liu; Ying Cui; Yizhou Chen; Wei Wang; Yuanyuan Tang; Hong Men. 2018. "Evaluation of Food Fineness by the Bionic Tongue Distributed Mechanical Testing Device." Sensors 18, no. 12: 4250.

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.

Journal article
Published: 18 January 2018 in Sensors
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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.

ACS Style

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 Style

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 (1):285.

Chicago/Turabian Style

Hong 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.

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.

Journal article
Published: 03 May 2016 in Anti-Corrosion Methods and Materials
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Purpose The purpose of this study is to analyze the corrosion behavior of 304SS in three kinds of solution, 3.5 per cent NaCl, 5 per cent H2SO4 and 1 M (1 mol/L) NaOH, using electrochemical noise. Design/methodology/approach Corrosion types and rates were characterized by spectrum and time-domain analysis. EN signals were evaluated using a novel method of phase space reconstruction and chaos theory. To evaluate the chaotic characteristics of corrosion systems, the delay time was obtained by the mutual information method and the embedding dimension was obtained by the average false neighbors method. Findings The varying degrees of chaos in the corrosion systems were indicated by positive largest Lyapunov exponents of the electrochemical potential noise. Originality/value The change of correlation dimension in three kinds of solution demonstrated significant differences, clearly differentiating various types of corrosion.

ACS Style

Hong Men; Bin Sun; Xiao Zhao; Xiujie Li; Jingjing Liu; Zhiming Xu. Dynamics of stainless steel corrosion based on the theory of phase space reconstruction and chaos. Anti-Corrosion Methods and Materials 2016, 63, 214 -225.

AMA Style

Hong Men, Bin Sun, Xiao Zhao, Xiujie Li, Jingjing Liu, Zhiming Xu. Dynamics of stainless steel corrosion based on the theory of phase space reconstruction and chaos. Anti-Corrosion Methods and Materials. 2016; 63 (3):214-225.

Chicago/Turabian Style

Hong Men; Bin Sun; Xiao Zhao; Xiujie Li; Jingjing Liu; Zhiming Xu. 2016. "Dynamics of stainless steel corrosion based on the theory of phase space reconstruction and chaos." Anti-Corrosion Methods and Materials 63, no. 3: 214-225.

Journal article
Published: 01 July 2014 in Applied Mechanics and Materials
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The transparent conductive film made of Indium Tin Oxide (ITO) is widely used in many fields because of its excellent electrical conductivity and optical properties. At present, the information in biological detection field has not satisfied the people's needs, which are obtained based on some kind of sensors with traditional rigid substrate preparation. The contact surface for creatures perceive and stimulation are flexible surfaces. The kinds of electrode with ITO conductive film as the base for electrode preparation have a great application prospect in biological information collection.

ACS Style

Jing Jing Liu; Hong Men; Qing Tian Zheng; Wei Kang Jiang; Hong Hui Gao; Xiao Zhao. The Production of ITO Transparent Conductive Materials and the Development Prospect in the Field of Biological Information. Applied Mechanics and Materials 2014, 597, 188 -191.

AMA Style

Jing Jing Liu, Hong Men, Qing Tian Zheng, Wei Kang Jiang, Hong Hui Gao, Xiao Zhao. The Production of ITO Transparent Conductive Materials and the Development Prospect in the Field of Biological Information. Applied Mechanics and Materials. 2014; 597 ():188-191.

Chicago/Turabian Style

Jing Jing Liu; Hong Men; Qing Tian Zheng; Wei Kang Jiang; Hong Hui Gao; Xiao Zhao. 2014. "The Production of ITO Transparent Conductive Materials and the Development Prospect in the Field of Biological Information." Applied Mechanics and Materials 597, no. : 188-191.

Research article
Published: 26 May 2014 in Journal of Sensors
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For the problem of the waste of the edible-oil in the food processing, on the premise of food security, they often need to add new edible-oil to the old frying oil which had been used in food processing to control the cost of the production. Due to the fact that the different additive proportion of the oil has different material and different volatile gases, we use fusion technology based on the electronic nose and electronic tongue to detect the blending ratio of the old frying oil and the new edible-oil in this paper. Principal component analysis (PCA) is used to distinguish the different proportion of the old frying oil and new edible-oil; on the other hand we use partial least squares (PLS) to predict the blending ratio of the old frying oil and new edible-oil. Two conclusions were proposed: data fusion of electronic nose and electronic tongue can be used to detect the blending ratio of the old frying oil and new edible-oil; in contrast to single used electronic nose or single used electronic tongue, the detection effect has increased by using data fusion of electronic nose and electronic tongue.

ACS Style

Hong Men; Donglin Chen; Xiaoting Zhang; Jingjing Liu; Ke Ning. Data Fusion of Electronic Nose and Electronic Tongue for Detection of Mixed Edible-Oil. Journal of Sensors 2014, 2014, 1 -7.

AMA Style

Hong Men, Donglin Chen, Xiaoting Zhang, Jingjing Liu, Ke Ning. Data Fusion of Electronic Nose and Electronic Tongue for Detection of Mixed Edible-Oil. Journal of Sensors. 2014; 2014 ():1-7.

Chicago/Turabian Style

Hong Men; Donglin Chen; Xiaoting Zhang; Jingjing Liu; Ke Ning. 2014. "Data Fusion of Electronic Nose and Electronic Tongue for Detection of Mixed Edible-Oil." Journal of Sensors 2014, no. : 1-7.