Domain Correction Based on Kernel Transformation for Drift Compensation in the E-Nose System

This paper proposes a way for drift compensation in electronic noses (e-nose) that often suffers from uncertain and unpredictable sensor drift. Traditional machine learning methods for odor recognition require consistent data distribution, which makes the model trained with previous data less generalized. In the actual application scenario, the data collected previously and the data collected later may have different data distributions due to the sensor drift. If the dataset without sensor drift is treated as a source domain and the dataset with sensor drift as a target domain, a domain correction based on kernel transformation (DCKT) method is proposed to compensate the sensor drift. The proposed method makes the distribution consistency of two domains greatly improved through mapping to a high-dimensional reproducing kernel space and reducing the domain distance. A public benchmark sensor drift dataset is used to verify the effectiveness and efficiency of the proposed DCKT method. The experimental result shows that the proposed method yields the highest average accuracies compared to other considered methods.

[1]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[2]  Lei Zhang,et al.  Anti-drift in E-nose: A subspace projection approach with drift reduction , 2017 .

[3]  M. Sjöström,et al.  Drift correction for gas sensors using multivariate methods , 2000 .

[4]  R. Brereton,et al.  Comparison of performance of five common classifiers represented as boundary methods: Euclidean Distance to Centroids, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Learning Vector Quantization and Support Vector Machines, as dependent on data structure , 2009 .

[5]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[6]  S. De Vito,et al.  Semi-Supervised Learning Techniques in Artificial Olfaction: A Novel Approach to Classification Problems and Drift Counteraction , 2012, IEEE Sensors Journal.

[7]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[8]  Matteo Falasconi,et al.  Drift Correction Methods for Gas Chemical Sensors in Artificial Olfaction Systems: Techniques and Challenges , 2012 .

[9]  Pengfei Jia,et al.  Improving the performance of electronic nose for wound infection detection using orthogonal signal correction and particle swarm optimization , 2014 .

[10]  K. Hayashi,et al.  Neural, fuzzy and neuro-fuzzy approach for concentration estimation of volatile organic compounds by surface acoustic wave sensor array , 2014 .

[11]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[12]  David Zhang,et al.  Correcting Instrumental Variation and Time-Varying Drift: A Transfer Learning Approach With Autoencoders , 2016, IEEE Transactions on Instrumentation and Measurement.

[13]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[14]  S. Guney,et al.  An electronic nose system for assessing horse mackerel freshness , 2012, 2012 International Symposium on Innovations in Intelligent Systems and Applications.

[15]  M. Hoorfar,et al.  On-Chip Electronic Nose For Wine Tasting: A Digital Microfluidic Approach , 2017, IEEE Sensors Journal.

[16]  David Zhang,et al.  Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems , 2015, IEEE Transactions on Instrumentation and Measurement.

[17]  Teerakiat Kerdcharoen,et al.  Electronic nose based wireless sensor network for soil monitoring in precision farming system , 2017, 2017 9th International Conference on Knowledge and Smart Technology (KST).

[18]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[19]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[20]  Tao Liu,et al.  Study on Interference Suppression Algorithms for Electronic Noses: A Review , 2018, Sensors.

[21]  M. Siadat,et al.  A comparison between SVM and PLS for E-nose based gas concentration monitoring , 2018, 2018 IEEE International Conference on Industrial Technology (ICIT).

[22]  David Zhang,et al.  A Novel Semi-Supervised Learning Approach in Artificial Olfaction for E-Nose Application , 2016, IEEE Sensors Journal.

[23]  Alexander Vergara,et al.  On the calibration of sensor arrays for pattern recognition using the minimal number of experiments , 2014 .

[24]  Fengchun Tian,et al.  A correlated information removing based interference suppression technique in electronic nose for detection of bacteria. , 2017, Analytica chimica acta.

[25]  David Zhang,et al.  Calibration transfer and drift compensation of e-noses via coupled task learning , 2016 .

[26]  Alexandre Perera,et al.  Drift compensation of gas sensor array data by Orthogonal Signal Correction , 2010 .

[27]  Kea-Tiong Tang,et al.  Development of an electronic-nose system for fruit maturity and quality monitoring , 2018, 2018 IEEE International Conference on Applied System Invention (ICASI).

[28]  Ricardo Gutierrez-Osuna,et al.  Pattern analysis for machine olfaction: a review , 2002 .

[29]  Edward J. Wolfrum,et al.  Metal Oxide Sensor Arrays for the Detection, Differentiation, and Quantification of Volatile Organic Compounds at Sub-Parts-Per-Million Concentration Levels , 2006 .

[30]  Xue Li,et al.  An Effective Approach to Handling Noise and Drift in Electronic Noses , 2014, ADC.

[31]  A. Gutierrez-Galvez,et al.  Signal and Data Processing for Machine Olfaction and Chemical Sensing: A Review , 2012, IEEE Sensors Journal.

[32]  Angiras Modak,et al.  A novel fuzzy based signal analysis technique in electronic nose and electronic tongue for black tea quality analysis , 2016, 2016 IEEE First International Conference on Control, Measurement and Instrumentation (CMI).

[33]  Shankar Vembu,et al.  Chemical gas sensor drift compensation using classifier ensembles , 2012 .

[34]  Qi Ye,et al.  Classification of multiple indoor air contaminants by an electronic nose and a hybrid support vector machine , 2012 .