Noise filtering framework for electronic nose signals: An application for beef quality monitoring

Abstract Beef is one of the most popular and widely consumed foodstuffs in the world. Nevertheless, it can easily decay if not properly treated during distribution and storage. The consumption of low quality beef causes a serious health hazard. The electronic nose (e-nose) is a rapid and low-cost instrument for beef quality classification. Hence, the development of a mobile e-nose for online meat quality monitoring is appealing. In the last few years, e-noses have been used to classify different grades of beef and to predict the number of the microbial population in beef samples. Several methods are used to deal with these classification and regression problems. Especially in multiclass beef classification and regression, signals contaminated with noise can significantly degrade the performance of the pattern recognition module. Therefore, the presence of internal and external noise in e-nose signals is a major challenge in beef quality monitoring. In this study, a noise filtering framework based on a fine-tuned discrete wavelet transform (DWT) was developed to handle noisy signals generated by an e-nose sensor array. To the best of our knowledge this is the first time the problem of e-nose signal noise in beef quality classification is tackled. The proposed framework was integrated and tested on several machine learning algorithms that were used in previous studies, i.e. k-nearest neighbor (k-NN), support vector machine (SVM), quadratic discriminant analysis (QDA), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS). Furthermore, the effect of noise filtering was investigated in the classification with two, three, and four classes of beef. The effect of noise filtering was also observed in regression tasks to predict the size of microbial population in beef samples. The experimental results showed that the proposed framework provides a significant improvement in multiclass classification and regression tasks.

[1]  Martin Sommer,et al.  On the Temporal Stability of Analyte Recognition with an E-Nose Based on a Metal Oxide Sensor Array in Practical Applications , 2018, Sensors.

[2]  Riyanarto Sarno,et al.  Development of mobile electronic nose for beef quality monitoring , 2017 .

[3]  Tomasz Dymerski,et al.  Electronic noses: Powerful tools in meat quality assessment. , 2017, Meat science.

[4]  Simon X. Yang,et al.  Circuit and Noise Analysis of Odorant Gas Sensors in an E-Nose , 2005, Sensors (Basel, Switzerland).

[5]  Riyanarto Sarno,et al.  Gas concentration analysis of resistive gas sensor array , 2016, 2016 International Symposium on Electronics and Smart Devices (ISESD).

[6]  Ruqiang Yan,et al.  Wavelets: Theory and Applications for Manufacturing , 2010 .

[7]  George-John E. Nychas,et al.  Contribution of Fourier transform infrared (FTIR) spectroscopy data on the quantitative determination of minced pork meat spoilage , 2011 .

[8]  Zhenxing Li,et al.  An overview of smart packaging technologies for monitoring safety and quality of meat and meat products , 2018, Packaging Technology and Science.

[9]  Vassilis S. Kodogiannis,et al.  An intelligent based decision support system for the detection of meat spoilage , 2014, Eng. Appl. Artif. Intell..

[10]  Vassilis S. Kodogiannis,et al.  Identification of meat spoilage by FTIR spectroscopy and neural networks , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[11]  J. Bruinsma,et al.  World agriculture towards 2030/2050: the 2012 revision , 2012 .

[12]  J Baranyi,et al.  Validating and comparing predictive models. , 1999, International journal of food microbiology.

[13]  Manuel Pineda-Sánchez,et al.  SENose: An under U$50 electronic nose for the monitoring of soil gas emissions , 2017, Comput. Electron. Agric..

[14]  Riyanarto Sarno,et al.  Detection of diabetes from gas analysis of human breath using e-Nose , 2017, 2017 11th International Conference on Information & Communication Technology and System (ICTS).

[15]  Riyanarto Sarno,et al.  Mobile Electronic Nose Architecture for Beef Quality Detection Based on Internet of Things Technology , 2015 .

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

[17]  Albert Fornells,et al.  A study of the effect of different types of noise on the precision of supervised learning techniques , 2010, Artificial Intelligence Review.

[18]  George-John E. Nychas,et al.  Ensemble-based support vector machine classifiers as an efficient tool for quality assessment of beef fillets from electronic nose data , 2016 .

[19]  George-John E. Nychas,et al.  Rapid qualitative and quantitative detection of beef fillets spoilage based on Fourier transform infrared spectroscopy data and artificial neural networks , 2010 .

[20]  Riyanarto Sarno,et al.  Sensor Array Optimization for Mobile Electronic Nose: Wavelet Transform and Filter Based Feature Selection Approach , 2016 .

[21]  Shuzhi Sam Ge,et al.  Drift Compensation for Electronic Nose by Semi-Supervised Domain Adaption , 2014, IEEE Sensors Journal.

[22]  Guruh Fajar Shidik,et al.  Classification of Music Moods Based on CNN , 2018 .

[23]  Royston Goodacre,et al.  Rapid and quantitative detection of the microbial spoilage of muscle foods: current status and future trends. , 2001 .

[24]  Suranjan Panigrahi,et al.  SPOILAGE IDENTIFICATION OF BEEF USING AN ELECTRONIC NOSE SYSTEM , 2004 .

[25]  Bartosz Krawczyk,et al.  On the Influence of Class Noise in Medical Data Classification: Treatment Using Noise Filtering Methods , 2016, Appl. Artif. Intell..

[26]  Da-Wen Sun,et al.  Meat Quality Evaluation by Hyperspectral Imaging Technique: An Overview , 2012, Critical reviews in food science and nutrition.

[27]  Xingquan Zhu,et al.  Class Noise vs. Attribute Noise: A Quantitative Study , 2003, Artificial Intelligence Review.

[28]  John N. Lygouras,et al.  An Intelligent Based Decision Support System for the Detection of Meat Spoilage , 2014, IEEE Conf. on Intelligent Systems.

[29]  Francisco Herrera,et al.  Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition , 2012, Knowledge and Information Systems.

[30]  Riyanarto Sarno,et al.  Electronic nose dataset for beef quality monitoring in uncontrolled ambient conditions , 2018, Data in brief.

[31]  G. Nychas,et al.  Meat spoilage during distribution. , 2008, Meat science.

[32]  Fan Li,et al.  Gas Recognition under Sensor Drift by Using Deep Learning , 2015, Int. J. Intell. Syst..

[33]  Vassilis Kodogiannis,et al.  Application of an Electronic Nose Coupled with Fuzzy-Wavelet Network for the Detection of Meat Spoilage , 2017, Food and Bioprocess Technology.

[34]  E. Schaller,et al.  ‘Electronic Noses’ and Their Application to Food , 1998 .

[35]  S. Balasubramanian,et al.  Neural networks-integrated metal oxide-based artificial olfactory system for meat spoilage identification , 2009 .

[36]  George-John E. Nychas,et al.  Sensory and microbiological quality assessment of beef fillets using a portable electronic nose in tandem with support vector machine analysis , 2013 .

[37]  Manuela Baietto,et al.  Evaluation of a portable MOS electronic nose to detect root rots in shade tree species , 2013 .

[38]  Jun Wang,et al.  Discrimination among tea plants either with different invasive severities or different invasive times using MOS electronic nose combined with a new feature extraction method , 2017, Comput. Electron. Agric..

[39]  Duoqian Miao,et al.  Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection , 2011, Expert Syst. Appl..

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

[41]  Ayten Atasoy,et al.  Study of fish species discrimination via electronic nose , 2015, Comput. Electron. Agric..

[42]  Jieping Xu,et al.  MUSIC MOOD CLASSIFICATION BASED ON VOTING , 2009 .

[43]  J Baranyi,et al.  A dynamic approach to predicting bacterial growth in food. , 1994, International journal of food microbiology.

[44]  José M. Barat,et al.  Monitorization of Atlantic salmon (Salmo salar) spoilage using an optoelectronic nose , 2014 .

[45]  Hyung Seok Kim,et al.  Meat and Fish Freshness Inspection System Based on Odor Sensing , 2012, Sensors.

[46]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[47]  Suranjan Panigrahi,et al.  Design and development of a metal oxide based electronic nose for spoilage classification of beef , 2006 .

[48]  Riyanarto Sarno,et al.  Classification of Music Mood Using MPEG-7 Audio Features and SVM with Confidence Interval , 2018, Int. J. Artif. Intell. Tools.

[49]  Riyanarto Sarno,et al.  Electronic nose for classifying beef and pork using Naïve Bayes , 2017, 2017 International Seminar on Sensors, Instrumentation, Measurement and Metrology (ISSIMM).

[50]  Sundar Balasubramanian,et al.  Possible Application of Electronic Nose Systems for Meat Safety: An Overview , 2016 .

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

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

[53]  D. Wijaya,et al.  Information Quality Ratio as a novel metric for mother wavelet selection , 2017 .

[54]  E. Llobet,et al.  Electronic Nose Based on Metal Oxide Semiconductor Sensors as an Alternative Technique for the Spoilage Classification of Red Meat , 2008, Sensors.

[55]  Vassilis S. Kodogiannis,et al.  Neuro-fuzzy based identification of meat spoilage using an electronic nose , 2016, 2016 IEEE 8th International Conference on Intelligent Systems (IS).

[56]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[57]  Vassilis S. Kodogiannis,et al.  An adaptive neuro-fuzzy identification model for the detection of meat spoilage , 2014, Appl. Soft Comput..

[58]  Teresa Bernarda Ludermir,et al.  Wavelet Filter for Noise Reduction and Signal Compression in an Artificial Nose , 2003, HIS.

[59]  Daniela M. Witten,et al.  An Introduction to Statistical Learning: with Applications in R , 2013 .