Recent development in electronic nose data processing for beef quality assessment

Beef is kind of perishable food that easily to decay. Hence, a rapid system for beef quality assessment is needed to guarantee the quality of beef. In the last few years, electronic nose (e-nose) is developed for beef spoilage detection. In this paper, we discuss the challenges of e-nose application to beef quality assessment, especially in e-nose data processing. We also provide a summary of our previous studies that explains several methods to deal with gas sensor noise, sensor array optimization problem, beef quality classification, and prediction of the microbial population in beef sample. This paper might be useful for researchers and practitioners to understand the challenges and methods of e-nose data processing for beef quality assessment.

[1]  C. Distante,et al.  On the study of feature extraction methods for an electronic nose , 2002 .

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

[3]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

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

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

[6]  Jun Yan,et al.  Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning , 2012, IEEE Transactions on Learning Technologies.

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

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

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

[10]  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).

[11]  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.

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

[13]  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 .

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

[16]  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 .

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

[18]  Matteo Falasconi,et al.  Electronic Nose for Microbiological Quality Control of Food Products , 2012 .

[19]  Vassilis S. Kodogiannis,et al.  A fuzzy-wavelet neural network model for the detection of meat spoilage using an electronic nose , 2016, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

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

[21]  Himanshu K. Patel,et al.  The Electronic Nose: Artificial Olfaction Technology , 2013 .

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

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

[24]  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.

[25]  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).

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

[27]  Riyanarto Sarno,et al.  Music mood classification using audio power and audio harmonicity based on MPEG-7 audio features and Support Vector Machine , 2017, 2017 3rd International Conference on Science in Information Technology (ICSITech).

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

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

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

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

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

[33]  P. Sans,et al.  World meat consumption patterns: An overview of the last fifty years (1961-2011). , 2015, Meat science.

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

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

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

[37]  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.

[38]  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 .