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

Computers and Electronics in Agriculture - Tập 157 - Trang 305-321 - 2019
Dedy Rahman Wijaya1,2, Riyanarto Sarno1, Enny Zulaika3
1Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
2School of Applied Science, Telkom University, Bandung, Indonesia
3Department of Biology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

Tài liệu tham khảo

Ahmed, 2018, An overview of smart packaging technologies for monitoring safety and quality of meat and meat products, Packaging Technol. Sci., 31, 449, 10.1002/pts.2380 Alexandratos, N., Bruinsma, J., 2012. WORLD AGRICULTURE TOWARDS 2030/2050 The 2012 Revision. Rome. Argyri, 2010, Rapid qualitative and quantitative detection of beef fillets spoilage based on Fourier transform infrared spectroscopy data and artificial neural networks, Sens. Actuat. B, 145, 146, 10.1016/j.snb.2009.11.052 Baietto, 2013, Evaluation of a portable MOS electronic nose to detect root rots in shade tree species, Comput. Electron. Agric., 96, 117, 10.1016/j.compag.2013.05.002 Balasubramanian, 2016, Possible application of electronic nose systems for meat safety: an overview, 59 Balasubramanian, 2004, Spoilage identification of beef using an electronic nose system, Transactions of the ASAE, 47, 1625, 10.13031/2013.17593 Balasubramanian, 2009, Neural networks-integrated metal oxide-based artificial olfactory system for meat spoilage identification, J. Food Eng., 91, 91, 10.1016/j.jfoodeng.2008.08.008 Baranyi, 1999, Validating and comparing predictive models, Int. J. Food Microbiol., 48, 159, 10.1016/S0168-1605(99)00035-5 Baranyi, 1994, A dynamic approach to predicting bacterial growth in food, Int. J. Food Microbiol., 23, 277, 10.1016/0168-1605(94)90157-0 Chang, 2011, LIBSVM: a library for support vector machines, ACM Trans. Intell. Syst. Technol., 2, 1, 10.1145/1961189.1961199 CSIRO Food and Nutritional Sciences, 2003. Vacuum-packed meat : storage life and spoilage. Deeplearning4j Development Team, 2017. Deeplearning4j: Open-source distributed deep learning for the JVM, Apache Software Foundation License 2.0. El Barbri, 2008, Electronic nose based on metal oxide semiconductor sensors as an alternative technique for the spoilage classification of red meat, Sensors, 8, 142, 10.3390/s8010142 Ellis, 2001, Rapid and quantitative detection of the microbial spoilage of muscle foods: current status and future trends, Trends Food Sci. Technol., 12, 414, 10.1016/S0924-2244(02)00019-5 Elmasry, 2012, Meat quality evaluation by hyperspectral imaging technique: an overview, Crit. Rev. Food Sci. Nutr., 52, 689, 10.1080/10408398.2010.507908 Gao, 2011 Güney, 2015, Study of fish species discrimination via electronic nose, Comput. Electron. Agric., 119, 83, 10.1016/j.compag.2015.10.005 Hariyanto, 2017, Detection of diabetes from gas analysis of human breath using e-Nose, 241 Harley, 2002, Microbiology James, 2013, An Introduction to Statistical Learning with Applications in R, 10.1007/978-1-4614-7138-7 Kiselev, 2018, On the temporal stability of analyte recognition with an e-nose based on a metal oxide sensor array in practical applications, Sensors (Switzerland), 18, 10.3390/s18020550 Kodogiannis, 2017, Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage, Food Bioprocess Technol., 10.1007/s11947-016-1851-6 Kodogiannis, 2016, Neuro-fuzzy based identification of meat spoilage using an electronic nose, 710 Kodogiannis, 2014, An adaptive neuro-fuzzy identification model for the detection of meat spoilage, Appl. Soft Comput. J., 23, 483, 10.1016/j.asoc.2014.06.009 Kodogiannis, 2014, Identification of meat spoilage by FTIR spectroscopy and neural networks, 1644 Kodogiannis, 2015, An intelligent based decision support system for the detection of meat spoilage, Eng. Appl. Artif. Intell., 323, 303 Liu, 2015, Gas recognition under sensor drift by using deep learning, Int. J. Intell. Syst., 30, 907, 10.1002/int.21731 Liu, 2014, Drift compensation for electronic nose by semi-supervised domain adaption, IEEE Sens. J., 14, 657, 10.1109/JSEN.2013.2285919 Mohareb, 2016, Ensemble-based support vector machine classifiers as an efficient tool for quality assessment of beef fillets from electronic nose data, Anal. Methods, 8, 3711, 10.1039/C6AY00147E Najam ul Hasan, 2012, Meat and fish freshness inspection system based on odor sensing, Sensors (Switzerland), 12, 15542, 10.3390/s121115542 Nettleton, 2010, A study of the effect of different types of noise on the precision of supervised learning techniques, Artif. Intell. Rev., 33, 275, 10.1007/s10462-010-9156-z Nychas, 2008, Meat spoilage during distribution, Meat Sci., 78, 77, 10.1016/j.meatsci.2007.06.020 Panigrahi, 2006, Design and development of a metal oxide based electronic nose for spoilage classification of beef, Sens. Actuat. B, 119, 2, 10.1016/j.snb.2005.03.120 Papadopoulou, 2011, Contribution of Fourier transform infrared (FTIR) spectroscopy data on the quantitative determination of minced pork meat spoilage, Food Res. Int., 44, 3264, 10.1016/j.foodres.2011.09.012 Papadopoulou, 2013, Sensory and microbiological quality assessment of beef fillets using a portable electronic nose in tandem with support vector machine analysis, Food Res. Int., 50, 241, 10.1016/j.foodres.2012.10.020 Pineda, 2017, SENose: An under U$50 electronic nose for the monitoring of soil gas emissions, Comput. Electron. Agric., 133, 15, 10.1016/j.compag.2016.12.004 PrimeSafe, 2002. Microbiological testing for process monitoring in the meat industry. Purslow, 2017, Chapter 1 - Introduction, 1 Ridoean, J.A., Sarno, R., Sunaryo, D., Wijaya, D.R., 2018. Music mood classification using audio power and audio harmonicity based on MPEG-7 audio features and Support Vector Machine, in: Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017. IEEE, Bandung. doi:10.1109/ICSITech.2017.8257088. Sáez, 2014, Analyzing the presence of noise in multi-class problems: alleviating its influence with the one-vs-one decomposition, Knowl. Inf. Syst., 38, 179, 10.1007/s10115-012-0570-1 Sáez, 2016, On the influence of class noise in medical data classification : treatment using noise filtering methods on the influence of class noise in medical data classification : treatment using noise filtering methods, Appl. Artificial Intelligence, 9514 Sans, P., Combris, P., 2015. World meat consumption patterns : An overview of the last fifty years (1961 – 2011). Sarno, 2018, Classification of music mood using MPEG-7 audio features and SVM with confidence interval, Int. J. Artif. Intell. Tools, 27, 10.1142/S0218213018500161 Schaller, 1998, “Electronic Noses” and their application to food, Lebensmittel-Wissenschaft und-Technologie, 31, 305, 10.1006/fstl.1998.0376 Sun, 2017, Discrimination among tea plants either with different invasive severities or different invasive times using MOS electronic nose combined with a new feature extraction method, Comput. Electron. Agric., 143, 293, 10.1016/j.compag.2017.11.007 Tian, 2005, Circuit and noise analysis of odorant gas sensors in an E-nose, Sensors, 5, 85, 10.3390/s5010085 Vergara, 2012, Chemical gas sensor drift compensation using classifier ensembles, Sens. Actuat. B, 166–167, 320, 10.1016/j.snb.2012.01.074 Wang, 2011, Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection, Expert Syst. Appl., 38, 14314 Wijaya, 2015, 655 Wijaya, D.R., Sarno, R., Daiva, A.F., 2017a. Electronic Nose for Classifying Beef and Pork using Naïve Bayes. International Seminar on Sensor, Instrumentation, Measurement and Metrology (ISSIMM) Surabaya. Wijaya, 2018, Electronic nose dataset for beef quality monitoring in uncontrolled ambient conditions, Data in Brief, 10.1016/j.dib.2018.11.091 Wijaya, 2016, Sensor array optimization for mobile electronic nose: wavelet transform and filter based feature selection approach, International Review Comput. Software, 11, 659 Wijaya, 2016, Gas concentration analysis of resistive gas sensor array, 337 Wijaya, 2016, Information quality ratio as a novel metric for mother wavelet selection, Chemometrics Intelligent Laboratory Syst., 160, 59, 10.1016/j.chemolab.2016.11.012 Wijaya, 2017, Development of mobile electronic nose for beef quality monitoring, 728 Wijaya, 2017, Development of mobile electronic nose for beef quality monitoring, Procedia Comput. Sci., 124211, 728, 10.1016/j.procs.2017.12.211 Wojnowski, 2017, Electronic noses: powerful tools in meat quality assessment, Meat Sci., 131, 119, 10.1016/j.meatsci.2017.04.240 Yan, 2016, Calibration transfer and drift compensation of e-noses via coupled task learning, Sens. Actuat. B, 225, 288, 10.1016/j.snb.2015.11.058 Zanchettin, 2007, Wavelet filter for noise reduction and signal compression in an artificial nose, Appl. Soft Comput. J., 7, 246, 10.1016/j.asoc.2005.06.004 Zaragozá, 2014, Monitorization of Atlantic salmon (Salmo salar) spoilage using an optoelectronic nose, Sens. Actuat. B, 195, 478, 10.1016/j.snb.2014.01.017 Zhang, 2017, Anti-drift in E-nose: a subspace projection approach with drift reduction, Sens. Actuat. B, 253, 407, 10.1016/j.snb.2017.06.156 Zhang, 2015, Domain adaptation extreme learning machines for drift compensation in E-nose systems, IEEE Trans. Instrum. Meas., 64, 1790, 10.1109/TIM.2014.2367775 Zhu, 2004, Class noise vs. attribute noise: a quantitative study, Artif. Intell. Rev., 22, 177, 10.1007/s10462-004-0751-8