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

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.