DWTLSTM for electronic nose signal processing in beef quality monitoring

[Display omitted] •DWTLSTM was proposed for e-nose signal processing in beef quality monitoring.•Various beef cuts were used to generate a larger dataset in this experiment.•The performance was investigated in classification and regression tasks.•DWTLSTM outperformed k-NN, LDA, SVM/SVR, MLP, and standard LSTM. The smart packaging system is needed to continuously monitor the quality of beef and microbial population for both the meat industries as well as end consumers. Moreover, several feasibility studies of electronic nose (e-nose) for rapid beef quality assessment are also conducted in recent years. The characteristics of e-nose are fast, cheap, and easy to use make it suitable and scalable for beef quality monitoring applications. It is also potential to be integrated with consumer electronics such as refrigerator and meat chiller. However, the inevitable challenge is how to handle time-series data that is contaminated with noise. In this paper, discrete wavelet transform and long short-term memory (DWTLSTM) is proposed to overcome the e-nose signal contaminated with noise in monitoring beef quality. In beef quality classification task, our proposed has a favorable performance with 94.83% of average accuracy and 85.05% of average F-measure. Moreover, it presents a satisfactory performance in the prediction of microbial population (RMSE = 0.0515 and R2 = 0.9712). These results indicate that the DWTLSTM outperforms conventional methods such as k-nearest neighbor (k-NN), linear discriminant analysis (LDA), support vector machine/support vector regression (SVM/SVR), multilayer perceptron (MLP), and even standard long-short term memory (LSTM).