TeaNet: Deep learning on Near-Infrared Spectroscopy (NIR) data for the assurance of tea quality

Abstract

A high standard of tea quality control is extremely important for the sake of the health protection of consumers. With the fast-growing of Deep Learning, the intelligent and labor-free authentication of the tea quality, becomes a substantially demanded yet unexplored area, with two key problems yet to be solved: (1) sampling of tea data, with a fast and environmentally friendly approach, (2) distinguishment of tea, in terms of building an accurate tea classifier. To solve the above two problems, NIR spectroscopy is used to collect the tea NIR data in a fast, non-invasive, and environmentally friendly manner. And this paper proposed a series of brand new convolutional neural networks(CNNs) for NIR-based tea data, called TeaNet, TeaResnet, and TeaMobilenet. Due to the success of the different network architectures in the classification task, this paper applies the basic convolution block, the residual block, and the inverted residual block into the network architecture designs, corresponding to TeaNet, TeaResnet, and TeaMobilenet. To serve as the input of networks, NIR data is transformed into a pseudo image and then the model exploits the information in NIR data to make a classification. In an extensive experimental study, we compare TeaNet, TeaResnet, and TeaMobilenet with the traditional machine learning algorithms like Support Vector Machine (SVM) and Random Forest. The experimental results indicate that TeaNet, TeaResnet, and TeaMobilenet could break through the accuracy bottleneck of traditional machine learning algorithms and reach up to a 100% accuracy rate. At the same time, the comparative experiments of the four preprocessing methods-Mean Centering (MC), Savitzky-Golay smoothing (SG), Standard Normal Variate (SNV), and Normalization(Norm) indicate that there is a positive correlation between standard deviation and classification performance. SNV is the best preprocessing method for tea near-infrared spectroscopy data with the greatest standard deviation.