Detection of black tea fermentation quality based on optimized deep neural network and hyperspectral imaging

Abstract

The quality of black tea significantly relies on its fermentation process. Nevertheless, achieving precise and objective evaluations remains challenging due to the subjective nature of manual judgment involved in quality monitoring. To address this problem, hyperspectral imaging combined with the deep learning algorithms are proposed to identify the fermentation quality of black tea. Firstly, the hyperspectral data of Yinghong No. 9 black tea during five fermentation time intervals within 0–5 h are collected. Then, the Support Vector Machine (SVM), Artificial Neural Network (ANN), Partial Least Squares Discriminant Analysis (PLS-DA), and Naive Bayesian (NB) are used to construct black tea fermentation quality detection models based on full spectrum and selected spectrum data. Furthermore, deep learning algorithms including the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and Swarm Optimization (PSO) optimized CNN-LSTM (PSO-CNN-LSTM) are also used to build the detection model using the spectral images. The experimental results indicate that deep learning algorithms have obvious advantages over traditional machine learning algorithms in tea fermentation quality detection. Besides, the PSO-CNN-LSTM model shows the best classification performance compared to other algorithms and achieves an accuracy of 96.78% on the test set. This study demonstrates the significant potential of combining deep learning with hyperspectral imaging for predicting black tea fermentation quality. This provides a new approach for effective monitoring of the black tea fermentation process and a useful reference for other applications in similar fields.