A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms

• Published in: Scientific reports Vol. 12; no. 1; p. 21418
• Main Authors: Chang, Chenjie, Li, Zongyuan, Li, Hongyi, Hou, Zhuoya, Zuo, Enguang, Zhao, Deyi, Lv, Xiaoyi, Zhong, Furu, Chen, Cheng, Tian, Feng
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 10.12.2022; Nature Publishing Group UK; Nature Portfolio
• Subjects: Algorithms; China; Deep Learning; Learning algorithms; Neural networks; Neural Networks, Computer; Spectroscopy; Spectroscopy, Near-Infrared - methods; Standardization; China
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-25671-8

Maojian is one of China's traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in different regions for profit, seriously disrupting the healthy tea market. Due to the similar appearance of Maojian produced in different regions, it is impossible to make a quick and objective distinction. It often requires experienced experts to identify them through multiple steps. Therefore, it is of great significance to develop a rapid and accurate method to identify different regions of Maojian to promote the standardization of the Maojian market and the development of detection technology. In this study, we propose a new method based on Near infra-red (NIR) with deep learning algorithms to distinguish different origins of Maojian. In this experiment, the NIR spectral data of Maojian from different origins are combined with the back propagation neural network (BPNN), improved AlexNet, and improved RepSet models for classification. Among them, improved RepSet has the highest accuracy of 99.30%, which is 8.67% and 0.70% higher than BPNN and improved AlexNet, respectively. The overall results show that it is feasible to use NIR and deep learning methods to quickly and accurately identify Maojian from different origins and prove an effective alternative method to discriminate different origins of Maojian.