Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

Yan Hu, Lijia Xu, Peng Huang, Xiong Luo, Peng Wang and Zhiliang Kang
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Yan Hu: College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China
Lijia Xu: College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China
Peng Huang: College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China
Xiong Luo: College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China
Peng Wang: College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China
Zhiliang Kang: College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China

Agriculture, 2021, vol. 11, issue 11, 1-19

Abstract: A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology and machine learning to distinguish Oolong tea by analyzing the spectral features of tea in the wavelength ranging from 475 to 1100 nm. The spectral data are preprocessed by multivariate scattering correction (MSC) and standard normal variable (SNV), which can effectively reduce the impact of baseline drift and tilt. Then principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are adopted for feature dimensionality reduction and visual display. Random Forest-Recursive Feature Elimination (RF-RFE) is used for feature selection. Decision Tree (DT), Random Forest Classification (RFC), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used to establish the classification model. The results show that MSC-RF-RFE-SVM is the best model for the classification of Oolong tea in which the accuracy of the training set and test set is 100% and 98.73%, respectively. It can be concluded that fluorescence hyperspectral technology and machine learning are feasible to classify Oolong tea.

Keywords: fluorescence hyperspectral; oolong tea; preprocessing; visual display; feature selection; classification model; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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