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

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 spectra...

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Autores principales:	Yan Hu, Lijia Xu, Peng Huang, Xiong Luo, Peng Wang, Zhiliang Kang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fluorescence hyperspectral oolong tea preprocessing visual display feature selection classification model Agriculture (General) S1-972
Acceso en línea:	https://doaj.org/article/f3afd197b3434d08a8778380b1c1110e
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spelling	oai:doaj.org-article:f3afd197b3434d08a8778380b1c1110e2021-11-25T15:59:13ZReliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning10.3390/agriculture111111062077-0472https://doaj.org/article/f3afd197b3434d08a8778380b1c1110e2021-11-01T00:00:00Zhttps://www.mdpi.com/2077-0472/11/11/1106https://doaj.org/toc/2077-0472A 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.Yan HuLijia XuPeng HuangXiong LuoPeng WangZhiliang KangMDPI AGarticlefluorescence hyperspectraloolong teapreprocessingvisual displayfeature selectionclassification modelAgriculture (General)S1-972ENAgriculture, Vol 11, Iss 1106, p 1106 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fluorescence hyperspectral oolong tea preprocessing visual display feature selection classification model Agriculture (General) S1-972
spellingShingle	fluorescence hyperspectral oolong tea preprocessing visual display feature selection classification model Agriculture (General) S1-972 Yan Hu Lijia Xu Peng Huang Xiong Luo Peng Wang Zhiliang Kang Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning
description	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.
format	article
author	Yan Hu Lijia Xu Peng Huang Xiong Luo Peng Wang Zhiliang Kang
author_facet	Yan Hu Lijia Xu Peng Huang Xiong Luo Peng Wang Zhiliang Kang
author_sort	Yan Hu
title	Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning
title_short	Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning
title_full	Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning
title_fullStr	Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning
title_full_unstemmed	Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning
title_sort	reliable identification of oolong tea species: nondestructive testing classification based on fluorescence hyperspectral technology and machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f3afd197b3434d08a8778380b1c1110e
work_keys_str_mv	AT yanhu reliableidentificationofoolongteaspeciesnondestructivetestingclassificationbasedonfluorescencehyperspectraltechnologyandmachinelearning AT lijiaxu reliableidentificationofoolongteaspeciesnondestructivetestingclassificationbasedonfluorescencehyperspectraltechnologyandmachinelearning AT penghuang reliableidentificationofoolongteaspeciesnondestructivetestingclassificationbasedonfluorescencehyperspectraltechnologyandmachinelearning AT xiongluo reliableidentificationofoolongteaspeciesnondestructivetestingclassificationbasedonfluorescencehyperspectraltechnologyandmachinelearning AT pengwang reliableidentificationofoolongteaspeciesnondestructivetestingclassificationbasedonfluorescencehyperspectraltechnologyandmachinelearning AT zhiliangkang reliableidentificationofoolongteaspeciesnondestructivetestingclassificationbasedonfluorescencehyperspectraltechnologyandmachinelearning
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Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

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