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Journal of Analytical Methods in Chemistry
Volume 2019, Article ID 4372395, 14 pages
Research Article

Simultaneous Recognition of Species, Quality Grades, and Multivariate Calibration of Antioxidant Activities for 12 Famous Green Teas Using Mid- and Near-Infrared Spectroscopy Coupled with Chemometrics

1The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, China
2College of Material and Chemical Engineering, Tongren University, Tongren 554300, Guizhou, China
3State Key Laboratory of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
4Technology Center, China Tobacco Guizhou Industrial Co., Ltd., Guiyang 550009, Guizhou, China

Correspondence should be addressed to Lu Xu; moc.361@omehcxl and Yuanbin She; nc.ude.tujz@byehs

Received 30 May 2018; Revised 6 September 2018; Accepted 27 September 2018; Published 2 January 2019

Guest Editor: Larisa Lvova

Copyright © 2019 Haiyan Fu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In this paper, mid- and near-infrared spectroscopy fingerprints were combined to simultaneously discriminate 12 famous green teas and quantitatively characterize their antioxidant activities using chemometrics. A supervised pattern recognition method based on partial least square discriminant analysis (PLSDA) was adopted to classify the 12 famous green teas with different species and quality grades, and then optimized sample-weighted least-squares support vector machine (OSWLS-SVM) based on particle swarm optimization was employed to investigate the quantitative relationship between their antioxidant activities and the spectral fingerprints. As a result, 12 famous green teas can be discriminated with a recognition rate of 100% by MIR or NIR data. However, compared with individual instrumental data, data fusion was more adequate for modeling the antioxidant activities of samples with RMSEP of 0.0065. Finally, the performance of the proposed method was evaluated and validated by some statistical parameters and the elliptical joint confidence region (EJCR) test. The results indicate that fusion of mid- and near-infrared spectroscopy suggests a new avenue to discriminate the species and grades of green teas. Moreover, the proposed method also implies other promising applications with more accurate multivariate calibration of antioxidant activities.