2-DE combined with two-layer feature selection accurately establishes the origin of oolong tea

被引:10
|
作者
Chien, Han-Ju [1 ]
Chu, Yen-Wei [2 ]
Chen, Chi-Wei [2 ]
Juang, Yu-Min [1 ]
Chien, Min-Wei [1 ]
Liu, Chih-Wei [1 ]
Wu, Chia-Chang [3 ]
Tzen, Jason T. C. [4 ]
Lai, Chien-Chen [1 ,5 ]
机构
[1] Natl Chung Hsing Univ, Inst Mol Biol, 145 Xingda Rd, Taichung 40227, Taiwan
[2] Natl Chung Hsing Univ, Inst Genom & Bioinformat, Taichung 40227, Taiwan
[3] Tea Res & Extens Stn, Taoyuan 32655, Taiwan
[4] Natl Chung Hsing Univ, Grad Inst Biotechnol, Taichung 40227, Taiwan
[5] China Med Univ, Grad Inst Chinese Med Sci, Taichung 40447, Taiwan
关键词
Oolong tea; Gel-based proteomics; LC-MS/MS; Support vector machine; Machine learning; Feature selection; PROTEIN PHOSPHATASE 2C; PROTEOMIC ANALYSIS; OXIDATIVE STRESS; GLUTAMINE-SYNTHETASE; TOLERANCE; BIOSYNTHESIS; RUBISCO; GENES; RICE;
D O I
10.1016/j.foodchem.2016.05.043
中图分类号
O69 [应用化学];
学科分类号
081704 ;
摘要
Taiwan is known for its high quality oolong tea. Because of high consumer demand, some tea manufactures mix lower quality leaves with genuine Taiwan oolong tea in order to increase profits. Robust scientific methods are, therefore, needed to verify the origin and quality of tea leaves. In this study, we investigated whether two-dimensional gel electrophoresis (2-DE) and nanoscale liquid chromatography/ tandem mass spectroscopy (nano-LC/MS/MS) coupled with a two-layer feature selection mechanism comprising information gain attribute evaluation (IGAE) and support vector machine feature selection (SVM-FS) are useful in identifying characteristic proteins that can be used as markers of the original source of oolong tea. Samples in this study included oolong tea leaves from 23 different sources. We found that our method had an accuracy of 95.5% in correctly identifying the origin of the leaves. Overall, our method is a novel approach for determining the origin of oolong tea leaves. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:392 / 399
页数:8
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