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標題: 2-DE combined with two-layer feature selection accurately establishes the origin of oolong tea
作者: Chien, Han-Ju
Chu, Yen-Wei
Chen, Chi-Wei
Juang, Yu-Min
Chien, Min-Wei
Liu, Chih-Wei
Wu, Chia-Chang
Tzen, Jason T C
Lai, Chien-Chen
關鍵字: Feature selection;Gel-based proteomics;LC–MS/MS;Machine learning;Oolong tea;Support vector machine;Electrophoresis, Gel, Two-Dimensional;Plant Leaves;Proteomics;Taiwan;Tandem Mass Spectrometry;Tea
Project: Food chemistry, Volume 211, Page(s) 392-9.
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.
ISSN: 0308-8146
DOI: 10.1016/j.foodchem.2016.05.043
Appears in Collections:基因體暨生物資訊學研究所

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