期刊
SENSORS AND ACTUATORS B-CHEMICAL
卷 311, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.snb.2020.127924
关键词
Tea grade; e-tongue system; Sum of ranking difference algorithm; Partial least squares-discriminant analysis model
资金
- National Key R&D Program of China [2017YFD0400805]
- Wenzhou science and technology project [N20160004]
- Wenzhou Basic Public Welfare Project [N20190017]
- Zhejiang Provincial Education Department Projection [201635569]
Identifying tea grades is crucial to providing consumers with tea and ensuring consumer rights. Partial least squares-discriminant analysis (PLS-DA) is a simple and traditional classification algorithm in analyzing e-tongue data. However, the number of latent variables (LVs) in a PLS-DA model needs to be determined, and cross-validation is the most common way to identify the optimal latent variables. To overcome this obstacle, sum of ranking difference (SRD) algorithm was applied to create a non-parametric PLS-DA-SRD model. The performance of PLS-DA and PLS-DA-SRD models were then compared, and significant improvement in term of accuracy, sensitivity, and specificity was obtained when SRD was combined with PLS-DA algorithm. Moreover, no training phase was needed to identify the optimal LVs for PLS-DA, making the calculation of classification rapid and concise. The PLS-DA-SRD method demonstrated its efficiency and capability by successfully identifying the tea sample grade.
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