Notice of retraction
Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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S&M2351 Research Paper of Special Issue

Partial Least Squares Optimization Method and Path Analysis Integration for Chinese Medicine Data

Tianci Li, Wangping Xiong, Jianqiang Du, Bin Nie, Jigen Luo, Yanyun Yang, and Chih-Cheng Chen

(Received April 22, 2020; Accepted August 18, 2020)

Keywords: Chinese medicine dosage, partial least squares (PLS), path analysis, variable selection

Partial least squares (PLS) is widely used in multivariate statistical analysis, but linear and nonlinear model variable selection is based on the selection of principal components. It does not involve the interaction of variables and the interaction of predictors, which may adversely affect prediction accuracy. In this article, we design a tailor built temperature control system to monitor and control temperature settings during experiments on traditional Chinese medicine (TCM). We combine results from path analysis and the variables’ covariance and correlation matrix, and propose a partial least squares optimization method that integrates path analysis (PLS-PA). To verify the validity of PLS-PA, we use measured coefficients and residuals as evaluation indicators. We tested the performance of PLS-PA using two TCM dose datasets and one dataset from the University of California, Irvine (UCI). The three experimental results demonstrate that the measured coefficients from traditional PLS and the PLS-PA method increase by 11.8%, 4.7%, and 8.5%, which suggest the validity of our experiment. We conclude that PLS-PA can optimize the screening of variables and improve the PLS regression analysis of TCM experimental data without hampering model accuracy.

Corresponding author: Wangping Xiong, Chih-Cheng Chen

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