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

摘要

Partial least squares (PLS) is widely used in multivariate statistical analysis, but linear and nonlinear model variable selections are based on the selection of principal components. It does not involve the interactions of variables and predictors, which may adversely affect prediction accuracy. In this study, 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 PLS optimization method that integrates path analysis (PLS-PA). To verify the validity of PLS-PA, we use the measured coefficients and residuals as evaluation indicators. We test 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 the traditional PLS and PLS-PA methods 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.