Research On Several Key Technologies Of Partial Least Squares Regression In Chemistry And Chemical Process Modeling

Partial least squares regression (PLSR), as a multivariate calibration method based on factor analysis, had been widely used in various kinds of fields, such as chemistry, chemical engineering, economy, environment, foodstuff, education psychology and etc. Now in this paper, to the questions of chemistry and chemical engineering data the time-series, nonlinearity, outliers and multivariate responses characteristics, we induced and constructed some novel PLSR algorithms and then applied them to solve the medicine molecule quantitative structure-activity relationship (QSAR) and chemical engineering process modeling respectively. The main work was as follow:1.The history, progress and application of PLSR had been first reviewed. Subsequently, we expatiated PLSR method the theory and its main property. In order to assess the performance and compare with some other methods, many kinds of its interrelated analysis technique were also introduced.2.A soft sensor model of a chemical process was established by PLSR method based on its time series data, and the model could be adjusted in the block-wise recursive way in the presence of new sample data. With a view to the data of time series characteristics, a strategy of allotting different weight coefficients to the time series data was introduced, and an approach of how to ascertain the weight coefficients was provided in the meantime. Subsequently, the weighted block-wise recursive partial least squares regression algorithm was developed and used to model the water content of solvent dehydration tower bottom drainage in a commerce purified terephthalic acid (PTA). The experimental result showed the algorithm was rapid and effective.3. A new nonlinear PLSR algorithm that embedded the adaptive fuzzy logic system into the regression framework of the PLSR method was proposed. The resulting model used Takagi-Sugeno fuzzy model to capture the nonlinearity andkept the projection to attain robust generalization property. At the same time, an adaptive learning algorithm for the fuzzy model was constructed to reduce the number of fuzzy rules. Subsequently, to increase partial least squares components interpretative capability, the error-based weights updating procedure was deduced and implemented in the fuzzy PLSR framework. Finally, application to the HIV-1 protease inhibitors QSAR modeling of the proposed fuzzy PLSR method was presented with comparison to some other methods. The Fuzzy PLSR method and its error-based method not only held on fine learning ability but also improved the model prediction performance and steady capability.4. In order to eliminate abnormal observations in the data set negative impact on the accuracy and reliability of the PLSR model, a new robust version of the simple partial least squares (SIMPLS) algorithm was constructed from a robust covariance matrix for. high-dimensional data and robust linear regression by embedding a simple multivariate outlier-detection procedure and a robust estimator into the SIMPLS regression framework. The multivariate outlier-detection procedure was based on the use of information obtained from projections onto the directions that maximize and minimize the kurtosis coefficient of the projected data. The proposed kurtosis-SIMPLS method application to the analysis of fish data near infrared spectroscopy was presented with comparison to the SIMPLS. The results showed that kurtosis-SIMPLS method not only found out the very outliers from the data set with less computational cost, but also held on better prediction performance and steady capability for the normal samples.5.In order to eliminate the correlation between predictor variables and make full use of information between the correlated responses in multi-input multi-output chemical process, a new partial least squares regression method called PLSR with mini-max estimator (PLS-Minimax) was introduced. It first implemented the PLSR algorithm with multiple responses to samples data to eliminate the predictor variables correlation and thus built a robust model. Then under the mini-max rule, a shrinkage matrix was calculated based on thecovariance matrix of the multiple responses errors between the PLS estimators and the responses to improve the model predictive precision by modifying the regression coefficient matrix. When the PLS-Minimax method was used to model a polymerization reaction process with four responses. The results showed that PLS-Minimax method not only gained a considerable improvement on predictive accuracy, but also held on high cross-validation correlative coefficient of the multiple responses.