Partial Least Squares–regression ( PLS–regression ) In Chemometricsنخستین کنفرانس سراسری دستاوردهای نوین در شیمی و مهندسی شیمی
Partial least squares regression (PLS– regression) is a statistical method. instead of finding hyperplanes of minimum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space regression is effectively used in process modeling and monitoring to deal with a large number of variables with collinearity. usage in PLS regression and cross–validation. As a natural extension, the recursive algorithm is extended to dynamic modeling and nonlinear modeling. The analysis of mixture data is a common problem in industrial research and development, particularly in chemical and related industries, e.g. pharmaceuticals, cosmetics, oil, and biotechnology. limitation of multiple regression with data in constrained regions. For the analysis of mixture data, partial least squares (PLS) has been found to be practical. In particular when both mixture and process variables are involved, it offers a flexible and simple approach which works well in practice<\div>
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