Abstract
This chapter provides a basic theoretical background on chemometrics and chemometric methods for the analysis of multivariate data. Multivariate data analysis is essential for both product and process development and optimization. Depending on the problem studied, classification and/or regression multivariate methods are applied for data analysis. Different supervised and unsupervised methods for classification and regression are presented, followed by examples of their application in pharmaceutical technology. Some of the methods described include principal component analysis, various supervised classification methods, multiple linear regression, principal component regression, partial least-squares regression, etc. The importance of chemometric tools within the smart manufacturing concept has been introduced.
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