Chemometric methods application in pharmaceutical products and processes analysis and control - Computer-Aided Applications in Pharmaceutical Technology - page 58

4.1 Introduction

Chemometrics is a scientifi c discipline where chemistry and
pharmaceutical science meet statistics and software (Massart and
Buydens, 1988). The term 'chemometrics' was coined several decades
ago to describe a new way of analyzing chemical data, in which elements
of both statistical and chemical thinking are combined (Martens and
Naes, 1996). In chemometric techniques, multivariate empirical modeling
methods are applied to chemical data (Miller, 1999). Many defi nitions of
chemometrics and chemometric methods are available.
Chemometric techniques, both multivariate data analysis and design of
experiments (DoE), have a central role in the process analytical technology
(PAT) initiative (Rajalahti and Kvalheim, 2011). The power of
chemometrics is that it can be used to model systems that are both largely
unknown and complex (Miller, 2005).
Development and availability of modern, computationally powerful
software tools has led to a signifi cant increase in chemometrics application
in pharmaceutical sciences and industry. Multivariate data analysis has
proven to be a profi cient tool when combined with advanced
characterization techniques (Rajalahti and Kvalheim, 2011).

4.2 Theory

Chemometric tools are methods designed to establish relationships between
different measurements from a chemical system, or process with the state
of the system, through the application of mathematical or statistical
methods (Lopes et al., 2004). Chemometrics are, in the fi eld of
pharmaceutical technology, usually associated with vibrational
spectroscopy techniques, such as infrared (IR), near infrared (NIR), and
Raman imaging techniques, etc. Vibrational spectroscopy techniques are
suitable for the analysis of solid, liquid, and biotechnological pharmaceutical
dosage forms. They can be implemented during pharmaceutical
development, in production for process monitoring, or in quality control
laboratories (Roggo et al., 2007). These techniques produce data with high
dimensionality, since each sample is described with hundreds or even
thousands of variables. Multivariate analysis provides tools for effective
process monitoring and control, enabling detection of multivariate
relationships between different variables such as raw materials, process
conditions, and end products (Rajalahti and Kvalheim, 2011).

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