The field of pharmaceutical analysis calls for rapid, simple, specific and accurate methods of analysis that ensure the quality of the products. The determination of active contents of compounds in pharmaceutical preparations is often achieved by high-performance liquid chromatography (HPLC). On the other hand, multivariate calibration methods are being successfully applied to solve a wide variety of tasks/problems in different fields of analytical chemistry. Several of these methods have been presented in the literature in the last few years. Partial least squares (PLS) has become a routine practice in pharmaceutical analysis. However, there are certain problems that still exists which require more sophisticated models to be solved. We present several situations in which conventional multivariate models have been successfully applied to simultaneously analyze several active principles in tablets, syrups, injections and drops. We also present events in which the presence of non-linearity was taken into account by using artificial neural networks (ANNs). Finally, a new tool that allows one to analyze samples containing non modeled interferences has been discussed, based on the acquisition of matrix-type (second-order) data, and subsequent construction of models presenting the phenomenon known as the “second order advantage”.
Keywords: highperformance liquid chromatography (hplc), analytical chemistry, calibration model, partial least-squares (pls), net analyte signal, error indicator function (eif), artificial neural networks(anns), excitation-emission matrices (eems), diode-array spectrophotometer
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