A robust near infrared (NIR) method able to quantify the active content of pilot non-coated pharmaceutical pellets was developed. A protocol of calibration was followed, involving 2 operators, independent pilot batches of non-coated pharmaceutical pellets and two different NIR acquisition temperatures. Prediction models based on Partial Least Squares (PLS) regression were then carried out. Afterwards, the NIR method was fully validated for an active content ranging from 80 to 120% of the usual active content using new independent pilot batches to evaluate the adequacy of the method to its final purpose. Conventional criteria such as the R(2), the Root Mean Square Error of Calibration (RMSEC), the Root Mean Square Error of Prediction (RMSEP) and the number of PLS factors enabled the selection of models with good predictive potential. However, such criteria sometimes fail to choose the most fitted for purpose model. Therefore, a novel approach based on accuracy profiles of the validation results was used, providing a visual representation of the actual and future performances of the models. Following this approach, the prediction model using signal pre-treatment Multiplicative Scatter Correction (MSC) was chosen as it showed the best ability to quantify accurately the active content over the 80-120% active content range. The reliability of the NIR method was tested with new pilot batches of non-coated pharmaceutical pellets containing 90 and 110% of the usual active content, with blends of validation batches and industrial batches. All those batches were also analyzed by the HPLC reference method and relative errors were calculated: the results showed low relative errors in full accordance with the results obtained during the validation of the method, indicating the reliability of the NIR method and its interchangeability with the HPLC reference method.
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