The validation of bioanalytical methods holds critical importance for regulatory agencies and organizations dedicated to ensuring the safety, efficacy, and quality of pharmaceuticals. In this context, the recent release of the ICH M10 guideline in May 2022 represents a significant milestone in standardizing bioanalytical method validation globally. However, this guideline lacks explicit experimental protocols for implementation. In this study, we address the practical implementation of the newly released ICH M10 guideline by providing a detailed validation protocol for a bioanalytical method. Our method specifically targets tiaprofenic acid, a widely used nonsteroidal anti-inflammatory drug. Tiaprofenic acid is a critical component in bioequivalence studies, underscoring the necessity for precise and accurate quantification within complex biological matrices. The integration of the accuracy profile approach, a statistical tool, enhances the significance of this work. This approach aids in assessing the accuracy and precision of bioanalytical methods, establishing confidence intervals around measured concentrations, and quantifying the level of accuracy and precision expected when using the validated method.

In this study, the adulteration of Moroccan Picholine extra virgin olive oil with Arbequina virgin olive oil was monitored using the Fourier transform mid-infrared (FT-MIR) spectroscopy technique and chemometrics methodologies. To discriminate between olive oil that has been adulterated and unadulterated, principal component analysis (PCA) was utilized for qualitative analysis. We created the best calibration models for quantitative analysis using principal component regression (PCR) and partial least-squares regression (PLS). The first three principal components account for 95% of the overall variability, according to PCA analysis. PCA allows for the classification of the dataset into two groups: adulterated and unadulterated Moroccan Picholine olive oil. The application of the PLS and PCR calibration models for the quantification of adulteration demonstrates high-performance capabilities, as indicated by high values of correlation coefficients R2 greater than 0.999 and 0.995 and lower values of root mean square error (RMSE) less than 0.767 and 2.16 using PLS and PCR, respectively. According to our results, FT-MIR spectroscopy combined with chemometrics approaches can be used successfully as a simple, quick, and non-destructive method for the quantification and discrimination of adulterated olive oil.