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Background: Accurate determination of active pharmaceutical ingredients and impurities is essential for ensuring the safety and effectiveness of medications. This study focuses on the validation of a high-performance liquid chromatography (HPLC) method for quantifying atorvastatin and its impurities, addressing a critical aspect of pharmaceutical analysis., Objective: The primary objective is to conduct a comprehensive validation study for the HPLC method, covering specificity assessment, response function establishment, and a detailed analysis of precision, trueness, and tolerance intervals. The emphasis is on demonstrating the method's precision, accuracy, and stability-indicating capabilities across various concentrations and compounds., Methods: The HPLC method is validated through rigorous assessments, including specificity, response function establishment, and analyses of precision, trueness, and tolerance intervals. Induced degradation experiments are conducted to explore atorvastatin's behavior under extreme conditions. Insights into the compound's synthesis and degradation pathways are provided through a proposed mechanism for intramolecular esterification., Results: The results affirm the precision, accuracy, and stability-indicating capabilities of the validated HPLC method. The method effectively differentiates between atorvastatin and its impurities, showcasing its suitability for pharmaceutical quality control., Conclusions: The validated HPLC method emerges as a robust and reliable tool for atorvastatin analysis, contributing significantly to pharmaceutical research and quality control. Its application ensures the safety and efficacy of medications, reinforcing its role in pharmaceutical analysis., Highlights: This study not only validates a crucial HPLC method for atorvastatin analysis but also provides insights into the compound's behavior under extreme conditions and its synthesis and degradation pathways. The validated method serves as a cornerstone in pharmaceutical research and quality control, ensuring medication safety and efficacy., (© The Author(s) 2024. Published by Oxford University Press on behalf of AOAC INTERNATIONAL. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Monitoring impurities in drug products is a principal requirement of pharmaceutical regulatory authorities all over the world to ensure drug safety. For this reason, there is a great need for analytical QC of dugs products., Objective: In this study, a simple, efficient, and direct HPLC method was developed for the determination of three impurities of diclofenac., Methods: The HPLC method was developed using a mobile phase which consisted of an HPLC grade mixture, acetonitrile-0.01M phosphoric acid adjusted to pH 2.3 (1 + 3, by volume)., Results: The separation was performed in 15 min. The calibration curves of the three impurities were linear; the correlation coefficients were 0.999 at concentrations of 0.00015-0.003 µg/mL., Conclusion: The validation of this method shows that it meets all validation criteria. This shows the reliability of this method for the routine control of diclofenac impurities., Highlights: The validation of a robust HPLC method for the determination of diclofenac impurities is of great importance for the pharmaceutical industry to control its products., (© The Author(s) 2023. Published by Oxford University Press on behalf of AOAC INTERNATIONAL. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Recent technological progress has bolstered efforts to bring personalized medicine from theory into clinical practice. However, progress in areas such as therapeutic drug monitoring (TDM) has remained somewhat stagnant. In drugs with well-known dose-response relationships, TDM can enhance patient outcomes and reduce health care costs. Traditional monitoring methods such as chromatography-based or immunoassay techniques are limited by their higher costs and slow turnaround times, making them unsuitable for real-time or onsite analysis., Objective: In this work, we propose the use of a fast, direct, and simple approach using Fourier transform infrared spectroscopy (FT-IR) combined with chemometric techniques for the therapeutic monitoring of valproic acid (VPA)., Method: In this context, a database of FT-IR spectra was constructed from human plasma samples containing various concentrations of VPA; these samples were characterized by the reference method (immunoassay technique) to determine the VPA contents. The FT-IR spectra were processed by two chemometric regression methods: partial least-squares regression (PLS) and support vector regression (SVR)., Results: The results provide good evidence for the effectiveness of the combination of FT-IR spectroscopy and SVR modeling for estimating VPA in human plasma. SVR models showed better predictive abilities than PLS models in terms of root-mean-square error of calibration and prediction RMSEC, RMSEP, R2Cal, R2Pred, and residual predictive deviation (RPD)., Conclusions: This analytical tool offers potential for real-time TDM in the clinical setting., Highlights: FTIR spectroscopy was evaluated for the first time to predict VPA in human plasma for TDM. Two regressions were evaluated to predict VPA in human plasma, and the best-performing model was obtained using nonlinear SVR., (© The Author(s) 2022. Published by Oxford University Press on behalf of AOAC INTERNATIONAL. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: The authentication of the geographical origin of virgin olive oils (VOO) generally requires the use of sophisticated and time-consuming analytical techniques. There is a need for quick and simple analytical techniques to predict the origin of olive oils., Objective: This study aims to examine the physico-chemical data of olive oils collected in six regions of Morocco during two consecutive years 2020 and 2021, and also to evaluate the ability of FT-IR in combination with discrimination tools to study the geographical origin of Moroccan olive oils., Method: Fourier transform infrared spectroscopy (FTIR) was used in this study as an emerging analytical technique to express a unique "fingerprint." A preliminary processing of the ATR-FTIR spectral data was performed by preprocessing algorithm to reduce the noise and the effect of signal variation as well as to minimize the effects of light scattering to extract the maximum analytical information from the spectra. A multivariate statistical procedure based on principal component analysis (PCA) coupled with linear discriminant analysis (LDA) as well as partial least-squares discriminant analysis (PLS-DA) was developed to provide a powerful classification approach., Results: Based on the PCA, six clusters were identified. The application of PCA-LDA and PLS-DA procedures demonstrate a powerful capacity in predicting the geographic origin of olive oils; this capacity is shown by the high value of correct classification rate (CCR), varying between 84.09 and 100%., Conclusions: The suggested procedure has given reliable results for the classification of olive oils according to their geographical origin, with advantages such as being fast, inexpensive, and not requiring any prior separation process., Highlights: The performance of this approach is significantly faster and possesses a higher degree of selectivity and sensitivity. The implementation of this technique for routine analysis of olive oil would save significant time, resources, and solvents., (© The Author(s) 2022. Published by Oxford University Press on behalf of AOAC INTERNATIONAL. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Morocco is an important world producer and consumer of several varieties of date palm. In fact, the discrimination between varieties remains difficult and requires the use of complex and high-cost techniques., Objective: We evaluated in this work the potential of mid-IR (MIR) spectroscopy and chemometric models to discriminate eight date palm varieties., Method: Four chemometric models were applied for the analysis of the spectral data, including principal-component analysis (PCA), support-vector machine discriminant analysis (SVM-DA), linear discriminant analysis (LDA), and partial-least-squares (PLS) analysis. MIR spectroscopic data were recorded from the wavenumber range 4000-600 cm-1, with a spectral resolution of 4 cm-1., Results: The discriminant analysis was performed by LDA and SVM-DA with a 100% correct classification rate for the date mesocarp. PLS analysis was applied as a complementary chemometric tool aimed at quantifying moisture content; the validation of this model shows a good predictive capacity with a regression coefficient of 84% and a root-mean-square error of cross-validation of 0.50., Conclusions: The present study clearly demonstrates that MIR spectroscopy combined with chemometric approaches constitutes a promising analytical method to classify date palms according to their varietal origin and to establish a regression model for predicting moisture content., Highlights: An alternative analytical method to discriminate date palm cultivars by FTIR-attenuated total reflection spectroscopy coupled with chemometric approaches is described., (© AOAC INTERNATIONAL 2021. All rights reserved. For permissions, please email: journals.permissions@oup.com.)