Chemometrics

Hassan SA, Abdelaal SH, El Azab NF, El-Kosasy AM. A new era in dietary supplement regulation: sustainable chemometric approach for quality control of four dietary ingredients in slimming preparations. Journal of Food Composition and Analysis. 2025;148:108619. AbstractWebsite

By the end of 2024, the largest reorganization in the modern history of the U.S. FDA took effect. This overhaul included the establishment of the Office of Food Chemical Safety, Dietary Supplements, and Innovation (OFCSDSI), underscoring the growing need for stringent oversight of the dietary supplement (DS) industry. Phenethylamines stimulants are on regulatory monitoring lists, yet they remain available in the DS market. With this reshaping of the regulatory framework of DS, there is an increasing demand for specific and sensitive analytical methods to monitor and control such dietary ingredients. This study introduces chemometrics as a pivotal tool in the enhanced regulatory oversight of DS. Advanced spectrophotometric techniques, coupled with chemometric models, were employed for the simultaneous quantitation of 2-Phenethylamine, caffeine, p-Hordenine, and p-Synephrine in complex slimming supplement formulations. Three multivariate models—Partial Least Squares-1 (PLS-1), Genetic Algorithm-Partial Least Squares (GA-PLS), and Genetic Algorithm-Artificial Neural Networks (GA-ANN)—were evaluated. Notably, the GA-based models (GA-ANN) outperformed PLS-1 in resolving spectral overlap without preliminary separation steps, even in the presence of formulation additives. These findings demonstrate the potential of chemometrics as viable eco-friendly alternative for chromatography in routine quality control of DS under the FDA’s new regulatory paradigm.

Wahba IA, El-Mosallamy SS, Fayed AS, Hassan SA. Beyond greenness and whiteness, a sustainability assessment framework integrating circularity for pharmaceutical quality control: Application to chemometric impurity profiling of paracetamol. Sustainable Chemistry and Pharmacy. 2026;51:102410. AbstractWebsite

The transition from Green to sustainable chemistry demands a paradigm shift in how analytical methodologies are evaluated, moving beyond isolated performance metrics toward holistic life-cycle assessment. In this study, we introduce a novel Sustainability Assessment Framework (Greenness, Applicability, Sustainability) to rigorously benchmark analytical strategies. As a proof-of-concept, the framework was applied to a complex pharmaceutical challenge: the impurity profiling of paracetamol (PAR) along with three official impurities: para-aminophenol (PAP), para-nitrophenol (PNP), and para-chloroacetanilide (PCA), and two co-formulated drugs ibuprofen (IBU) and chlorzoxazone (CHZ). Two chemometric models, partial least squares (PLS) and artificial neural networks (ANN), were utilized to achieve this purpose. Furthermore, both models were subjected to a variable selection process using genetic algorithm (GA) to identify the most significant wavelengths. The genetic algorithm-optimized neural network (GA-ANN) demonstrated superior predictive accuracy for the six-component mixture. The core innovation of this work lies in the comparative application of the Sustainability Assessment Framework, benchmarking the proposed chemometric method against a reference HPLC method using eight state-of-the-art metrics. Greenness was evaluated via Analytical Eco-Scale, GAPI, and AGREE; Applicability via BAGI, RGB12 algorithm, and EPPI; and Sustainability via the Carbon Footprint, NQS Index, and %Circularity. This study establishes the Framework as a robust prototype for modern quality control, validating chemometrics not merely as an alternative technique, but as a superior sustainable evolution aligned with the principles of the circular economy.

Basha MA, Abd El-Rahman MK, Bebawy LI, Moustafa AA, Hassan SA. A comparative Study of two analytical techniques for the simultaneous determination of Amprolium HCl and Ethopabate from combined dosage form and in presence of their alkaline degradation. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2020;243:118756. Abstract

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Ibrahim N, Elzanfaly ES, El Gendy AE, Hassan SA. Development, optimization, and validation of a green spectrofluorimetric method for the determination of moxifloxacin using an experimental design approach. Research Journal of Pharmacy and Technology. 2021;14(4):1880-6. Abstract

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Sharkawi MM, Farid NF, Hassan MH, Hassan SA. New chemometrics-assisted spectrophotometric methods for simultaneous determination of co-formulated drugs montelukast, rupatadine, and desloratadine in their different dosage combinations. BMC chemistry. 2024;18(1):232. AbstractWebsite

Two accurate, precise and robust multivariate chemometric methods were developed for the simultaneous determination of montelukast sodium (MON), rupatadine fumarate (RUP) and desloratadine (DES). These methods provide a cost-effective alternative to chromatographic techniques by utilizing spectrophotometry in pharmaceutical quality control. The proposed approaches, partial least squares-1 (PLS-1) and artificial neural network (ANN), were optimized using genetic algorithm (GA) to select the most influential wavelengths, enhancing model performance. A five-level, three-factor design was employed to construct a calibration set with 25 mixtures, utilizing concentration ranges of 3–19, 5–25, and 4–20 µg.mL−1 for MON, RUP, and DES, respectively. An independent validation set was employed to assess the performance of the models. GA significantly improved the PLS-1 and ANN models for RUP and DES, though minimal enhancement was observed for MON. These methods were successfully applied to the simultaneous quantification of the compounds in pharmaceutical formulations and proved useful as stability-indicating assays for RUP, given that DES is a known degradation product. The developed methods offer a valuable tool for impurity profiling and quality control in pharmaceutical analysis.

Kelani KM, Fekry RA, Fayez YM, Hassan SA. Advanced chemometric methods for simultaneous quantitation of caffeine, codeine, paracetamol, and p-aminophenol in their quaternary mixture. Scientific Reports. 2024;14:2085. AbstractWebsite

Two different multivariate techniques have been applied for the quantitative analysis of caffeine, codeine, paracetamol and p-aminophenol (PAP) in quaternary mixture, namely, Partial Least Squares (PLS-1) and Artificial Neural Networks (ANN). For suitable analysis, a calibration set of 25 mixtures with various ratios of the drugs and PAP impurity were established using a 4-factor 5-level experimental design. The most meaningful wavelengths for the chemometric models were chosen using Genetic Algorithm (GA) as a variable selection technique. By using an independent validation set, the validity of the proposed methods was evaluated. A comparative study was established between the three multivariate models (PLS-1, GA–PLS and GA–ANN). The comparison between the various models revealed that the GA–ANN model was superior at resolving the highly overlapped spectra of this quaternary combination. The drugs were successfully quantified in their pharmaceutical dosage form utilizing the GA–ANN models.

Hassan SA, Ibrahim N, Elzanfaly ES, El Gendy AE. Simultaneous Determination of Amlodipine and Olmesartan Using HPLC with Fluorescence Detection. Pharmaceutical Chemistry Journal. 2021;55(2):206-12. Abstract

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