Quantification of metformin in pharmaceutical formulations using Vis-SWNIR hyperspectral imaging combined with multivariate curve resolution

Abstract

Hyperspectral imaging (HSI), integrated with chemometrics, is a powerful tool in process analytical technology (PAT) for accurately determining the concentration of metformin hydrochloride, the active pharmaceutical ingredient (API), during the manufacturing of extended-release tablets. This study evaluated the effectiveness of visible-short wavelength near infrared (Vis-SWNIR) HSI (400–950 nm) combined with multivariate curve resolution-alternating least squares (MCR-ALS) for determining the API in a six-component mixture, which included excipients such as hydroxypropyl methylcellulose (HPMC), polyvinylpyrrolidone (PVP), and microcrystalline cellulose. Both linear and nonlinear calibration models were assessed. Partial least squares regression (PLSR) with the API concentration profile from MCR-ALS yielded promising calibration metrics, with a root mean square error of prediction (RMSEP) of 6.1 % w/w and a coefficient of determination (R²p) of 0.94, based on a set covering an API range of 0.0–70.6 % w/w. The method also demonstrated promising figures of merit (FOMs), including a limit of detection (LOD) of 4.7 % w/w and a limit of quantification (LOQ) of 14.2 % w/w, indicating its effectiveness in detecting API levels below standard thresholds. Further improvement was achieved using support vector machine (SVM) with radial basis function (RBF), enhancing RMSEP to 5.6 % w/w and R²p to 0.98, aiming to evaluate a non-linear method as a proof of concept. The study concluded that Vis-SWNIR HSI combined with chemometrics, provides an effective and non-destructive method for determining the correct API concentration in powder blends during blending, without the need for sample preparation.