In-Process Control Assay of Pharmaceutical Microtablets Using Hyperspectral Imaging Coupled with Multivariate...

Area of Science:

• Pharmaceutical Sciences
• Analytical Chemistry
• Spectroscopy

Background:

• Ensuring content uniformity of active pharmaceutical ingredient (API) in finished dosage forms is critical for product efficacy and safety.
• Traditional methods for API quantification can be time-consuming, limiting real-time quality control during manufacturing.

Purpose of the Study:

• To develop and validate a hyperspectral imaging (HSI) technique for rapid, in-line prediction of API concentration in microtablets.
• To compare the performance of partial least-squares regression (PLSR) and principal component regression (PCR) for API quantification using HSI data.

Main Methods:

• Collected hyperspectral data from microtablets with varying API concentrations (60-130% w/w) in the visible/near-infrared (vis/NIR) and short-wave infrared (SWIR) regions.
• Developed and cross-validated PLSR and PCR multivariate models to predict API concentration against a high-performance liquid chromatography (HPLC) reference method.
• Generated concentration-mapped images to visualize API distribution on the tablet surface.

Main Results:

• PLSR models demonstrated reliable performance for API prediction, particularly in the SWIR region.
• Achieved a high coefficient of determination (R²p) of 0.96, with low predicted error (4.45%) and bias (-0.35%).
• HSI-derived concentration maps provided visual insights into API distribution within the microtablets.

Conclusions:

• Hyperspectral imaging (HSI) combined with multivariate data analysis offers a viable solution for rapid, in-line determination of pharmaceutical product quality.
• The developed HSI method can be implemented in production environments to monitor API content uniformity efficiently.
• This technique supports enhanced quality control and process understanding in pharmaceutical manufacturing.