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Monitoring the amount of active pharmaceutical ingredient (API) in finished dosage form is important to ensure the content uniformity of the product. In this report, we summarize the development and validation of a hyperspectral imaging (HSI) technique for rapid in-line prediction of the active pharmaceutical ingredient (API) in microtablets with concentrations varying from 60 to 130% API (w/w). The tablet spectra of different API concentrations were collected in-line using an HSI system within the visible/near-infrared (vis/NIR; 400-1000 nm) and short-wave infrared (SWIR; 1100-2500 nm) regions. The ability of the HSI technique to predict the API concentration in the tablet samples was validated against a reference high-performance liquid chromatography (HPLC) method. The prediction efficiency of two different types of multivariate data modeling methods, that is, partial least-squares regression (PLSR) and principle component regression (PCR), were compared. The prediction ability of the regression models was cross-validated against results generated with the reference HPLC method. The results obtained from the PLSR models showed reliable performance for predicting the API concentration in SWIR region. The highest coefficient of determination (Rp) was 0.96, associated with the lowest predicted error and bias of 4.45 and -0.35%, respectively. Furthermore, the concentration-mapped images of PLSR and PCR models were used to visually characterize the API concentration present on the tablet surface. Based on these results, we suggest that HSI measurement combined with multivariate data analysis and chemical imaging could be implemented in the production environment for rapid in-line determination of pharmaceutical product quality.

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In this study, hyperspectral imaging (HSI) sensor was used to rapidly estimate the content of an active pharmaceutical ingredient (API) in powder blend samples in order to optimize small molecule formulation. Small molecule powder blend samples containing excipients and varying API concentrations were prepared using a blender. The spectrum of each powder blend was obtained using a short‐wave infrared hyperspectral imaging (SWIR HSI) system over a wavelength range of 1,000–2,500 nm. The use of the SWIR HSI method to predict API concentration in the powder blend samples was validated against that of a high‐performance liquid chromatography method. Partial least squares (PLS) regression and least squares support vector machine (LS‐SVM) analyses were used to build calibration models for predicting API concentration in the powder samples. Both the PLS and LS‐SVM models yielded high coefficients of determination of 0.99 and low errors (root‐mean‐square error of prediction) for API content prediction, which were 0.73 and 0.60 mg, respectively. Furthermore, image processing algorithms were developed to visualize the predicted API concentration in each pixel of the powder surface. Concentration map and binary images were also used to visualize the API concentration in the powder samples. The results suggest that the HSI technique permits the quantification and visualization of pharmaceutical ingredients and could be easily used during manufacturing for the non‐destructive formulations optimization and quality control of products.