The development of a virtual instrument (VI) interface to acquire spectroscopic measurements using a quantum cascade laser-based on LabVIEWTM is presented. The interface can enable mid-infrared laser spectroscopic data acquisition using a fast response mercury-cadmium-telluride (MCT) detector. The MCT is connected to an analog/digital converter card, and the data is displayed in a virtual high-speed oscilloscope. The VI processes the acquired data, displays the information, and saves it to a file through several operations using a “state machine” architecture. Transmission measurements of a polystyrene film were used to calibrate the system. They were compared to the National Institute of Standards and Technology-based FT-IR reference spectrum, showing excellent agreement. The system was tested with four different optical setups for IR Transmission, Diffuse Reflectance Infrared Spectroscopy, Reflection Absorption Infrared Spectroscopy, and Attenuated Total Reflection. The VI can be easily customized, allowing users to adapt it according to their specific laboratory needs.
The process analytical technology (PAT) initiative proposed by the US Food and Drug Administration (FDA) suggests innovative methods to better understand pharmaceutical processes. The development of analytical methods that quantify active pharmaceutical ingredients (APIs) in powders and tablets is fundamental to monitoring and controlling a drug product’s quality. Analytical methods based on vibrational spectroscopy do not require sample preparation and can be implemented during in-line manufacturing to maintain quality at each stage of operations. In this study, a mid-infrared (MIR) quantum cascade laser (QCL) spectroscopy-based protocol was performed to quantify ibuprofen in formulations of powder blends and tablets. Fourteen blends were prepared with varying concentrations from 0.0% to 21.0% (w/w) API. MIR laser spectra were collected in the spectral range of 990 to 1600 cm−1. Partial least squares (PLS) models were developed to correlate the intensities of vibrational signals with API concentrations in powder blends and tablets. PLS models were evaluated based on the following figures of merit: correlation coefficient (R2), root mean square error of calibration, root mean square error of prediction, root mean square error of cross-validation, and relative standard error of prediction. QCL assisted by multivariate analysis was demonstrated to be accurate and robust for analysis of the content and blend uniformity of pharmaceutical compounds.
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