Plata Enríquez, Jorge L.

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    Analytical method development using quantum cascade laser spectroscopies for determining low concentrations of active pharmaceutical ingredients (APIs)
    (2025-02-27) Plata Enríquez, Jorge L.; Hernández Rivera, Samuel P.; College of Arts and Sciences - Sciences; Torres Candelaria, Jessica; Meléndez Martínez, Enrique; Mina Camilde, Nairmen; Department of Chemistry; Pérez Muñoz, Fernando
    The study investigates the use of Quantum Cascade Laser Spectroscopy (QCLS) combined with Multivariate Analysis (MVA) to enhance Process Analytical Technology (PAT) in pharmaceutical manufacturing and explosives detection. It focuses on QCLS in three optical configurations: Mid-Infrared Attenuated Total Reflection (ATR), Diffuse Reflectance Backscattering (DRBS), and Grazing Angle Probe (GAP), addressing specific challenges in the pharmaceutical and explosives sections. In the pharmaceutical section, the research analyzes low-concentration acetaminophen tablets in nine formulations ranging from 0.0% to 3.0% w/w of active pharmaceutical ingredient (API) concentration. The tablet blends include excipients such as mannitol, croscarmellose, cellulose, and magnesium stearate, which add complexity and are well-managed by QCLS. This non-contact spectroscopic analysis spans a spectral range of 770–1890 cm-1 . Various regression models, including Partial Least Squares (PLS), Support Vector Machine Regression (SVM), Decision Tree Regression, and Linear Regression, were employed to assess API distribution and potential inhomogeneities that may impact drug efficacy. Model performance was estimated based on Root Mean Square Error (RMSE) and Coefficient of Determination (R²), with SVM and PLS showing superior predictive accuracy, thereby enhancing precision in pharmaceutical analysis. For explosives detection, the study employed Infrared (IR) and Raman spectroscopy to identify highly energetic materials (HEMs) such as TNT, DNT, PETN, and RDX on metallic surfaces and solid samples, simulating real-world conditions. Reference spectra were used for IR and Raman detections, and comparative quantitative analyses were performed based on the spectral correlation index. Supervised multivariate analyses, including Partial Least Squares Discriminant Analysis (PLS-DA), were applied to enhance detection accuracy and sensitivity. The results demonstrated the effectiveness of vibrational spectroscopy in accurately detecting HEMs, highlighting its vital role in public safety. The application of QCLS in this study demonstrates its ability to handle complex analytical challenges effectively. In pharmaceutical analysis, the high power and precision of QCLS, combined with MVA, led to robust discrimination methodologies crucial for ensuring drug safety and efficacy. Similarly, in explosives detection, the same technological principles have significantly advanced security protocols. This research underscores the transformative potential of combining QCLS with MVA in PAT, promising to elevate precision and safety standards across multiple industries.