Hardware / Algorithm Integration for Pharmaceutical Analysis
Abstract
New experimental strategies and algorithmic approaches were devised and tested to improve the analysis of pharmaceutically relevant materials. These new methods were developed to address key bottlenecks in the design of amorphous solid dispersions for the delivery of low-solubility active pharmaceutical ingredients in the final dosage forms exhibiting high bioavailability. New hardware includes: i) triboluminescence based instrumentation for crystallinity quantification, ii) integration of differential scanning calorimetry with second harmonic generation. On the algorithm side, improvements include: i)iterative non-negative matrix factorization for blind deconvolution, ii) calibration-free quantification of crystallinity within second harmonic generation images of pharmaceutical materials, and iii)adversarial spectroscopy, an application of techniques from neural networks to common chemical analysis tools such as principle component analysis and linear discriminant analysis for improving these techniques as well as an interesting case to understand the underlying principles behind neural networks. The combination of hardware and algorithmic techniques have enabled crystallization detection and quantification within model pharmaceutical formulations such as ASDs.
Degree
Ph.D.
Advisors
Simpson, Purdue University.
Subject Area
Philosophy|Analytical chemistry|Artificial intelligence|Atmospheric sciences|Atomic physics|Chemistry|Mathematics|Medical imaging|Optics|Pharmaceutical sciences|Physics|Plastics|Polymer chemistry
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