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Ultrafast Tablet Imaging Made Practical with the Novel Use of QCL Sources Coupled with Machine Learning-Based Analysis Algorithms

Oct 26, 2022
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Presented by
Bruker Optics Inc.
ABOUT THIS WEBINAR
The composition of pharmaceutical tablets and the distribution of the ingredients therein can be determined using chemical imaging performed by vibrational microscopy. Doing so provides valuable information for manufacturing, troubleshooting, and counterfeit analysis.

Until recently, the applicability of vibrational microscopy was limited by the extensive analysis time required to obtain high-resolution chemical images. Now, with the maturity and commercialization of QCL-based IR laser imaging, complete tablets can be analyzed in a matter of minutes.

With this technology, high spatial resolution may be paired with state-of-the-art machine learning algorithms, providing a reliable, fast method to evaluate and visualize particle size and component distribution.

***This presentation premiered during the 2022 BioPhotonics Conference. For more information on Photonics Media conferences, visit events.photonics.com.  

About the presenter
 
Thomas TagueThomas Tague, Ph.D., is the applications manager for Bruker Optics. He is also a member of the Visiting Advisory Committee of the Metropolitan Museum of Art in N.Y. and the Advisory Board of Amplified Sciences. Tague received his doctorate in chemistry from The University of Utah and his B.S., also in chemistry, from The University of Texas at San Antonio. He conducted his postdoctoral research at the University of Virginia, working with professor Lester Andrews. Tague is a member of the American Chemical Society, the Society for Applied Spectroscopy, the American Physical Society, and Optica. He is active in developing new methods and instrumentation, with the goal of improving the sensitivity and detection limits of spectroscopy-related applications. He has more than 90 publications and holds five patents.
imaginglasersMicroscopypharmaceuticalvibrational microscopyQCLlaser imagingmachine learning
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