Monday afternoon?s 2PM session titled, ?Challenges for Raman,? will discuss topics ranging from calibrating spectrographs to transferring chemometric models between hand-held Raman systems and more.
Monday afternoon’s 2PM session titled, “Challenges for Raman,” will discuss topics ranging from calibrating spectrographs to transferring chemometric models between hand-held Raman systems and more.
The first topic addressed, “A Novel Method for Calibration of Imaging Spectrographs,” will be given by Jason McClure, of Princeton Instruments. Following this presentation will be “Alignment of Raman Spectra for Calibration Maintenance,” given by Wesley of Applied Physics Lab.
Next will be an interesting talk given by Robert L. Green of Ahura Scientific titled, “Transfer of Chemometric Models Between Handheld Raman Systems: A Case Study Involving More than 2000 Fielded Systems.”
Following this topic is another discussion regarding the challenges and rewards involved in this science (“Process Raman:Challenges and Rewards”) given by Brian Marquardt of the University of Washington.
Charles Gardner of ChemImage Corporation will give a presentation in conjuction with Applied Perception, Inc. and the US Army TATRC titled, “Integration of a Proximity Raman Hyperspectral Imaging Chemical, Biological and Explosives (CBE) Detector on a Small Unmanned Ground Vehicle.”
Finally, closing out this interesting session will be, “Status of Small Robot-Mounted or Hand-Held, Solar-Blind, Standoff Chemical, Biological, and Explosives (CBE) Sensors,” presented by William Hug on behalf of Photon Systems, Inc. and Caltech, JPL.
Advancing Near-Infrared Spectroscopy and Machine Learning for Personalized Medicine
February 12th 2025Researchers have developed a novel approach to improve the accuracy of near-infrared spectroscopy (NIRS or NIR) in quantifying highly porous, patient-specific drug formulations. By combining machine learning with advanced Raman imaging, the study enhances the precision of non-destructive pharmaceutical analysis, paving the way for better personalized medicine.
New Method for Detecting Fentanyl in Human Nails Using ATR FT-IR and Machine Learning
February 11th 2025Researchers have successfully demonstrated that human nails can serve as a reliable biological matrix for detecting fentanyl use. By combining attenuated total reflectance-Fourier transform infrared (ATR FT-IR) spectroscopy with machine learning, the study achieved over 80% accuracy in distinguishing fentanyl users from non-users. These findings highlight a promising, noninvasive method for toxicological and forensic analysis.