The Eastern Analytical Symposium (EAS) Award for Outstanding Achievements will be presented to Peter de Boves Harrington on Tuesday, November 19.
The Eastern Analytical Symposium (EAS) Award for Outstanding Achievements was presented to Peter de Boves Harrington on Tuesday, November 19. Harrington gave a talk on “Chemometrics for the Masses: How to Painlessly Improve Your Science.”
Peter Harrington
Harrington received his PhD from the University of North Carolina-Chapel Hill in 1988. From 1987 to 1989 he created the DOS-based software platforms Resolve and Presager for identifying bacteria from their pyrolysis-mass spectra, while working for Kent Voorhees at the Colorado School of Mines. He joined the faculty of Ohio University (Athens, Ohio) in 1989. In 1992, he founded the Center for Intelligent Chemical Instrumentation. He has more than 200 publications, and has made more than 300 scientific presentations, including many plenary and keynote speeches around the world. In 2016, Harrington won the Ohio University College of Arts & Sciences Outstanding Faculty Research Award and the 2019 Eastern Analytical Symposium Award for Outstanding Achievement in Chemometrics. He is the Director of the Ohio University Center for Intelligent Chemical Instrumentation and is a Fellow of the American Academy of Forensic Sciences and the North American Academy of Sciences. Harrington’s current research focuses on the development and coupling of artificial intelligence to chemotyping by spectrometric measurements of botanical medicines and foods.
AI and Dual-Sensor Spectroscopy Supercharge Antibiotic Fermentation
June 30th 2025Researchers from Chinese universities have developed an AI-powered platform that combines near-infrared (NIR) and Raman spectroscopy for real-time monitoring and control of antibiotic production, boosting efficiency by over 30%.
Toward a Generalizable Model of Diffuse Reflectance in Particulate Systems
June 30th 2025This tutorial examines the modeling of diffuse reflectance (DR) in complex particulate samples, such as powders and granular solids. Traditional theoretical frameworks like empirical absorbance, Kubelka-Munk, radiative transfer theory (RTT), and the Hapke model are presented in standard and matrix notation where applicable. Their advantages and limitations are highlighted, particularly for heterogeneous particle size distributions and real-world variations in the optical properties of particulate samples. Hybrid and emerging computational strategies, including Monte Carlo methods, full-wave numerical solvers, and machine learning (ML) models, are evaluated for their potential to produce more generalizable prediction models.