At SciX, Jay Kitt discusses a project applying Raman spectroscopy to PFAS analysis, the mentoring role he found after leaving traditional academia, and why machine learning could unlock the biochemical information in vibrational spectra.
In an interview with Spectroscopy Online at the
In the interview clip, Kitt said Marc Porter brought him into a Small Business Innovation Research (SBIR) project exploring Raman spectroscopy for PFAS analysis. Kitt said the team had ideas to contribute, and once funding was in place, the work has gained real momentum. He said he has enjoyed seeing his research put to real-world use and described designing a system for field deployment as a unique challenge.
He also noted an unexpected benefit of leaving a traditional academic role. Kitt said he had worried about losing the teaching side of academia, but he now mentors two colleagues, one with a doctorate and one who recently completed a bachelor’s degree in chemical engineering, and said he is doing more mentoring now than he did in a chemistry department.
Kitt’s second area of interest grew out of a SciX talk he attended roughly a decade ago, in which a Purdue University group described
The obstacle, he said, was interpretation: assigning meaning to amide modes, protein–phospholipid interactions, DNA structure, and hydrogen-bonding effects remains difficult. With mature instrumentation now widely available, Kitt sees machine learning and AI as tools for mining the literature and running correlation analyses to link specific band changes to biological meaning, positioning Raman spectroscopy to move in a powerful new direction.
