Best of the Week: The 2026 Emerging Leader in Molecular Spectroscopy, Advancements in FT-IR Spectroscopy

At the annual SciX Conference, Spectroscopy Magazine presents the Emerging Leader in Molecular Spectroscopy Award. This year’s winner is Jay Kitt, who is a research assistant professor at the University of Utah. A recently published feature article on Kitt explores his work in confocal Raman microscopy from phospholipid membranes to nucleic acids, polymer thin films, and peptide-membrane interactions. Also, a new proof-of-concept study explores how to improve Raman spectral comparisons, and we also highlight the key takeaways from last month’s “What’s Nu” LinkedIn newsletter.

This is the Best of the Week.

Each year at the annual SciX Conference, Spectroscopy Magazine honors an Emerging Leader in Molecular Spectroscopy. This year's recipient is Jay Kitt, who is a research assistant professor at the University of Utah.¹ Kitt has had a successful decade-long career since earning his PhD in 2016. Kitt’s research has focused on using confocal Raman microscopy to study everything from phospholipid membranes and nucleic acids to polymer thin films and peptide-membrane interactions.¹ He'll be formally recognized next month at SciX in Sparks, Nevada.

Next, we turn to a new proof-of-concept study tackling a persistent challenge in Raman spectroscopy, and that is spectral comparison. The researchers in this study combined a convolutional neural network (CNN) feature extractor with the traditional hit quality index (HQI) to create a hybrid comparison method.² By transforming spectra into a learned feature space before applying HQI, the approach reduced false similarity matches and improved discrimination between closely related proteins and antibody-drug conjugates. Tested against real protein and ADC data sets, the CNN-augmented method outperformed conventional approaches while keeping results interpretable.

We're also recapping last month's "What's Nu" newsletter, which covered the history of Beer's Law, innovations in dual-comb spectroscopy and forensic analysis, and how ICP-MS method transfer is solving palladium recovery and memory-effect issues.

Finally, our FT-IR feature explores how the technique is expanding into food authentication, detecting adulterants, verifying geographic origin, and identifying microplastics in environmental samples. With advances in chemometrics and AI, FT-IR continues tackling real-world problems across pharma, food science, medicine, and the environment.

And that’ll do it for Best of the Week. Thanks for reading, and we’ll see you next week.

References

1. Workman, Jr., J. The 2026 Emerging Leader in Molecular Spectroscopy Award. Spectroscopy Online, 2026. https://www.spectroscopyonline.com/view/the-2026-emerging-leader-in-molecular-spectroscopy-award (accessed September 9, 2026).
2. Khadka, N. CNN-Enhanced Hybrid Spectral Comparison Using HQI in a Learned Feature Space. Spectroscopy Online, 2026. https://www.spectroscopyonline.com/view/cnn-enhanced-hybrid-spectral-comparison-using-hqi-in-a-learned-feature-space (accessed September 9, 2026).
3. Wetzel, W. Highlights from What’s Nu August 2026. Spectroscopy Online, 2026. https://www.spectroscopyonline.com/view/highlights-from-what-s-nu-august-2026 (accessed September 9, 2026).
4. Wetzel, W. Advancements and Applications in FT-IR Spectroscopy. Spectroscopy Online, 2026. https://www.spectroscopyonline.com/view/advancements-and-applications-in-ft-ir-spectroscopy (accessed September 9, 2026).