Top articles published this week include several interviews to preview the upcoming SciX Conference, a recent study using an epidermal spectroscopic scanning (ESS) device to detect skin cancer, and a news story about machine learning for meteorite classification.
This week, Spectroscopy published various articles that covered many topics in analytical spectroscopy. This week’s articles feature topics such as clinical analysis and outer space. Much attention is given to spectroscopic techniques including surface-enhanced Raman spectroscopy (SERS), machine learning, and process analytical technology (PAT), among others. Below, we’ve highlighted some of the most popular articles, according to our readers and subscribers. Happy reading!
Joseph P. Smith Named 2024 Emerging Leader in Molecular Spectroscopy by Spectroscopy Magazine
Joseph P. Smith, Director of Process R&D Enabling Technologies at Merck, has been awarded the 2024 Emerging Leader in Molecular Spectroscopy Award for his impactful work in the pharmaceutical industry. Smith’s research in vibrational and electronic spectroscopy, biocatalysis, and data analysis has advanced pharmaceutical process development, particularly in biologics and vaccines (1). Since joining Merck in 2017, he has authored 53 articles and integrated machine learning and spectroscopy into process analytical technology (PAT) (1). Smith is also recognized for his mentorship and advocacy for early-career scientists and students with disabilities (1). He will present a plenary lecture at the 2024 SciX conference.
DermaSensor Device Demonstrates Ability to Improve Detection of Skin Cancer
DermaSensor, a health technology company, recently published a study demonstrating the effectiveness of its handheld epidermal spectroscopic scanning (ESS) device in detecting skin cancer. The device was tested by primary care clinicians (PCCs) on 178 lesions, showing 90.0% diagnostic sensitivity and 60.7% specificity compared to biopsy or dermatologist assessments (2). Without the device, PCCs had lower sensitivity. The study highlights trends in analytical spectroscopy, particularly the development of portable, non-invasive tools for real-time diagnostics. These devices not only enhance clinical analysis, but they also reduce costs and are expected to improve healthcare across multiple industries (2).
At SciX 2024, Jason Dwyer from the University of Rhode Island in Kingstown, RI, will receive the American Electrophoresis Society’s Mid-Career Award for his achievements. Dwyer, an associate professor of chemistry, specializes in nanopore technology and spectroscopy. In a recent interview, he shared insights on his research and the award (3).
Machine Learning Used for Meteorite Classification to Unlock Asteroid Composition Mysteries
A new study led by the Planetary Science Institute, Mount Holyoke College, and UMass Amherst introduces machine learning (ML) to enhance asteroid composition analysis using meteorite spectra. By applying logistic regression to a dataset of 1,422 meteorite spectra, researchers achieved 92% classification accuracy, linking meteorite types to asteroid parent bodies (4). This method surpasses traditional asteroid taxonomies, providing direct mineralogical insights into asteroid compositions. The study’s ML-based approach, combined with spectroscopy, offers a robust framework for predicting asteroid compositions, deepening our understanding of the Solar System's formation and history (4). Future research aims to refine the model with expanded spectral data.
At SciX 2024, Conor L. Evans will receive the Clara Craver Award, presented by The Coblentz Society, for his contributions to applied vibrational spectroscopy. Evans, an Associate Professor at Harvard Medical School in Cambridge, Massachusetts focuses on developing optical tools for biomedical research and clinical applications. His recent work includes innovative chemical imaging techniques like S4RS (5).
AI Boosts SERS for Next Generation Biomedical Breakthroughs
July 2nd 2025Researchers from Shanghai Jiao Tong University are harnessing artificial intelligence to elevate surface-enhanced Raman spectroscopy (SERS) for highly sensitive, multiplexed biomedical analysis, enabling faster diagnostics, imaging, and personalized treatments.
Tip-enhanced Raman Scattering using a Chemically-modified Tip
Published: June 9th 2025 | Updated: June 17th 2025In this tutorial article, Yukihiro Ozaki explores the recent advancements and broadening applications of tip-enhanced Raman scattering (TERS), a cutting-edge technique that integrates scanning probe microscopy (SPM) with surface-enhanced Raman scattering (SERS). TERS enables highly localized chemical analysis at the nano- to subnano-scale, achieving spatial resolution well beyond the diffraction limit of light. Ozaki highlights the versatility of TERS in various experimental environments—ranging from ambient air to ultrahigh vacuum and electrochemical systems—and its powerful utility in fields such as single-molecule detection, biomolecular mechanism studies, nanomaterial characterization, and high-resolution imaging.
Machine Learning Accelerates Clinical Progress of SERS Technology
May 22nd 2025A new review in TrAC Trends in Analytical Chemistry by Alfred Chin Yen Tay and Liang Wang highlights how machine learning (ML) is transforming surface-enhanced Raman spectroscopy (SERS) into a powerful, clinically viable tool for rapid and accurate medical diagnostics.
New SERS Platform Enhances Real-Time Detection of Cardiovascular Drugs in Blood
May 13th 2025Researchers at Harbin Medical University recently developed a SERS-based diagnostic platform that uses DNA-driven “molecular hooks” and AI analysis to enable real-time detection of cardiovascular drugs in blood while eliminating interference from larger biomolecules.