Key Points
- DOGE-related federal funding reductions have led to salary cuts, reduced lab budgets, and graduate program slowdowns, with experts calling for systemic reforms and stronger academic–industry collaboration to stabilize the field.
- BaySpec’s VaporID system, tested at the U.S.–Mexico border, showed promise as a non-contact fentanyl detection tool, while Raman spectroscopy is increasingly used in cancer diagnostics for its data-rich, non-invasive capabilities.
- Raman spectroscopy combined with ML is improving fruit quality assessment, while FT-IR and GC-MS techniques are being used to predict wine ratings.
This week, Spectroscopy published articles highlighting recent studies in several application areas, including cancer diagnostics and research, drug discovery, and food analysis. Key techniques highlighted in these articles include mass spectrometry (MS), Raman spectroscopy, and Fourier transform infrared (FT-IR) spectroscopy. Happy reading!
How Analytical Chemists Are Navigating DOGE-Driven Funding Cuts
Recent federal funding cuts related to the Department of Energy (DOGE) have significantly impacted the analytical chemistry community, leading to reduced salaries, laboratory budgets, and graduate support. Researchers attribute these politically driven changes to broader systemic issues in U.S. science funding, especially during administrative transitions (1). Although federal laboratories face varied timelines of impact, the overall uncertainty about future appropriations has created stress and instability. In this article, several experts highlight scaled-back graduate admissions, spending freezes, and widespread anxiety among academic personnel (1). Experts suggest that long-term recovery will require structural reforms, streamlined administration, and stronger collaboration between academia and industry to ensure sustainability and resilience (1).
Improving Drug Detection Techniques
At the American Society of Mass Spectrometry (ASMS) conference in Baltimore, Maryland, BaySpec’s Krisztian Torma presented successful field test results of the VaporID system, which is a contactless drug detection technology developed at Pacific Northwest National Laboratory (PNNL) by Robert Ewing’s team (2). During testing at the U.S.–Mexico border in Nogales, the system accurately identified trace amounts of fentanyl and other narcotics. In the final part of our three-part interview with Ewing, he highlighted the benefits of VaporID over traditional methods like swab sampling and canine units, noting its non-invasive nature, broader coverage, and 24/7 operability (2). The system also offers adaptability to emerging synthetic drugs, making it a promising tool for future drug interdiction efforts (2).
The Role of Raman Spectroscopy in Modern Cancer Research
Raman spectroscopy is playing a large role in oncology applications. As a result, there is an abundance of recent studies exploring how to apply Raman spectroscopy in cancer research and diagnostics. In this compilation article, five recent published peer-reviewed papers are highlighted, showcasing the versatility of Raman spectroscopy in cancer diagnostics and research (3). Many of the articles highlight the integration of machine learning (ML) algorithms with Raman spectroscopy, which has helped improve patient outcomes by efficiently analyzing the spectral data (3).
New Frontiers in Fruit Analysis: How Raman Spectroscopy and Machine Learning Are Improving Quality Detection
A recent review article by researchers at Guangdong Polytechnic Normal University, which was published in Agriculture, explores the growing use of Raman spectroscopy in the agri-food sector, especially for assessing fruit quality (4). The technique, valued for its non-destructive and rapid analysis, is being enhanced through machine learning (ML) and chemometrics to detect fruit diseases, pesticide residues, and geographic origins. Challenges like weak signals and background interference are being addressed with advanced spectral techniques such as SERS and SORS (4). With ML models automating complex data interpretation, Raman spectroscopy is poised to become a key tool in food safety, smart agriculture, and real-time quality monitoring (4).
Predicting Wine Ratings Using FT-IR Spectroscopy: A Data-Driven Approach
Researchers at the University of Copenhagen conducted a feasibility study to predict Vivino user wine ratings using chemical analysis and machine learning. By analyzing 89 German white wines with gas chromatography–mass spectrometry (GC–MS) for volatile compounds and FT-IR for physicochemical properties, they explored how these factors relate to crowd-sourced Vivino scores (5). The study highlights the platform’s value in reflecting average consumer preferences and supports the use of instrumental data to estimate perceived wine quality (5). Published in Food Chemistry, the study’s findings suggest that combining analytical techniques with ML and user-generated data offers a promising path for future wine quality prediction models (5).
References
- Workman, Jr., J. How Analytical Chemists Are Navigating DOGE-Driven Funding Cuts. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/how-analytical-chemists-are-navigating-doge-driven-funding-cuts (accessed 2025-07-16)
- Wetzel, W. Improving Drug Detection Techniques. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/improving-drug-detection-techniques (accessed 2025-07-16).
- Wetzel, W. The Role of Raman Spectroscopy in Modern Cancer Research. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/the-role-of-raman-spectroscopy-in-modern-cancer-research (accessed 2025-07-16).
- Wetzel, W. New Frontiers in Fruit Analysis: How Raman Spectroscopy and Machine Learning Are Improving Quality Detection. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/new-frontiers-in-fruit-analysis-how-raman-spectroscopy-and-machine-learning-are-improving-quality-detection (accessed 2025-07-16).
- Chasse, J. Predicting Wine Ratings Using FT-IR Spectroscopy: A Data-Driven Approach. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/predicting-wine-ratings-using-ft-ir-spectroscopy-a-data-driven-approach (accessed 2025-07-16).
