Top articles published this week include a tribute to John Albert Reffner, who was known for his work in infrared microspectroscopy, an application of Raman spectroscopy to improve recycling practices, and an Icons of Spectroscopy column on Karl Norris, known as the founder of near-infrared (NIR) spectroscopy.
This week, Spectroscopy published articles highlighting recent studies in several application areas in analytical spectroscopy including environmental analysis and bioprocessing. Key techniques highlighted in these articles include infrared (IR) microspectroscopy, Raman spectroscopy, and near-infrared (NIR) spectroscopy. Happy reading!
In Remembrance: John Albert Reffner (1935–2025)
John Albert Reffner, PhD, who was a pioneer in analytical and forensic science, passed away on March 21, 2025, at age 90. Renowned for his work in IR microspectroscopy, he contributed significantly to microscopy, spectroscopy, and trace evidence analysis (1). Over a distinguished career spanning academia, industry, and forensic consultation, Reffner held roles at numerous organizations and earned prestigious awards for his scientific achievements (1). As a professor at John Jay College, he advocated for the integration of advanced analytical methods in forensic science. Remembered for his intellect, mentorship, and love of sailing, his legacy endures in science, education, and the lives he touched (1).
Karl Norris: A Pioneer in Optical Measurements and Near-Infrared Spectroscopy, Part I
Karl H. Norris (1921–2019), known as the founding father of near-infrared (NIR) spectroscopy, helped advance agricultural analysis through decades of work at the USDA. Beginning his career in the 1950s, Norris developed pioneering optical and electronic methods, including an automated egg-sorting system. His most influential contributions emerged from NIR spectroscopy, where he demonstrated that overlapping absorption bands could be analyzed using derivative mathematics for accurate, non-invasive compositional analysis (2). Norris developed the first computerized NIR spectrophotometer and helped establish NIR as a mainstream analytical tool across agriculture, food science, pharmaceuticals, and industry (2). His legacy includes critical advancements in instrumentation, calibration methods, and multivariate spectral analysis.
New Raman Spectroscopy Breakthrough Boosts E-Waste Plastic Recycling Efficiency
Ainara Pocheville and her team at the Gaiker Technology Centre recently developed a new approach to classify plastics in waste electrical and electronic equipment (WEEE) using Raman spectroscopy and machine learning. By optimizing Raman settings—especially using a 1064 nm laser—and combining the technique with discriminant analysis (DA) and support vector machine (SVM) models, they improved classification accuracy for complex plastic mixtures (3). This scalable solution addresses challenges in plastic recycling, aiming to enhance recovery rates, reduce downcycling, and support global efforts toward plastics circularity and sustainability.
Assessing the Impact of Increased Tariffs on Analytical Research and Equipment
President Trump’s proposed tariffs on imported semiconductor chips mark a new phase in his trade agenda, potentially disrupting key sectors reliant on advanced technology. The tariffs could significantly impact research and development, especially in science and healthcare, by raising costs for essential instruments and materials. Industries such as pharmaceuticals and life sciences face potential tariff increases from $0.5 billion to $63 billion annually (4). Laboratories and universities could potentially struggle with tighter budgets, delayed projects, and reduced access to specialized tools. Coupled with cuts to National Institute of Health (NIH) funding and restructuring at federal agencies, the policy shift could hinder innovation and scientific progress nationwide (4).
New Raman Spectroscopy Method Enhances Real-Time Monitoring Across Fermentation Processes
A recent study led by Marieke E. Klijn at Delft University of Technology demonstrated that supplementing calibration data sets with single compound spectra can improve the transferability of Raman spectroscopy-based partial least-squares (PLS) models for real-time fermentation monitoring. The new approach enhanced prediction accuracy for glucose, ethanol, and biomass across different fermentation modes, reducing root-mean-square errors significantly (5). It also simplified model updates without needing complex experiments. Although the results were promising, biomass quantification remains challenging because of overlapping spectral signals (5). Overall, the method offers a more efficient way to adapt Raman models for evolving bioprocesses, potentially accelerating biotechnological advancements.
AI-Powered SERS Spectroscopy Breakthrough Boosts Safety of Medicinal Food Products
April 16th 2025A new deep learning-enhanced spectroscopic platform—SERSome—developed by researchers in China and Finland, identifies medicinal and edible homologs (MEHs) with 98% accuracy. This innovation could revolutionize safety and quality control in the growing MEH market.
New Raman Spectroscopy Method Enhances Real-Time Monitoring Across Fermentation Processes
April 15th 2025Researchers at Delft University of Technology have developed a novel method using single compound spectra to enhance the transferability and accuracy of Raman spectroscopy models for real-time fermentation monitoring.