Vol 31 No s6 Spectroscopy June 2016 Raman Technology Supplement PDF

At SciX, Kevin Wilkinson of the Universite de Montreal explains how faster data acquisition and new software have cut analysis time and how a particle’s chemical composition can link arsenic-bearing emissions back to a copper refinery’s processes.

Microspectroscopy, the marriage of spectroscopic chemical identification with microscopic spatial resolution, has advanced from Perkin-Elmer's first commercial infrared microspectrometer in 1953 to today's nanoscale AFM-IR probes, synchrotron-brightness infrared beamlines, and compact hyperspectral tissue imaging devices being developed for AI-assisted cancer detection.

Marc Porter describes how his group’s surface science work led to SERS-based immunoassays, why reproducibility and pre-analytics decide whether a test reaches the clinic, and the questions he still wants to answer.

The 2026 first edition of Spectroscopy's Chemometrics, Data Processing, Machine Learning, and Artificial Intelligence Terminology Guide defines the classical chemometric, statistical, machine learning, AI, and process analytical technology (PAT) terms most likely to be encountered by working spectroscopists, analytical chemists, and process engineers.

Does industry need to get more involved in training the next generation of spectroscopists?

In an interview at SciX, Alessandro De Giacomo discusses nanoparticles as signal enhancers and medical tags, a self-calibrating drone LIBS system, open questions in laser–matter interaction, and why students need a strong foundation in fundamentals.

The New York / New Jersey Section of the Society for Applied Spectroscopy will present its 2026 Gold Medal Award to Richard Crocombe at the Eastern Analytical Symposium on November 17, recognizing two decades of work that helped carry spectroscopy out of the laboratory and into the field.

Near-infrared spectroscopy combined with principal components analysis and a back propagation neural network identified Huanglongbing-infected Gannan navel orange leaves with an overall accuracy of 98.75%, outperforming LDA, KNN, and SVM models.