On Tuesday February 27, a Pittcon oral session, titled “Novel Spectroscopic Techniques,” took place at the San Diego Convention Center in San Diego, California. This session featured three talks that covers spectroscopic applications across various fields.
The first talk is titled “Low Water Analysis Reimagined: Instant NIR Measurements for Quality Control & Process Upgrades,” and was delivered by Elena Hagemann a product manager. from Metrohm. Haagemann’s talk focused on water analysis, detailing how using near infrared (NIR) spectroscopy requires calibration efforts, and how auto-building prediction models down can help save time by removing the calibration efforts from the process.
Next, Tobias Gokus of Attocube Systems delivered a talk titled, “Infrared Correlation Nanoscopy with Unprecedented Spectral Coverage.” Gokus’s spoke about a recent study that was conducted on how scattering-type scanning near-field optical microscopy (s-SNOM) and tapping AFM-IR+ imaging and spectroscopy based on a fully integrated, automated OPO laser can cover the spectral range from 1.5–18.2 µm (7100–540 cm-1) with a narrow linewidth of <4 cm-1 in the entire spectral range.
Jaakko Lehtinen of Gasera closed out the session with a discussion on “PFC leak detection in semiconductor cleanrooms using photoacoustic spectroscopy.” Lehtinen’s lectured focused on monitoring perfluorocarbons (PFCs) levels using a new gas monitor, called Gasera One PFC, that can directly monitor different kinds of PFCs at very low levels.
AI and Dual-Sensor Spectroscopy Supercharge Antibiotic Fermentation
June 30th 2025Researchers from Chinese universities have developed an AI-powered platform that combines near-infrared (NIR) and Raman spectroscopy for real-time monitoring and control of antibiotic production, boosting efficiency by over 30%.
Combining AI and NIR Spectroscopy to Predict Resistant Starch (RS) Content in Rice
June 24th 2025A new study published in the journal Food Chemistry by lead authors Qian Zhao and Jun Huang from Zhejiang University of Science and Technology unveil a new data-driven framework for predicting resistant starch content in rice