Pharmaceutical Industry Embraces NIR Technology (PDF)

A team of researchers from Nankai University has developed an advanced method to classify tea types using near-infrared spectroscopy (NIRS) and artificial intelligence (AI). Their approach, involves a fine-tuned 1DResNet model, outperforms traditional methods, and offers an accurate, non-destructive, and efficient classification solution for the tea industry.

An interview with Charles Wilkins, the winner of the 2013 American Chemical Society Division of Analytical Chemistry Award in Chemical Instrumentation, sponsored by the Dow Chemical Company.

Scientists demonstrate a self-supervised learning framework that dramatically improves near-infrared spectroscopy classification results, even with minimal labeled data.

This excerpt from The Concise Handbook of Analytical Spectroscopy, which spans five volumes, serves as a comprehensive reference, detailing the theory, instrumentation, sampling methods, experimental design, and data analysis techniques for each spectroscopic region.

A recent study published in Meat Science highlighted how visible and near-infrared (vis-NIR) spectroscopy, when combined with chemometrics, can differentiate lamb meat based on pasture-finishing durations.

Spectroscopy sat down with Daniel Cozzolino of the University of Queensland to discuss his latest research using near-infrared (NIR) spectroscopy to determine the fatty acid content in black soldier fly.