Magnesium, calcium, or zinc stearates are commonly used in pharmaceutical drug manufacturing. While these metal stearates exhibit similar chemical properties, they are not necessarily interchangeable in manufacturing processes. It is critical therefore that they are identified and differentiated at receipt in the warehouse to avoid process disruptions. Accurately differentiating stearate analogs at receipt by Raman spectroscopy has historically been challenging. Given the similarities of the spectra of the compounds, sophisticated chemometric software is often needed to build stearate models that are then used to identify them. This study shows that the Agilent Vaya handheld Raman spectrometer with Spatially Offset Raman Spectroscopy (SORS) can identify metal stearates in their original primary packaging, without the need for complex chemometric software packages. The handheld Vaya Raman enables the selective verification of stearates using a two-criteria decision algorithm combined with the "Analogous Sample" software feature.
Top content published this week include a digital e-book to celebrate National Forensic Science Week, a recap of the top 10 articles published in August 2026, and more.
A new review article in Microchimica Acta finds that smartphone-based optical and spectroscopic sensors show strong potential to decentralize food safety testing across contaminants like pesticides, heavy metals, and pathogens.
In the third episode of “Spectroscopy Around the Globe,” we're heading underground and into a hillside in southwestern France to talk about one of the greatest art discoveries of the 20th century: the Lascaux Caves.
An upcoming talk at the SciX Conference will explore the concept of electroosmosis, and how machine learning (ML) can be used to understand electroosmotic flow behavior.
Raman spectroscopy just got a brain, a stopwatch, and a nose. Together, these upgrades are turning a century-old light-scattering trick into a frontline tool for catching cancer earlier, chiral drugs cleaner, and toxic chemicals faster than ever before.
A new deep-learning framework that was recently developed improves the accuracy of near-infrared spectroscopy for non-destructively measuring internal quality traits.
At the upcoming 2026 SciX Conference, Ji-Xin Cheng at Boston University will be recognized with the Charles Mann Award for Applied Raman Spectroscopy. Leading up to the conference, Cheng sat down with Spectroscopy to talk about the advancements being made in confocal Raman microscopy.