Application Notes: General

This study demonstrates the through-opaque-container analysis capability of the Agilent Vaya handheld Raman spectrometer by performing measurements on a range of common excipients and active ingredients within blue barrels. Spatially offset Raman spectroscopy (SORS) is the unique Agilent technology that is the basis of the unique Vaya container subtraction algorithm. This technology can optimize the spectra to provide the clearest signature of contents with the minimum amount of container interference. Verification of raw materials directly through plastic barrels provides efficient raw material identification (RMID) workflows in the warehouse without the need for specialized personnel or controlled sampling environments.

Handheld Raman spectrometers can distinguish raw materials through transparent and opaque packaging containers. This application note demonstrates the use of the handheld Agilent Vaya Raman raw material identity verification system based on spatially offset Raman spectroscopy (SORS) for chemical ID verification and testing of mRNA lipid nanoparticle (LNP) components through transparent glass and white opaque polyethylene containers.

Over 85% of scientific organizations have incorporated environmental sustainability into their long term goals and commitments. Agilent helps scientists reach their sustainability goals without compromising results or productivity, with products developed with the environment in mind. The Accountability, Consistency, and Transparency Label (ACT label) helps to communicate the environmental impact of manufacturing, operation, and disposal of scientific products and packaging, helping scientists make green equipment decisions. Agilent Vaya handheld Raman spectrometers are ACT Label certified and enable zero-waste workflows, offering a green choice for raw material identification workflows.

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.

Raman spectroscopy is a rapidly expanding field, with modern Raman spectrometers offering labs higher ease of use and sensitivity. Furthermore, combining Raman spectroscopy with scanning electron microscopy (SEM) and fluorescence-lifetime imaging microscopy (FLIM) can enhance the technique for various applications.

Potassium bromate as an oxidizing agent in bread production is a staple, but it also poses health risks due to its classification as a carcinogen, necessitating careful monitoring of its residual levels. This application note reviews the spectrophotometric determination of potassium bromate concentration in bread based on the redox reaction between potassium bromate and promethazine in an acidic environment.

This Agilent application note details the use of a Cary 5000 UV-Vis-NIR spectrophotometer with a universal measurement accessory for the optical characterization and reverse-engineering of thin films. It emphasizes the importance of multi-angle spectral photometric data in assessing the optical parameters of thin films, which is crucial for quality control in manufacturing processes.

Learn how to apply a ‘Total Workflow’ approach to sample preparation for elemental analysis. Leading to increased lab productivity, better data quality, lower costs, and improved safety.