This week we bring you application notes by Shimadzu and Thermo Fisher Scientific, poster by Agilent Technologies and presentation by University of Alberta / MDCW!

GC×GC-HRTOFMS enables precise identification of trace-level odorous compounds in complex VOC profiles, supporting material emission characterization and odor reduction strategies in recycled products.

This week we bring you application notes by Agilent Technologies and Shimadzu and presentation by University of Liege / MDCW!

Non-target screening of indoor dust revealed 2500+ contaminants, including phthalates and COVID-19-related chemicals. Samples from 4 cities show distinct pollutant profiles. Workflow under development.

This week we bring you application notes by Agilent Technologies and Shimadzu, presentation by Certech / MDCW and poster by Thermo Fisher Scientific!

Using GC×GC and statistical tools, researchers highlight differences between alternative and petroleum-based jet fuels, focusing on minor aromatics and heteroatom-containing compounds.

This week we bring you application notes by Agilent Technologies, Shimadzu and presentation by University of Alberta / MDCW!

Peak detection is crucial in GC×GC & LC×LC workflows. We study how signal features impact detection performance, helping select tools for both manual and automated method development.

The integration of chromatographic and statistical approaches in GC×GC-MS enhances compound separation, data interpretation, and reliability—crucial for complex sample analysis across many fields.

We developed an AI-based EI mass spectral database with 120 million predicted spectra to improve unknown compound identification in GC×GC-MS beyond commercial library limits.