Cerno Bioscience releases GC/ID V1.1 software for highly confident GC/MS compound ID

Cerno Bioscience announced today that it has released version 1.1 of its innovative MassWorks Rx GC/ID software. GC/ID is a fully automated data processing software that enables dramatic improvements for GC/MS qualitative analysis. By incorporating Cerno’s proven TrueCal™ calibration technology for accurate mass formula ID along with conventional NIST library search, a significant improvement in compound ID certainty is achieved on single quad GC/MS systems. GC/ID also provides an entirely automated method of quantitatively utilizing the NIST retention index values to provide yet a third orthogonal metric for compound ID. Finally, a powerful new approach is used to identify and deconvolve mixtures and the background of co-eluting peaks to minimize the misidentification of compounds in complex samples.

Cerno Bioscience releases GC/ID V1.1 software for highly confident GC/MS compound ID

The software can be set up to automatically process an entire sequence of GC/MS runs from most vendor instruments including the popular Agilent GC/MSD. An entire run can be analyzed in a few minutes including spectral and chromatogram peak calibration, peak picking/deconvolution, and subsequent analysis of every peak by NIST search, accurate mass/spectral accuracy formula ID, retention index fit, and final report generation. The three metrics can be “blended” into a new overall match quality or used independently to deliver dramatic compound ID improvements and valuable time savings.  

New in V1.1 is the ability to perform accurate mass/spectral accuracy identification of the major fragment ions as well as the molecular ion. This further improves the confidence of compound ID.  GC/ID can now use calculated retention time index values to extend the retention time libraries for compounds in the NIST or user libraries. Further improvements have also been made to the powerful peak deconvolution algorithms and report generation is now more flexible than ever.

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