HTC-15 - Abstract

Abstract Title: All Ion Differential Analysis in Product Control Applications using GC/MS and Comprehensive GCxGC/MS
Abstract Type: Seminar
Presenter Name: Dr Marco Ruijken
Company/Organisation: MsMetrix
Session Choice: Advanced Analysis of Food and Beverages

Abstract Information :

Many applications in industry, using GC/MS or Comprehensive GCxGC/MS, relate to finding differences between a newly measured Sample and a so-called Reference Sample. These questions may typically arise in application areas like Product Control or during Trouble Shooting. Examples are: what new impurities are present in a new batch compared to a reference batch, or why does this product behave differently compared to our reference batch, or the comparison of samples in Food Fraud applications to detect illegally added substances. Typically for the above examples is the limited time available to solve these problems. Furthermore, most of the time only a few samples are available, which excludes the use of statistical comparison tools as applied in the field of Metabolomics.

Although GCxGC-MS has become an invaluable laboratory analysis tool, the procedure may produce gigabytes of data per sample in four dimensions, which makes data analysis time consuming and complicated. In the presentation new methods and software tools will be presented to quickly find differential components from a comparison between two samples only, applicable to GC/MS and GCxGC/MS.

Certainly, comprehensive GCxGC/MS is a technique having superior separation capabilities compared to 1-dimensional GC/MS, but co-elution or near co-elution still might occur, especially in complicated matrices. Whereas most software tools for GCxGC/MS use processing of "TIC" data only, our new methods apply data analysis using the "All Ions" approach. The implemented method allows for the detection and deconvolution of differential components that are not or badly separated, even in two dimensions. It will be demonstrated that processing using the "All Ion" approach will substantially detect more (differential) components, compared to the analysis using TIC data only.

Technical details of the algorithms will be explained and examples will be given from applications like Food Analysis, Product Control in Flavor & Fragrance industry and from Base Chemistry industry.