Discovery OMICS and Progenesis QI for Food Authenticity Testing

Library Number:
WEBC134826798
Author(s):
Robert Tonge and Gareth Cleland
Source:
Hosted by spectroscopyNOW.com
Content Type:
On Demand Webinar
Content Subtype:
Laboratory Informatics and Software
Related Products:
 
 
 
 
Hosted by spectroscopyNOW.com
 
Presenters:

Robert Tonge, Ph.D.
Senior Product Manager, OMICS Informatics
Waters Corporation
 
Gareth Cleland, Ph.D.
Principal Scientist, Food & Environmental Business
Waters Corporation
 
Overview:
There is increased concern regarding the authenticity of basmati rice throughout the world. For years, traders have been passing off a lesser quality rice as the world's finest long-grained, aromatic rice, basmati, in key markets like the US, Canada and the EU.

In this on-demand webinar, we describe a proof of principle method to assess the authenticity of basmati rice using the latest advancements in high resolution GC-MS and informatics technologies. Volatile compounds of interest were extracted from heated dry rice via SPME and headspace. Following a generic GC separation, detection was performed using a SYNAPT G2-Si MS run in HDMSE mode. Collection of a HDMSE dataset offers a high level of specificity owing to the orthogonal nature of the ion mobility separation to the GC separation.

Progenesis QI, the latest OMICS informatics package from Waters, is designed to utilize the 4-dimensional data produced during a SYNAPT G2-Si HDMSE acquisition. Initially, alignment of all injections was performed followed by the unique peak co-detection process resulting in the same number of analyte measurements in every sample and no missing values. Data from all isotopes and adducts of a parent compound were then deconvoluted giving a single robust and accurate quantitative measurement for that parent compound. The peak picking and deconvolution algorithms ensure a high quality compound table where adducts and isotopes are grouped with the parent m/z. Compounds of interest from the rice samples were highlighted using various multivariate statistical techniques such as PCA and identified using elucidation tools and relevant database searches within the software platform.

This workflow has general applicability to many areas of discovery OMICS and food research where biomarkers differentiating between two or multiple groups are required.