HTC-15 - Abstract

Abstract Title: Data to decision: efficient processing of complex petroleomics data
Abstract Type: Seminar
Session Choice: Big Data Chemometrics and Method Development(In-Silico)(KVCV)
Presenter Name: Mr Samuel Ellick
Co-authors:Dr Paul Gates
Dr Christopher Arthur
Dr Samuel Whitmarsh
Dr Christianne Wickking
Company/Organisation: University of Bristol
Country: United Kingdom

Abstract Information :

The information age is truly upon us and concepts like big data are becoming commonplace in both academic and industrial settings. In the analytical chemistry space the advancement of increasingly sophisticated instruments means more data from our samples, and with it, a larger requirement for effective data handling. This presentation discusses relevant data processing techniques for handling large complicated data sets demonstrated on one of the world's most complex samples: petroleum. High resolution mass spectrometry analysis of petroleum can yield in excess of 10,000 signals per spectra; this data is often of little value in its raw form, and so only hyphenating with advanced data handling and visualisation strategies can we generate insight and answer our difficult questions.

"Python" is a versatile and relatively easy to use object orientated programming language. Python scripts can interface with analysis software packages like KNIME and JMP, separating the user from the complexity of coding whilst automating file handling and visualisation. With this combined ensemble of tools, it is possible to, filter, collate, recalibrate and assign HRMS petroleomics data enabling enhanced visualisations and access to multivariate analysis/statistics.

From this presentation I will demonstrate how powerful these techniques can be and how accessible they are. The complexity of the questions we ask our data and speed at which we expect answers to be returned is ever increasing. Analytical chemists in the 21st century therefore need to be increasingly literate in such data processing tools. This presentation aims to highlight this need using practical, real-world examples.