Final Activity Report Summary - CHEVAR (The chemical variance: building new bridges between mathematics and chemistry)
The current project has made a significant advance in several fronts in the field of optimisation and data treatment of two-dimensional chromatography. A protocol has been developed for optimisation of flow-rates, column dimensions and modulation time in LCxLC. On the other, a new resolution metric (able to deal with non-ideal situations) has been developed and tested. This new resolution metric correlates with the chromatographer's appraisal when the quality of a chromatogram is evaluated.
In the field of data treatment, a new concept, called "Chemical Variance", has been developed. This technique is aimed to facilitate the analysis of families of compounds in two-dimensional chromatography. Also, a method for peak detection in two-dimensional chromatography has been developed, and applied to both gas and liquid two-dimensional chromatography modes. Applications of this method have been found on the fields of proteomics and food analysis.
Also, significant advancements have been made in the field of automation of data treatment, aimed to facilitate the data digestion produced in high-throughput experiments. More specifically, the method automates multivariate curve resolution techniques, used to separate the signals from individual compounds in second-order instruments.
In the field of data treatment, a new concept, called "Chemical Variance", has been developed. This technique is aimed to facilitate the analysis of families of compounds in two-dimensional chromatography. Also, a method for peak detection in two-dimensional chromatography has been developed, and applied to both gas and liquid two-dimensional chromatography modes. Applications of this method have been found on the fields of proteomics and food analysis.
Also, significant advancements have been made in the field of automation of data treatment, aimed to facilitate the data digestion produced in high-throughput experiments. More specifically, the method automates multivariate curve resolution techniques, used to separate the signals from individual compounds in second-order instruments.