Automatic peak assignment and visualisation of copolymer mass spectrometry data using the ‘genetic algorithm’

Abstract

Copolymer analysis is vitally important as the materials have a wide variety of applications due to their tunable properties. Processing mass spectrometry data for copolymer samples can be very complex due to the increase in the number of species when the polymer chains are formed by two or more monomeric units. In this paper, we describe the use of the genetic algorithm for automated peak assignment of copolymers synthesised by a variety of polymerisation methods. We find that in using this method we are able to easily assign copolymer spectra in a few minutes and visualise them into heat maps. These heat maps allow us to look qualitatively at the distribution of the chains, by showing how they alter with different polymerisation techniques, and by changing the initial copolymer composition. This methodology is simple to use and requires little user input, which makes it well suited for use by less expert users. The data outputted by the automatic assignment may also allow for more complex data processing in the future.

Rapidly self-deoxygenating controlled radical polymerization in water via in situ disproportionation of Cu(i)

“…S9 †). These chain termination events are also observed under conventionally deoxygenated systems 56,61,62 originating from the excess of water where the rate of hydrolysis is lower than the rate of chain propagation, thus they cannot be correlated only with the presence or absence of oxygen. However, the analysis of the 1 H NMR spectra of both PNiPAm which was analyzed through MALDI and the extreme case of PNiPAm synthesized in open air, showed no peaks corresponding to vinyl protons originating from elimination (Fig.…”

Genetic algorithm parallel optimization of a new high mass resolution planar electrostatic ion trap mass analyzer

“…Genetic algorithm (GA) is an artificial intelligence technique capable of performing heuristic searches in a naturally evolving manner, and it has been shown to be effective and efficient in dealing with difficult problems in searching landscapes with local maxima or minima without excessive manual parameter tuning, which is suitable for searching in multiple dimensions simultaneously. 21,22 Sysoev reported the use of GA for voltage optimization of gridless ion mirrors and demonstrated its potential in optimizing a TOF mass analyzer with seven electrodes. 23 Wurz et al modified the GA to enable the in situ self-optimization of a reflectron-TOF mass spectrometer.…”

Genetic Algorithm Optimized Printed Circuit Board Ion Funnel Tandem Subambient Pressure Ionization with Nanoelectrospray (SPIN) for High Sensitivity Mass Spectrometry

“…A genetic algorithm (GA) is a smart artificial intelligence algorithm. A GA was proven to be relatively effective in processing with search issues to find extreme values, which saves tedious tuning time. , Sysoev reports on the optimization of a time-of-flight mass analyzer with seven electrodes using a GA . We have used the GA to parallelly optimize a planar electrostatic ion trap mass analyzer with four electrode variables, resulting in the highest mass resolution .…”

“…A GA was proven to be relatively effective in processing with search issues to find extreme values, which saves tedious tuning time. 25,26 Sysoev reports on the optimization of a time-of-flight mass analyzer with seven electrodes using a GA. 27 We have used the GA to parallelly optimize a planar electrostatic ion trap mass analyzer with four electrode variables, resulting in the highest mass resolution. 28 Therefore, the GA has great value in parameter optimization of a multivariable system and should be explored for optimizing ion transmission in IF.…”