Fractional Factorial Design of MALDI-TOF-MS Sample Preparations for the Optimized Detection of Phospholipids and Acylglycerols

AlMasoud, N, Correa, ES ORCID:, Trivedi, DK and Goodacre, R 2016, 'Fractional Factorial Design of MALDI-TOF-MS Sample Preparations for the Optimized Detection of Phospholipids and Acylglycerols' , Analytical Chemistry, 88 (12) , pp. 6301-6308.

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Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) has successfully been used for the analysis of high molecular weight compounds, such as proteins and nucleic acids. By contrast, analysis of low molecular weight compounds with this technique has been less successful due to interference from matrix peaks which have a similar mass to the target analyte(s). Recently, a variety of modified matrices and matrix additives have been used to overcome these limitations. An increased interest in lipid analysis arose from the feasibility of correlating these components with many diseases, e.g. atherosclerosis and metabolic dysfunctions. Lipids have a wide range of chemical properties making their analysis difficult with traditional methods. MALDI-TOF-MS shows excellent potential for sensitive and rapid analysis of lipids, and therefore this study focuses on computational-analytical optimization of the analysis of five lipids (4 phospholipids and 1 acylglycerol) in complex mixtures using MALDI-TOF-MS with fractional factorial design (FFD) and Pareto optimality. Five different experimental factors were investigated using FFD which reduced the number of experiments performed by identifying 720 key experiments from a total of 8064 possible analyses. Factors investigated included the following: matrices, matrix preparations, matrix additives, additive concentrations, and deposition methods. This led to a significant reduction in time and cost of sample analysis with near optimal conditions. We discovered that the key factors used to produce high quality spectra were the matrix and use of appropriate matrix additives.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Analytical Chemistry
Publisher: ACS Publications
ISSN: 0003-2700
Depositing User: Dr Elon Correa
Date Deposited: 07 Feb 2017 11:01
Last Modified: 16 Feb 2022 18:10

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