A multivariate approach for high throughput pectin profiling by combining glycan microarrays with monoclonal antibodies

• 359 pectin samples were successfully printed, probed and analysed using glycan microarray and monoclonal antibodies technology.
• Chemometric statistical models were applied to the dataset obtained, which comprised pectin chemical characteristics (DM and DB) and antibody binding patterns.
• There was a strong correlation specifically between DM and HG binding antibodies, JIM5, LM18 and LM20.
• The created models could be used for the prediction of new DM values from microarray datasets.

Pectin–one of the most complex biomacromolecules in nature has been extensively studied using various techniques. This has been done so in an attempt to understand the chemical composition and conformation of pectin, whilst discovering and optimising new industrial applications of the polymer. For the last decade the emergence of glycan microarray technology has led to a growing capacity of acquiring simultaneous measurements related to various carbohydrate characteristics while generating large collections of data. Here we used a multivariate analysis approach in order to analyse a set of 359 pectin samples probed with 14 different monoclonal antibodies (mAbs). Principal component analysis (PCA) and partial least squares (PLS) regression were utilised to obtain the most optimal qualitative and quantitative information from the spotted microarrays. The potential use of microarray technology combined with chemometrics for the accurate determination of degree of methyl-esterification (DM) and degree of blockiness (DB) was assessed.

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