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Simultaneous measurement of two enzyme activities using infrared spectroscopy: A comparative evaluation of PARAFAC, TUCKER and N-PLS modeling

•Simultaneous enzyme activity determination of two co-acting enzymes.•Activity calibration and validation for pectin lyase and pectin methyl esterase.•Chemometric multiway modeling comparing PARAFAC, TUCKER3 and N-PLS.•Kinetic investigations on genuine substrates as pectin.•Universally applicable wi...

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Bibliographic Details
Published in:Analytica chimica acta 2013-08, Vol.790, p.14-23
Main Authors: Baum, Andreas, Hansen, Per Waaben, Meyer, Anne S., Mikkelsen, Jørn Dalgaard
Format: Article
Language:English
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Summary:•Simultaneous enzyme activity determination of two co-acting enzymes.•Activity calibration and validation for pectin lyase and pectin methyl esterase.•Chemometric multiway modeling comparing PARAFAC, TUCKER3 and N-PLS.•Kinetic investigations on genuine substrates as pectin.•Universally applicable without using any external standards or (bio-)markers. Enzymes are used in many processes to release fermentable sugars for green production of biofuel, or the refinery of biomass for extraction of functional food ingredients such as pectin or prebiotic oligosaccharides. The complex biomasses may, however, require a multitude of specific enzymes which are active on specific substrates generating a multitude of products. In this paper we use the plant polymer, pectin, to present a method to quantify enzyme activity of two pectolytic enzymes by monitoring their superimposed spectral evolutions simultaneously. The data is analyzed by three chemometric multiway methods, namely PARAFAC, TUCKER3 and N-PLS, to establish simultaneous enzyme activity assays for pectin lyase and pectin methyl esterase. Correlation coefficients Rpred2 for prediction test sets are 0.48, 0.96 and 0.96 for pectin lyase and 0.70, 0.89 and 0.89 for pectin methyl esterase, respectively. The retrieved models are compared and prediction test sets show that especially TUCKER3 performs well, even in comparison to the supervised regression method N-PLS.
ISSN:0003-2670
1873-4324
DOI:10.1016/j.aca.2013.06.039