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FTIR as a rapid tool for monitoring molecular weight distribution during enzymatic protein hydrolysis of food processing by-products

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Abstract

Enzymatic protein hydrolysis of food processing by-products is a well-recognized strategy for producing peptide-rich formulations of added value. The biochemical complexity of this process poses a significant challenge in the use of classical analytical methods as process monitoring tools in both laboratory and industrial setups. In the present study, we are reporting an FTIR-based multivariate approach for monitoring the change in the molecular size distribution of proteins during enzymatic hydrolysis of chicken fillets and processing by-products. For 129 protein hydrolysates, weight-average molecular weight derived from size-exclusion chromatographic analysis was established as a pragmatic measure of the extent of hydrolysis. FTIR spectra acquired from dry films of the hydrolysates were used to build multivariate calibration models using partial least squares (PLS) regression. The best predictions were obtained when the data from two different raw materials, i.e., chicken fillets and mechanical chicken deboning residues, were modeled individually. A good prediction model was also achieved for the combined data from the two raw materials using canonical PLS (CPLS) regression or variable selection. The results from the current study underline the potential of FTIR spectroscopy as a rapid analytical tool for monitoring enzymatic protein hydrolysis of complex raw materials.

Graphical abstract: FTIR as a rapid tool for monitoring molecular weight distribution during enzymatic protein hydrolysis of food processing by-products

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Publication details

The article was received on 31 Mar 2017, accepted on 18 Jun 2017 and first published on 19 Jun 2017


Article type: Paper
DOI: 10.1039/C7AY00865A
Citation: Anal. Methods, 2017, Advance Article
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    FTIR as a rapid tool for monitoring molecular weight distribution during enzymatic protein hydrolysis of food processing by-products

    S. G. Wubshet, I. Måge, U. Böcker, D. Lindberg, S. H. Knutsen, A. Rieder, D. A. Rodriguez and N. K. Afseth, Anal. Methods, 2017, Advance Article , DOI: 10.1039/C7AY00865A

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