Literature DB >> 29247609

Improved HPLC-method for estimation and correction of amino acid losses during hydrolysis of unknown samples.

Anne Lamp1, Martin Kaltschmitt2, Oliver Lüdtke3.   

Abstract

Amino acid analysis, commonly done by acid hydrolysis of proteins and HPLC analysis, faces one major problem: incomplete hydrolysis of stable amino acids and degradation of unstable amino acids are causing amino acid losses. As a result, amino acid recovery of unknown samples cannot be estimated. Some methods have been reported for correction of these factors in the past. This paper shows an improved and integrated method to overcome this problem by using stillage as an exemplary unknown sample material. Amino acid recovery from an unknown sample can be estimated by standard addition of a known protein. If the sample does not cause matrix effects during amino acid hydrolysis, recoveries of the standard protein are transferable to the sample. If the sample does cause matrix effects correction of amino acid losses can instead be done by determination of hydrolysis kinetics. Therefore, first order kinetics were used for amino acids that undergo degradation during hydrolysis. For all stable amino acids higher order kinetics were used, a novel approach to determine hydrolysis kinetics. The presented method can be a helpful tool for scientists who want to optimize amino acid analysis of a particular biomass substrate.
Copyright © 2017 Elsevier Inc. All rights reserved.

Keywords:  Amino acid analysis; HPLC; Hydrolysis kinetics; Matrix effects; Non-linear least-squares regression

Mesh:

Substances:

Year:  2017        PMID: 29247609     DOI: 10.1016/j.ab.2017.12.009

Source DB:  PubMed          Journal:  Anal Biochem        ISSN: 0003-2697            Impact factor:   3.365


  2 in total

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Authors:  Qiao Liu; Qirui Bi; Ninghua Tan
Journal:  Amino Acids       Date:  2021-10-06       Impact factor: 3.520

2.  Phenolic composition and nutritional attributes of diaphragma juglandis fructus and shell of walnut (Juglans regia L.).

Authors:  Qiang Hu; Jia Liu; Jun Li; Hui Liu; Nan Dong; Yang-Yang Geng; Yang Lu; Yan Wang
Journal:  Food Sci Biotechnol       Date:  2019-08-08       Impact factor: 2.391

  2 in total

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