Literature DB >> 22394515

Development and validation of a high-performance liquid chromatography method for the determination of diacetyl in beer using 4-nitro-o-phenylenediamine as the derivatization reagent.

Pingliang Li1, Yuncong Zhu, Shun He, Jiqiao Fan, Qiongbo Hu, Yongsong Cao.   

Abstract

Diacetyl is a natural byproduct of fermentation and known to be an important flavor compound in many food products. Because of the potential undesirable effects of diacetyl on health safety and beer flavor, determination of its concentration in beer samples is essential and its analytical methods have attracted close attention recently. The aim of the present work is to develop and validate a novel high-performance liquid chromatography method for the quantification of diacetyl in beer based on the derivatization reaction of diacetyl with 4-nitro-o-phenylenediamine (NPDA). After the derivatization with NPDA in pH 3.0 at 45 °C for 20 min, diacetyl was separated on a kromasil C(18) column at room temperature in the form of the resulting 6-nitro-2,3-dimethylquinoxaline and detected by the ultraviolet detector at 257 nm. The results showed that the correlation coefficient for the method was 0.9992 in the range of 0.0050-10.0 mg L(-1) and the limit of detection was 0.0008 mg L(-1) at a signal-to-noise ratio of 3. The applicability of the proposed method was evaluated in the analysis of beer samples with the recovery range of 94.0-99.0% and relative standard deviation range of 1.20-3.10%. The concentration levels of diacetyl detected in beer samples from 12 brands ranged from 0.034 to 0.110 mg L(-1). The proposed method showed efficient chromatographic separation, excellent linearity, and good repeatability that can be applied to quantification of diacetyl in beer samples.

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Year:  2012        PMID: 22394515     DOI: 10.1021/jf3007163

Source DB:  PubMed          Journal:  J Agric Food Chem        ISSN: 0021-8561            Impact factor:   5.279


  1 in total

1.  Analysis of α-dicarbonyl compounds and volatiles formed in Maillard reaction model systems.

Authors:  Jiyoon Cha; Trishna Debnath; Kwang-Geun Lee
Journal:  Sci Rep       Date:  2019-03-29       Impact factor: 4.379

  1 in total

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