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Issue 10, 2014
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Which spectroscopic technique allows the best differentiation of coffee varieties: comparing principal component analysis using data derived from CD-, NMR- and IR-spectroscopies and LC-MS in the analysis of the chlorogenic acid fraction in green coffee beans

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Abstract

In this contribution we have analysed aqueous methanolic extracts of a total of 38 green bean coffee samples, which vary in terms of the coffee variety and processing conditions. We have characterised these extracts using NMR-, IR- and CD-spectroscopies along with LC-MS. All spectroscopic data have been analysed by principal component analysis (PCA) using different PCA processing parameters by an unsupervised non-targeted approach. We could show that the distinction between different groups of samples, in particular, Arabica versus Robusta green coffee beans can be successfully analysed by IR-spectroscopy and LC-MS. Surprisingly both CD- and NMR-spectroscopies fail to achieve, in this case, an adequate level of distinction. This is, to the best of our knowledge, the first study that directly compares the value of various spectroscopic techniques if multivariant statistical techniques are employed to them.

Graphical abstract: Which spectroscopic technique allows the best differentiation of coffee varieties: comparing principal component analysis using data derived from CD-, NMR- and IR-spectroscopies and LC-MS in the analysis of the chlorogenic acid fraction in green coffee beans

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Publication details

The article was received on 06 Nov 2013, accepted on 07 Feb 2014 and first published on 07 Feb 2014


Article type: Paper
DOI: 10.1039/C3AY41970C
Anal. Methods, 2014,6, 3268-3276

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    Which spectroscopic technique allows the best differentiation of coffee varieties: comparing principal component analysis using data derived from CD-, NMR- and IR-spectroscopies and LC-MS in the analysis of the chlorogenic acid fraction in green coffee beans

    S. Deshpande, R. M. El-Abassy, R. Jaiswal, P. Eravuchira, B. von der Kammer, A. Materny and N. Kuhnert, Anal. Methods, 2014, 6, 3268
    DOI: 10.1039/C3AY41970C

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