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Clustering analysis of different hop varieties according to their essential oil composition measured by GC/MS

  • Paulius Kaškonas , Žydrūnas Stanius , Vilma Kaškonienė EMAIL logo , Kȩestutis Obelevičius , Ona Ragažinskienė , Antanas Žilinskas and Audrius Maruška
Published/Copyright: September 28, 2016
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Abstract

This study describes the analysis of total hops essential oils from 18 cultivated varieties of hops, five of which were bred in Lithuania, and 7 wild hop forms using gas chromatography-mass spectrometry. The study sought to organise the samples of hops into clusters, according to 72 semi-volatile compounds, by applying a well-known method, k-means clustering analysis and to identify the origin of the Lithuanian hop varieties. The bouquet of the hops essential oil was composed of various esters, terpenes, hydrocarbons and ketones. Monoterpenes (mainly β-myrcene), sesquiterpenes (dominated by β-caryophyllene and α-humulene) and oxygenated sesquiterpenes (mainly caryophyllene oxide and humulene epoxide II) were the main compound groups detected in the samples tested. The above compounds, together with α-muurolene, were the only compounds found in all the samples. Qualitative and quantitative differences were observed in the composition of the essential oils of the hop varieties analysed. For successful and statistically significant clustering of the data obtained, expertise and skills in employing chemometric analysis methods are necessary. The result is also highly dependent on the set of samples (representativeness) used for segmentation into groups, the technique for pre-processing the data, the method selected for partitioning the samples according to the similarity measures chosen, etc. To achieve a large and representative data set for clustering analysis from a small number of measurements, numerical simulation was applied using the Monte Carlo method with normal and uniform distributions and several relative standard deviation values. The grouping was performed using the k-means clustering method, employing several optimal number of clusters evaluation techniques (Davies-Bouldin index, distortion function, etc.) and different data pre-processing approaches. The hop samples analysed were separated into 3 and 5 clusters according to the data filtering scenario used. However, the targeted Lithuanian hop varieties were clustered identically in both cases and fell into the same group together with other cultivated hop varieties from Ukraine and Poland.

Authors’ contribution

P. Kaskonas performed statistical data mining, interpretation of the clustering analysis results and prepared the section on chemometric analysis. V. Kaškonienė was responsible for the qualitative and quantitative data analysis and preparation of the chemical part. Z. Stanius performed GC/MS analysis and identification of the semi-volatile compounds. O. Ragažinskiene was responsible for collection of the hops and preparation of the samples’ material. K. Obelevičius prepared the essential oils using hydrodistillation. A. Maruška and A. Zilinskas assisted in the experimental design and interpretation of the results.

Supplementary data

Supplementary data associated with this article can be found in the online version of this paper (DOI: 10.1515/chempap-2016-0092).

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Received: 2015-12-1
Revised: 2016-5-3
Accepted: 2016-5-3
Published Online: 2016-9-28
Published in Print: 2016-12-1

© 2016 Institute of Chemistry, Slovak Academy of Sciences

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