World Journal of Analytical Chemistry
ISSN (Print): 2333-1178 ISSN (Online): 2333-1283 Website: Editor-in-chief: Raluca-Ioana Stefan-van Staden
Open Access
Journal Browser
World Journal of Analytical Chemistry. 2013, 1(4), 69-72
DOI: 10.12691/wjac-1-4-5
Open AccessArticle

Untargetted Metabolomics in Assessment of Variations among Kenyan Arabica Coffee Genotypes Using Organic Compounds in the Brew

Cecilia Kathurima1, and Fredrick Ng’ang’a1

1Coffee Research Foundation Ruiru-Kenya

Pub. Date: November 27, 2013

Cite this paper:
Cecilia Kathurima and Fredrick Ng’ang’a. Untargetted Metabolomics in Assessment of Variations among Kenyan Arabica Coffee Genotypes Using Organic Compounds in the Brew. World Journal of Analytical Chemistry. 2013; 1(4):69-72. doi: 10.12691/wjac-1-4-5


Genotypical variations between two commercial coffee cultivars considered as controls (Ruiru 11 and SL28) and five other genotypes under investigation (Cr8, Cr22, Cr23, Cr27 and Cr30) all grown under similar conditions were evaluated. Organic compounds were extracted using C18 Solid Phase Extraction (SPE) cartridges and analyzed by Gas Chromatography- Mass Spectrometry. Untargeted metabolomics data processing of the mass spectra was carried out on XCMS online (a web based platform). The analysis revealed absence of any significant variation in composition of the organic compounds at p = 0.05. However, from the Principal Component Analysis plots the genotypes Cr27, Cr22 and Cr23 clustered close together with Ruiru 11 while the other genotypes showed no distinct clustering pattern. Overall, there was no significant variation in the organic compounds composition across the coffee genotypes grown under similar agronomic practices in the same region and processed uniformly.

untargeted metabolomics XCMS online organic compounds genotypes coffee

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit


Figure of 2


[1]  Tsugawa, H., Tsujimoto, Y., Arita, M, Bamba, T. and Fukusaki, E. GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA). BMC Bioinformatics, 12 (131). 1-13. 2011.
[2]  Cevallos-Cevallos J., Reyes-De-Corcuera J., Etxeberria E, Danyluk M., and Rodrick G. Metabolomic analysis in food science: a review. Trends in Food Science and Technology, 20. 557-566. 2009.
[3]  Roessner-Tunali, U., Beale M. H., Trethewey R. N., Lange B. M., Wurtele, E. S. and Sumner L. Potential of metabolomics as a functional genomics tool. Trends Plant Science. 9(9):418-425, 2004.
[4]  Wishart D. S. Current progress in computational metabolomics. Briefs in Bioinformatics, 8(5):279-293, 2007.
[5]  Ramautar, R., Demirci, A., and Jong, G. J.d. Capillary electrophoresis in metabolomics. Trends in Analytical Chemistry, 25(5), 12. (2006).
[6]  Vinayavekhin, N. and Saghatelian, A. Untargeted metabolomics. Current Protocols in Molecular Biology. 90 (30) 1-24 .2010.
[7]  Monton, M. and Soga, T. Metabolome analysis by capillary electrophoresise mass spectrometry. Journal of Chromatography 1168 (1), 237-246. 2007.
[8]  Kemsley, E. K., Le Gall, G., Dainty, J. R., Watson, A. D., Harvey, L. J., Tapp, H. S., Multivariate techniques and their application in nutrition: a metabolomics case study. British Journal of Nutrition, 98 (1), 1-14. (2007).
[9]  Son, H. S., Kim, K. M., Van den Berg, F., Hwang, G. S., Park, W. M., Lee, C. H., H-1 nuclear magnetic resonance-based metabolomic characterization of wines by grape varieties and production areas. Journal of Agricultural and Food Chemistry, 56(17), 8007-8016.
[10]  Ikeda, T., Kanaya, S., Yonetani, T., Kobayashi, A. and Fukusaki, E. Prediction of Japanese green tea ranking by fourier transform near-infrared reflectance spectroscopy. Journal of Agricultural and Food Chemistry, 55. 9908-9912 (2007).
[11]  Cavaliere, B., De Nino, A., Hayet, F., Lazez, A., Macchione, B., and Moncef, C., (2007). A metabolomic approach to the evaluation of the origin of extra virgin olive oil: a convenient statistical treatment of mass spectrometric analytical data. Journal of Agricultural and Food Chemistry, 55(4), 1454-1462.
[12]  Donarski, J. A., Jones, S. A., and Charlton, A. J. (2008). Application of cryoprobe H-1 nuclear magnetic resonance spectroscopy and multivariate analysis for the verification of Corsican honey. Journal of Agricultural and Food Chemistry, 56(14), 5451-5456.
[13]  Beckmann, M., Enot, D. P., Overy, D. P. and Draper, J. (2007). Representation, comparison, and interpretation of metabolome finger print data for total composition analysis and quality trait investigation in potato cultivars. Journal of Agricultural and Food Chemistry, 55 (9), 3444-3451.
[14]  Denkert C., Budczies J., Kind T., Weichert W., Tablack P., Sehouli J., Niesporek S., Könsgen D., Dietel M. and Fiehn O. Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors. Cancer research, 66. 10795-10804. 2006.
[15]  Katajama, M. and Orešič, M., Data processing for mass spectrometry-based metabolomics. Journal of chromatography, 1158, 318-328, (2007).
[16]  Patti, G., Yanes, O., Shriver, L., Courade, J., Tautenhahn, R., Manchester, M. and Siuzdak, G. Nature Chemical Biology, 8 (3), 232-234. 2012.
[17]  Jaetzold, R. and Schmidt H. (1993). Farm Management Handbook of Kenya Vol. IIB Natural conditions and farm management information Central Kenya, Published by Ministry of Agriculture Kenya in cooperation with the German Agricultural Team (GAT) of the German Agency for Technical Cooperation GTZ.
[18]  Lingle, T.R., The Cuppers Handbook. Systematic Guide to the Sensory Evaluation of Coffee`s Flavour, Third edition 2001.
[19]  Mburu, J.K. The current recommendations for the processing of high quality and safe coffee in Kenya. In the Proceedings of 20th International Scientific Colloquium on Coffee. Bangalore India. 509-512. 2004.
[20]  Kathurima C., Kenji G., Muhoho S., Boulanger R. and Ng’ang’a F. Comparative study of genotypical variation of volatile organic composition in brewed Kenyan coffee through solid phase extraction gas chromatography mass spectrometry. Food Science and Quality Management. 8, 18-26. (2012).