* Corresponding Author
Metabolite Profile Evaluation of Indonesian Roasted Robusta Coffees by 1H NMR Technique and Chemometrics
https://doi.org/10.22146/ijc.46492 Nizar Happyana(1*), Elvira Hermawati(2), Yana Maolana Syah(3), Euis Holisotan Hakim(4)
(1) Organic Chemistry Division, Chemistry Study Program, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Jl. Ganesha No. 10, Bandung 40132, West Java, Indonesia
(2) Organic Chemistry Division, Chemistry Study Program, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Jl. Ganesha No. 10, Bandung 40132, West Java, Indonesia
(3) Organic Chemistry Division, Chemistry Study Program, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Jl. Ganesha No. 10, Bandung 40132, West Java, Indonesia
(4) Organic Chemistry Division, Chemistry Study Program, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Jl. Ganesha No. 10, Bandung 40132, West Java, Indonesia
(*) Corresponding Author
Abstract
In this work, 1H NMR analysis, along with a chemometrics approach, had been applied for investigating metabolite profiles of Indonesian roasted Robusta coffees obtained from Lampung and Aceh. In total, 24 compounds had been successfully detected in the 1H NMR spectra of the Robusta coffee extracts. Concentrations of some identified metabolites present in the coffees were determined by the quantitative 1H NMR technique. Orthogonal projection to latent structure-discriminant analysis (OPLSDA) was used as a primary method for the chemometric approach. OPLSDA had classified clearly the Robusta coffee samples corresponding to their origin. Loading plot and S-plot of the OPLSDA revealed characteristic metabolites for each Robusta coffee. The results indicated that quinic acid, mannose, arabinoses, and acetic acid were an important discriminant compound for Lampung Robusta coffees. Meanwhile, lipids, lactic acid, and 5-caffeoylquinic acid were found as characteristic metabolites for Aceh Robusta coffee. This report provided knowledge about the chemical composition of Lampung and Aceh Robusta coffees and shed more light on the diversity of Indonesian Robusta coffees. Furthermore, it confirmed that 1H NMR analysis coupled with chemometrics was a powerful method for evaluating and classifying metabolite profiles of the roasted Robusta coffees.
Keywords
Full Text:
Full Text PDFReferences
[1] Haile, M., and Kang, W.H., 2019, The role of microbes in coffee fermentation and their impact on coffee quality, J. Food Qual., 2019, 4836709.
[2] Lashermes, P., and Anthony, F., 2007, “Coffee” in Technical Crops, Eds. Kole, C., Springer, Berlin, 109–118.
[3] Farah, A., 2012, “Coffee constituents” in Coffee: Emerging Health Effects and Disease Prevention, Eds. Chu, Y.F., John Wiley and Sons Inc., New Jersey, 21–58.
[4] Alves, M.R., Casal, S., Oliveira, M.B.P.P., and Ferreira, M.A., 2003, Contribution of FA profile obtained by high-resolution GC/chemometric techniques to the authenticity of green and roasted coffee varieties, J. Am. Oil Chem. Soc., 80 (6), 511–517.
[5] Cossignani, L., Montesano, D., Simonetti, M.S., and Blasi, F., 2016, Authentication of Coffea arabica according to triacylglycerol stereospecific composition, J. Anal. Methods Chem., 2016, 7482620.
[6] Downey, G., and Spengler, B., 1996, Compositional analysis of coffee blends by near infrared spectroscopy, Ir. J. Agric. Food Res., 35 (2), 179–188.
[7] El-Abassy, R.M., Donfack, P., and Materny, A., 2011, Discrimination between Arabica and Robusta green coffee using visible micro-Raman spectroscopy and chemometric analysis, Food Chem., 126 (3), 1443–1448.
[8] Suhandy, D., and Yulia, M., 2017, Peaberry coffee discrimination using UV-visible spectroscopy combined with SIMCA and PLS-DA, Int. J. Food Prop., 20, S331–S339.
[9] Alonso-Salces, R.M., Serra, F., Reniero, F., and Héberger, K., 2009, Botanical and geographical characterization of green coffee (Coffea arabica and Coffea canephora): Chemometric evaluation of phenolic and methylxanthine contents, J. Agric. Food Chem., 57 (10), 4224–4235.
[10] Wei, F., Furihata, K., Koda, M., Hu, F., Kato, R., Miyakawa, T., and Tanokura, M., 2012, 13C NMR-based metabolomics for the classification of green coffee beans according to variety and origin, J. Agric. Food Chem., 60 (40), 10118–10125.
[11] Zambonin, C.G., Balest, L., De Benedetto, G.E., and Palmisano, F., 2005, Solid-phase microextraction–gas chromatography mass spectrometry and multivariate analysis for the characterization of roasted coffees, Talanta, 66 (1), 261–265.
[12] Okubo, N., and Kurata, Y., 2019, Nondestructive classification analysis of green coffee beans by using Near-Infrared spectroscopy, Foods, 8 (2), 82.
[13] Downey, G., Briandet, R., Wilson, R.H., and Kemsley, E.K., 1997, Near- and mid-infrared spectroscopies in food authentication: Coffee varietal identification, J. Agric. Food Chem., 45 (11), 4357–4361.
[14] Dong, W., Zhao, J., Hu, R., Dong, Y., and Tan, L., 2017, Differentiation of Chinese Robusta coffees according to species, using a combined electronic nose and tongue, with the aid of chemometrics, Food Chem., 229, 743–751.
[15] Dong, W., Tan, L., Zhao, J., Hu, R., and Lu, M., 2015, Characterization of fatty acid, amino acid and volatile compound compositions and bioactive components of seven coffee (Coffea robusta) cultivars grown in Hainan province, China, Molecules, 20 (9), 16687–16708.
[16] Liu, C., Yang, Q., Linforth, R., Fisk, I.D., and Yang, N., 2019, Modifying Robusta coffee aroma by green bean chemical pre-treatment, Food Chem., 272, 251–257.
[17] ICO (International Coffee Organization), 2019, Coffee Report Market April 2019, http://www.ico.org/documents/cy2018-19/cmr-0419-e.pdf, accessed on 17 May 2019.
[18] Wei, F., Furihata, K., Hu, F., Miyakawa, T., and Tanokura, M., 2011, Two-dimensional 1H-13C nuclear magnetic resonance (NMR)-based comprehensive analysis of roasted coffee bean extra, J. Agric. Food Chem., 59 (17), 9065–9073.
[19] Wei, F., Furihata, K., Miyakawa, T., and Tanokura, M., 2014, A pilot study of NMR-based sensory prediction of roasted coffee bean extracts, Food Chem., 152, 363–369.
[20] D’Amelio, N., Fontanive, L., Uggeri, F., Suggi-Liverani, F., and Navarini, L., 2009, NMR reinvestigation of the caffeine–chlorogenate complex in aqueous solution and in coffee brews, Food Biophys., 4 (4), 321–330.
[21] Cagliani, L.R., Pellegrino, G., Giugno, G., and Consonni, R., 2013, Quantification of Coffea arabica and Coffea canephora var. Robusta in roasted and ground coffee blends, Talanta, 106, 169–173.
[22] Consonni, R., Cagliani, L.R., and Cogliati, C., 2012, NMR based geographical characterization of roasted coffee, Talanta, 88, 420–426.
[23] Tavares, L., and Ferreira, A.G., 2006, Quali- and quantitative analysis of commercial coffee by NMR, Quím. Nova, 29 (5), 911–915.
[24] Wei, F., Furihata, K., Koda, M., Hu, F., Miyakawa, T., and Tanokura, M., 2012, Roasting process of coffee beans as studied by nuclear magnetic resonance: Time course of changes in composition, J. Agric. Food Chem., 60 (4), 1005–1012.
[25] Bylesjö, M., Rantalainen, M., Cloarec, O., Nicholson, J.K., Holmes, E., and Trygg, J., 2006, OPLS discriminant analysis: Combining the strengths of PLS-DA and SIMCA classification, J. Chemom., 20, 341–351.
[26] Villarreal, D., Laffargue, A., Posada, H., Bertrand, B., Lashermes, P., and Dussert, S., 2009, Genotypic and environmental effects on coffee (Coffea arabica L.) bean fatty acid profile: Impact on variety and origin chemometric determination, J. Agric. Food Chem., 57 (23), 11321–11327.
[27] Buffo, R.A., and Cardelli-Freire, C., 2004, Coffee flavour: An overview, Flavour Fragr. J., 19 (2), 99–104.
DOI: https://doi.org/10.22146/ijc.46492
Article Metrics
Abstract views : 3634
| 
views : 2809
Copyright (c) 2019 Indonesian Journal of Chemistry
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Indonesian Journal of Chemistry (ISSN 1411-9420 /e-ISSN 2460-1578) - Chemistry Department, Universitas Gadjah Mada, Indonesia.