Abstract
Coffee is the second most consumed beverage in the world and Brazil is the biggest coffee producer. The main coffee species are Arabica (Coffea arabica) and Robusta (Coffea canephora). Arabica presents superior sensorial characteristics, as flavor and aroma, and Robusta is less expensive to produce. Pure Arabica coffee presents a market share of 70% and Arabic and Robusta are mixed to produce blended coffee. In this work, a fast and simple method to identify Arabica and blended coffee was proposed. The samples were analyzed by Infrared Spectroscopy in the mid and near-infrared regions and the spectra were used to develop a discriminant method. Using the method, the purity varied from 99.44 to 99.94% for pure Arabica coffees. To evaluate the method, the samples were characterized by gas chromatography coupled to mass spectrometry. It was possible to identify Arabica and blended coffee with high accuracy, in one minute, without complex analyses or sample preparations. The method is useful when Arabica is blended with more than 20% of Robusta and the practical application of the method can be extended to all coffee producers and distributors to ensure quality and to identify frauds or blended coffees and pure Arabica coffees.
Graphical abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Belchior V, Botelho BG, Oliveira LS, Franca AS (2019) Attenuated total reflectance fourier transform spectroscopy (ATR-FTIR) and chemometrics for discrimination of espresso coffees with different sensory characteristics. Food Chem 273:178–185
Craig AP, Franca AS, Oliveira LS (2012) Discrimination between defective and non-defective roasted coffees by diffuse reflectance infrared Fourier transform spectroscopy. LWT Food Sci Technol 47:505–511
Garrigues JM, Bouhsain Z, Garrigues S, De La Guardia M (2000) Fourier transform infrared determination of caffeine in roasted coffee samples. Fresenius J Anal Chem 366:319–322
Hashimoto A, Mori H, Kanou M, Kameoka T, Shimazu H, Kobayashi K (2009) Mid-infrared spectroscopic characteristics of brewed coffee and simultaneous content determination of main components using an attenuated total reflection method. J Illum Eng Inst Japan 93:501–509
Hong E, Lee SY, Jeong JY, Park JM, Kim BH, Kwon K, Chun HS (2017) Modern analytical methods for the detection of food fraud and adulteration by food category. J Sci Food Agric 97:3877–3896
Huck CW, Guggenbichler W, Bonn GK (2005) Analysis of caffeine, theobromine and theophylline in coffee by near infrared spectroscopy (NIRS) compared to high-performance liquid chromatography (HPLC) coupled to mass spectrometry. Anal Chim Acta 538:195–203
International Coffee Organization (2020). www.ico.org. Accessed in September of 2020
Jeszka-Skowron M, Zgoła-Grzeskowiak A, Grzeskowiak T (2015) Analytical methods applied for the characterization and the determination of bioactive compounds in coffee. Eur Food Res Technol 240:19–31
Kath J, Byrareddy VM, Mushtaq S, Craparo A, Porcel M (2021) Temperature and rainfall impacts on robusta coffee bean characteristics. Clim Risk Manag 32:100281–100296
Kennedy SP, Gonzales P, Rongchun J (2021) Coffee and tea fraud. In: Hellberg RS, Everstine K, Sklare SA Food Fraud: a global threat with public health and economic consequences. Academic Press, Cambridge, pp. 139–150
Magalhães LM, Machado S, Segundo MA, Lopes JA (2016) Rapid assessment of bioactive phenolics and methylxanthines in spent coffee grounds by FT-NIR spectroscopy. Talanta 147:460–467
Martin MJ, Pablos F, Gonzales AG (1997) Discrimination between arabica and robusta green coffee varieties according to their chemical composition. Talanta 46:1259–1264
Mellita (2020). www.melitta.com.br/regioes-brasileiras. Accessed in September of 2020
Nallamuthu I, Devi A, Khanum F (2015) Chlorogenic acid loaded chitosan nanoparticles with sustained release property, retained antioxidant activity and enhanced bioavailability. Asian J Pharm Sci 10:203–211
Núñez N, Saurina J, Núñez O (2021) Non-targeted HPLC-FLD fingerprinting for the detection and quantitation of adulterated coffee samples by chemometrics. Food Control 124:107912–107921
Oliveira LS, Franca AS, Mendonça JCF, Barros-Junior MC (2006) Proximate composition and fatty acids profile of green and roasted defective coffee beans. LWT Food Sci Technol 39:235–239
PerkinElmer Application Note (2013). Coffee Characterization Using Clarus SQ 8 GC/MS, TurboMatrix HS Trap and GC SNFR Olfactory Port. Andrew Tipler, PerkinElmer, Inc. Shelton, CT
Quinlan P, Lane J, Aspinall L (1997) Effects of hot tea, coffee and water ingestion on physiological responses and mood: the role of caffeine, water and beverage type. Psychopharmacology 134:164–173
Ribeiro JS, Augusto F, Salva TJG, Thomaziello RA, Ferreira MMC (2009) Prediction of sensory properties of Brazilian Arabica roasted coffees by headspace solid phase microextraction-gas chromatography and partial least squares. Anal Chim Acta 634:172–179
Silverstein RM (2005) Spectrometric identification of organic compounds, 8th edn. John Wiley & Sons, Hoboken
Thammarat P, Kulsing C, Wongravee K, Leepipatpiboon N, Nhujak T (2018) Identification of volatile compounds and selection of discriminant markers for elephant dung coffee using static headspace gas chromatography-mass spectrometry and chemometrics. Molecules 23:1910–1918
UNICAFÉ (2020). Foreign Trade Company. www.unicafe.com.br. Accessed in September of 2020
Zhang X, Li W, Yin B, Chen W, Kelly DP, Wang X (2013) Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabicaa coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS). Spectrochim Acta Part A Mol Biomol Spectrosc 114:350–356
Acknowledgements
To the Federal Institute of Education, Science, and Technology of São Paulo – IFSP - Campus Matão. The work described has not been published before, it is not under consideration for publication elsewhere, its submission to JFST publication has been approved by all authors as well as the responsible authorities, if accepted, it will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright holder, and JFST will not be held legally responsible should there be any claims for compensation or dispute on authorship. All data generated or analysed during this study are included in this published article [and its supplementary information files].
Funding
Not Applicable
Author information
Authors and Affiliations
Contributions
AC: Conceptualization, Formal analysis, Investigation, Methodology.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. All authors have read and approved the MS; and, that all are aware of its submission to JFST. The corresponding author shall review at least three manuscripts (in his own specialization) submitted to JFST.
Ethical approval
Not Applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Cestari, A. Development of a fast and simple method to identify pure Arabica coffee and blended coffee by Infrared Spectroscopy. J Food Sci Technol 58, 3645–3654 (2021). https://doi.org/10.1007/s13197-021-05176-4
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13197-021-05176-4