Detection and Differentiation of Volatile Compound Profiles in Roasted Coffee Arabica Beans from Different Countries Using an Electronic Nose and GC-MS

This paper describes the possibility of electronic nose-based detection and discrimination of volatile compound profiles of coffee from different countries roasted in a Gothot roaster under identical time and thermal regimes. The material used in the study was roasted Arabica coffee beans from Brazil, Ethiopia, Guatemala, Costa Rica, and Peru. The analyses were carried out with the use of the Agrinose electronic nose designed and constructed at the Institute of Agrophysics of the Polish Academy of Sciences in Lublin. The results of the volatile compound profile analysis provided by the Agrinose device were verified with the GC-MS technique. Chemometric tests demonstrated a dominant role of alcohols, acids, aldehydes, azines, and hydrazides in the coffee volatile compound profile. The differences in their content had an impact on the odor profile of the coffees originating from the different countries. High content of pyridine from the group of azines was detected in the coffee from Peru and Brazil despite the same roasting conditions. The results of the Agrinose analysis of volatile substances were consistent and correlated with the GC-MS results. This suggests that the Agrinose is a promising tool for selection of coffees based on their volatile compound profile.

[1]  Ian D Fisk,et al.  Determination of volatile marker compounds of common coffee roast defects , 2016, Food chemistry.

[2]  R. Rusinek,et al.  Application of an electronic nose with novel method for generation of smellprints for testing the suitability for consumption of wheat bread during 4-day storage , 2020 .

[3]  Robert Rusinek,et al.  A Novel Method for Generation of a Fingerprint Using Electronic Nose on the Example of Rapeseed Spoilage. , 2019, Journal of food science.

[4]  Majid Nooshkam,et al.  The Maillard reaction products as food-born antioxidant and antibrowning agents in model and real food systems. , 2019, Food chemistry.

[5]  S. Selli,et al.  Differentiation of Volatile Profiles and Odor Activity Values of Turkish Coffee and French Press Coffee , 2016 .

[6]  Wenjiang Dong,et al.  Characterization of Fatty Acid, Amino Acid and Volatile Compound Compositions and Bioactive Components of Seven Coffee (Coffea robusta) Cultivars Grown in Hainan Province, China , 2015, Molecules.

[7]  Robert Rusinek,et al.  Application of an electronic nose for determination of pre‐pressing treatment of rapeseed based on the analysis of volatile compounds contained in pressed oil , 2019, International Journal of Food Science & Technology.

[8]  A. C. Freitas,et al.  Coffee geographic origin — an aid to coffee differentiation , 1999 .

[9]  Alexia N. Gloess,et al.  Comparison of nine common coffee extraction methods: instrumental and sensory analysis , 2013, European Food Research and Technology.

[10]  D. Knysak.  Volatile compounds profiles in unroasted Coffea arabica and Coffea canephora beans from different countries , 2017 .

[11]  H. Jeleń.  Use of solid phase microextraction (SPME) for profiling fungal volatile metabolites , 2003, Letters in applied microbiology.

[12]  Robert Rusinek,et al.  Identification of Volatile Organic Compounds and Their Concentrations Using a Novel Method Analysis of MOS Sensors Signal. , 2019, Journal of food science.

[13]  Y. Sagara,et al.  Gas chromatography/olfactometry and electronic nose analyses of retronasal aroma of espresso and correlation with sensory evaluation by an artificial neural network. , 2010, Journal of food science.

[14]  L. Pezza,et al.  Relationship Between the Different Aspects Related to Coffee Quality and Their Volatile Compounds. , 2016, Comprehensive reviews in food science and food safety.

[15]  A. Furtado,et al.  Influence of genotype and environment on coffee quality , 2016 .

[16]  Hehe Li,et al.  Comparative evaluation of the volatile profiles and taste properties of roasted coffee beans as affected by drying method and detected by electronic nose, electronic tongue, and HS-SPME-GC-MS. , 2019, Food chemistry.

[17]  L. Müller,et al.  Coffee aroma--statistical analysis of compositional data. , 2009, Talanta.

[18]  B. Bruggen,et al.  Influence of storage conditions on aroma compounds in coffee pads using static headspace GC―MS , 2009 .

[19]  G. Pellegrino,et al.  Coffee aroma: Chemometric comparison of the chemical information provided by three different samplings combined with GC-MS to describe the sensory properties in cup. , 2017, Food chemistry.

[20]  A. Visconti,et al.  Screening of deoxynivalenol contamination in durum wheat by MOS-based electronic nose and identification of the relevant pattern of volatile compounds , 2014 .

[21]  Miguel Peris,et al.  Electronic noses and tongues to assess food authenticity and adulteration , 2016 .

[22]  Wenjiang Dong,et al.  Differentiation of Chinese robusta coffees according to species, using a combined electronic nose and tongue, with the aid of chemometrics. , 2017, Food chemistry.