Variability of single bean coffee volatile compounds of Arabica and robusta roasted coffees analysed by SPME-GC-MS

Abstract

We report on the analysis of volatile compounds by SPME-GC-MS for individual roasted coffee beans. The aim was to understand the relative abundance and variability of volatile compounds between individual roasted coffee beans at constant roasting conditions. Twenty-five batches of Arabica and robusta species were sampled from 13 countries, and 10 single coffee beans randomly selected from each batch were individually roasted in a fluidised-bed roaster at 210 °C for 3 min. High variability (CV = 14.0-53.3%) of 50 volatile compounds in roasted coffee was obtained within batches (10 beans per batch). Phenols and heterocyclic nitrogen compounds generally had higher intra-batch variation, while ketones were the most uniform compounds (CV < 20%). The variation between batches was much higher, with the CV ranging from 15.6 to 179.3%. The highest variation was observed for 2,3-butanediol, 3-ethylpyridine and hexanal. It was also possible to build classification models based on geographical origin, obtaining 99.5% and 90.8% accuracy using LDA or MLR classifiers respectively, and classification between Arabica and robusta beans. These results give further insight into natural variation of coffee aroma and could be used to obtain higher quality and more consistent final products. Our results suggest that coffee volatile concentration is also influenced by other factors than simply the roasting degree, especially green coffee composition, which is in turn influenced by the coffee species, geographical origin, ripening stage and pre- and post-harvest processing.

Keywords: Coffea arabica L.; Coffea canephora L; Coffee aroma; Coffee roasting; Coffee volatile compounds; Headspace analysis; SPME-GC/MS; Single coffee bean.

Graphical abstract

Fig. 1

Boxplot distribution of volatile compounds in single roasted coffee beans, by separately showing Arabica and robusta species. Compounds are shown in order of elution (Table 1), except the most abundant ones, shown separately. Vertical bars indicate the median for each compound, horizontal bars indicate the maximum and minimum value, circles indicate possible outliers. The bottom plot shows the most concentrated compounds.

Fig. 2

Volatile compounds in roasted coffee beans grouped by chemical classes. The (a) average concentration of each class is shown for each coffee batch (n = 10), and the (b) intra-batch variability is expressed as the relative standard deviation of the ten beans per batch (n = 10).

Fig. 3

Cluster analysis of volatile compounds in single roasted coffee beans, analysed by SPME-GC-MS (n = 248).

Fig. 4

Results of Linear Discriminant Analysis (LDA) applied to discriminate roasted coffee beans according to their (a) botanical species or (b) geographical origin, based on volatile composition assessed by SPME-GC-MS (expressed as % total peak areas). n = 248. Each point represents a sample of a single coffee bean.

Fig. 5

Classification model for coffee origin using Neural Network (NN). NN score 1 and NN score 2 are extracted features from the neural network model.