Efforts have been made to predict the sensory profile of coffee samples by instrumental measurement results. The objective of the work was to evaluate the most important sensory attributes of coffee samples prepared from ground roasted coffee by electronic tongue and by sensory panel. Further aim was to predict the Arabica concentration and the main sensory attributes of the different coffee blends by electronic tongue and to analyze the sensitivity of the electronic tongue to the detection of poor quality coffee samples. Five coffee blends with known Arabica and Robusta concentration ratio, five commercially available coffee blends and a poor quality coffee were analyzed. The electronic tongue distinguished the coffee samples according to the Arabica and Robusta content. The sensory panel was able to discriminate the samples based on global aroma, bitterness and coffee aroma intensity (p < 0.01). The Arabica concentration was predicted from the electronic tongue results by PLS with close correlation and low prediction error. Models were developed to predict sensory attributes of the tested coffee samples from the results obtained by the electronic instrument.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.