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Open AccessFeature PaperArticle

Correlating Espresso Quality with Coffee-Machine Parameters by Means of Association Rule Mining

Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
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Electronics 2020, 9(1), 100; https://doi.org/10.3390/electronics9010100
Received: 29 November 2019 / Revised: 30 December 2019 / Accepted: 31 December 2019 / Published: 4 January 2020
(This article belongs to the Special Issue Big Data Analytics for Smart Cities)
Coffee is among the most popular beverages in many cities all over the world, being both at the core of the busiest shops and a long-standing tradition of recreational and social value for many people. Among the many coffee variants, espresso attracts the interest of different stakeholders: from citizens consuming espresso around the city, to local business activities, coffee-machine vendors and international coffee industries. The quality of espresso is one of the most discussed and investigated issues. So far, it has been addressed by means of human experts, electronic noses, and chemical approaches. The current work, instead, proposes a data-driven approach exploiting association rule mining. We analyze a real-world dataset of espresso brewing by professional coffee-making machines, and extract all correlations among external quality-influencing variables and actual metrics determining the quality of the espresso. Thanks to the application of association rule mining, a powerful data-driven exhaustive and explainable approach, results are expressed in the form of human-readable rules combining the variables of interest, such as the grinder settings, the extraction time, and the dose amount. Novel insights from real-world coffee extractions collected on the field are presented, together with a data-driven approach, able to uncover insights into the espresso quality and its impact on both the life of consumers and the choices of coffee-making industries. View Full-Text
Keywords: association rule mining; correlation analysis; coffee-machine remote monitoring; data-driven product quality evaluation association rule mining; correlation analysis; coffee-machine remote monitoring; data-driven product quality evaluation
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Apiletti, D.; Pastor, E. Correlating Espresso Quality with Coffee-Machine Parameters by Means of Association Rule Mining. Electronics 2020, 9, 100.

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