Elisabete A. De Nadai Fernandes
Adv. Artif. Intell. Mach. Learn., 2 (1):273-287
Elisabete A. De Nadai Fernandes : Nuclear Energy Center for Agriculture, University of São Paulo, Avenida Centenário 303, 13416-000 Piracicaba, SP, Brazil.
DOI: 10.54364/AAIML.2022.1118
Article History: Received on: 25-Feb-22, Accepted on: 25-Feb-22, Published on: 15-Feb-22
Corresponding Author: Elisabete A. De Nadai Fernandes
Email: lis@cena.usp.br
Citation: Elisabete A. De Nadai Fernandes,Yuniel T. Mazola, Robson C. de Lima, Gustavo N. Furlan, Márcio A. Bacchi, Gabriel A. Sarriés (2022). Machine learning to support geographical origin traceability of Coffea Arabica. Adv. Artif. Intell. Mach. Learn., 2 (1 ):273-287
The
species, variety and geographic origin of coffee directly influence the
characteristics of the coffee beans and, consequently, the quality of the
beverage. The added economic value that these features bring to the product has
boosted the use of non-designative tools for authentication purposes. In this
work, the feasibility of implementing a traceability system for Arabica coffee
by country of origin was investigated using quality attributes and supervised machine
learning algorithms: Multilayer Perceptron (MLP), Random Forest (RF), Random
Tree (RT) and Sequential Minimal Optimization (SMO). We use an available
database containing quality parameters for coffee beans produced in 15
countries, including the largest exporters and importers. Overall, Ethiopia,
Kenya and Uganda had the highest coffee quality index (Total Cup Points).
Differences between countries were found with 99% confidence using Robusta
Multivariate Data Science with original data and 98% accuracy using
Bootstrapping resampling method and Supervised Machine
Learning algorithms. The model obtained by RF provided the best
classification accuracy. The most important attributes to discriminate Arabica
coffee by country of origin, in descending order, were body, moisture, total
cup points, cupper points, acidity, aftertaste, flavor, aroma, balance,
sweetness and uniformity. The coffee variety proved to be a promising variable to
increase accuracy and can be incorporated among the quality attributes for
classification and grading of coffee beans.