Sensory Classification of Brazilian Coffea arabica by Laser-Assisted Rapid Evaporative Ionization Mass Spectrometry and Machine Learning Algorithms.

Brazil plays an important role in coffee quality assessment since it is the top producer and exporter. New technologies must be developed to increase production and ensure product quality. Thus, this study presents an application of laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) to fingerprint more than 800 Arabica coffee samples. These samples were divided into six sensory classes by professional tasters according to the Brazilian official classification. Machine learning algorithms were applied for a better understanding of complex fingerprints, and their performances were compared. Partial least-squares discriminant analysis (PLS-DA) was inferior in its predictive capability compared to support vector machines (SVM) and artificial neural networks (ANN), which achieved up to 100% accuracy. The high sensitivity to distinct sensory classes enabled a tentative identification of spectral signals, such as fatty acids, chlorogenic acids, and phospholipids, which are likely being related to these properties in Arabica coffee for the first time.