On characterization of sensory data in presence of missing values: The case of sensory coffee quality assessment

Abstract

Multiple factor analysis was used to examine organoleptic coffee assessments such as aroma, aftertaste, flavor, acidity, balance, body, uniformity, sweetness, clean cup, and other organoleptic-related properties used in Coffee Quality Assessment. The Sensory analysis was performed using missing values (NA) scenarios with 5%, 10%, 20%, and 30% of NA. The results suggest that RI-MFA is robust to NA presence of and appears to be appropriate when sensory data are present. Simulation scenarios deleting or replacing values from real-world datasets could be a good strategy; different domains, samples, types of variables, and distributions could prove much closer to reality.