Integrating a low‐cost electronic nose and machine learning modelling to assess coffee aroma profile and intensity

Abstract

Aroma is one of the main attributes that consumers consider when appreciating and se-lecting a coffee; hence it is considered an important quality trait. However, the most common methods to assess aroma are based on expensive equipment or human senses through sensory evalua-tion, which is time‐consuming and requires highly trained assessors to avoid subjectivity. Therefore, this study aimed to estimate the coffee intensity and aromas using a low‐cost and portable electronic nose (e‐nose) and machine learning modeling. For this purpose, triplicates of six commercial coffee samples with different intensity levels were used for this study. Two machine learning models were developed based on art..