Electronic nose coupled with artificial neural network for classifying of coffee roasting profile

Abstract

Coffee known for its diverse aromas shaped by postharvest treatments, particularly the roasting process, plays a pivotal role in determining the quality of the brewed beverage. This study focuses on classifying the aroma of Arabica coffee beans based on roasting temperature, employing an electronic nose equipped with a TGS gas array sensor. The classification methodology integrates deep learning through an artificial neural network (ANN), along with a calculation analysis utilizing the Pearson correlation coefficient. Raw Robusta coffee beans were subjected to five distinct roasting treatments (185 °C, 195 °C, 205 °C, 215 °C, and 225 °C), resulting in light roasts, light to medium roasts, medium to dark roasts, medium to dark roasts, and dark roasts. The repeatability test affirms the TGS sensor's reliability, exhibiting a standard deviation (STD) below 20%. Notably, the TGS 2612 and TGS 2611 sensors, dedicated to odor detection, demonstrated excellent validity with an STD below 10% across various roasting temperatures. Classification results from deep learning cross-validation showcase impressive accuracy: 98.2% for Light Roasts, 98.4% for Light to Medium Roasts, 98.8% for Medium Roasts, 97.8% for Medium Roasts, and 95.9% for Dark Roasts. In conclusion, this study reveals that the E-nose, utilizing the TGS gas sensor array with deep learning analysis, effectively detects and classifies coffee types based on roasting time with high accuracy.