Artificial Q‐Grader: Machine Learning‐Enabled Intelligent Olfactory and Gustatory Sensing System

Abstract

Abstract Portable and personalized artificial intelligence (AI)‐driven sensors mimicking human olfactory and gustatory systems have immense potential for the large‐scale deployment and autonomous monitoring systems of Internet of Things (IoT) devices. In this study, an artificial Q‐grader comprising surface‐engineered zinc oxide (ZnO) thin films is developed as the artificial nose, tongue, and AI‐based statistical data analysis as the artificial brain for identifying both aroma and flavor chemicals in coffee beans. A poly(vinylidene fluoride‐co‐hexafluoropropylene)/ZnO thin film transistor (TFT)‐based liquid sensor is the artificial tongue, and an Au, Ag, or Pd nanoparticles/ZnO nanohybrid gas sensor is the artificial nose. In order to classify the flavor of coffee beans (acetic acid (sourness), ethyl butyrate and 2‐furanmethanol (sweetness), caffeine (bitterness)) and the origin of coffee beans (Papua New Guinea, Brazil, Ethiopia, and Colombia‐decaffeine), rational combination of TFT transfer and dynamic response curves capture the liquids and gases‐dependent electrical transport behavior and principal component analysis (PCA)‐assisted machine learning (ML) is implemented. A PCA‐assisted ML model distinguished the four target flavors with >92% prediction accuracy. ML‐based regression model predicts the flavor chemical concentrations with >99% accuracy. Also, the classification model successfully distinguished four different types of coffee‐bean with 100% accuracy.

Keywords

• gas sensor
• liquid sensor
• machine learning
• surface engineering
• zinc oxide