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

Publisher: WILEY-VCH Country of Publication: Germany NLM ID: 101664569 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2198-3844 (Electronic) Linking ISSN: 21983844 NLM ISO Abbreviation: Adv Sci (Weinh) Subsets: MEDLINE

Original Publication: Weinheim : WILEY-VCH, [2014]-

Machine Learning* ; Electronic Nose*; Humans ; Artificial Intelligence ; Taste/physiology ; Coffee/chemistry ; Odorants/analysis ; Smell/physiology ; Tongue/physiology ; Zinc Oxide/chemistry ; Principal Component Analysis

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.
(© 2024 The Authors. Advanced Science published by Wiley‐VCH GmbH.)

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National Research Foundation of Korea; 2021M3D1A2046733 Ministry of Science and ICT; 2021M3H4A3A02099208 Ministry of Science and ICT

Keywords: gas sensor; liquid sensor; machine learning; surface engineering; zinc oxide

0 (Coffee)
SOI2LOH54Z (Zinc Oxide)

Date Created: 20240406 Date Completed: 20240619 Latest Revision: 20240621

20250114

PMC11186046

10.1002/advs.202308976

38582529