Artificial Q-Grader: Machine Learning-Enabled Intelligent Olfactory and Gustatory Sensing System
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.PMID:38582529 | DOI:10.1002/advs.202308976
Source: Adv Data - Category: Epidemiology Authors: Moonjeong Jang Garam Bae Yeong Min Kwon Jae Hee Cho Do Hyung Lee Saewon Kang Soonmin Yim Sung Myung Jongsun Lim Sun Sook Lee Wooseok Song Ki-Seok An Source Type: research
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