ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
Artificial Q-Grader: Machine Learning-Enabled Intelligent Olfactory and Gustatory Sensing System.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The trial behind it
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Who cites it
7 citing papers in PubMed.
- Machine learning for food flavor prediction and regulation: models, data integration, and future perspectives.Journal of advanced research · 2026Review
- Long-Tea-CLIP: An Expert-Level Multimodal AI Framework for Fine-Grained Green Tea Grading Across Five Sensory Dimensions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Advances in cell-based biosensors: Transforming food flavor evaluation with novel approaches.Food chemistry: X · 2025Review
- Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi-Sensor System with Foundation Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Integrated Mixed Potential Gas Sensor with Efficient Structure for Discriminative Volatile Organic Compounds Detection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array.Nano-micro letters · 2024Review
- Artificial Q-Grader: Machine Learning-Enabled Intelligent Olfactory and Gustatory Sensing System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.