<br>Artificial Q-Grader：支持机器学习的智能嗅觉和味觉传感系统,Advanced Science

当前位置： X-MOL 学术 › Adv. Sci. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Artificial Q-Grader：支持机器学习的智能嗅觉和味觉传感系统
Advanced Science ( IF 14.3 ) Pub Date : 2024-04-06 , DOI: 10.1002/advs.202308976
Moonjeong Jang _{1,

2} , Garam Bae _{1,

3} , Yeong Min Kwon ₁ , Jae Hee Cho ₁ , Do Hyung Lee ₁ , Saewon Kang ₁ , Soonmin Yim ₁ , Sung Myung ₁ , Jongsun Lim ₁ , Sun Sook Lee ₁ , Wooseok Song _{1,

4} , Ki-Seok An ₁

Affiliation

模仿人类嗅觉和味觉系统的便携式个性化人工智能（AI）驱动传感器对于物联网（IoT）设备的大规模部署和自主监控系统具有巨大潜力。在这项研究中，开发了一种由表面工程氧化锌 (ZnO) 薄膜组成的人工 Q 分级机，作为人工鼻子、人工舌头，以及基于人工智能的统计数据分析作为人工大脑，用于识别咖啡豆中的香气和风味化学物质。基于聚(偏二氟乙烯-六氟丙烯)/ZnO薄膜晶体管(TFT)的液体传感器是人造舌头，而Au、Ag或Pd纳米颗粒/ZnO纳米混合气体传感器是人造鼻子。为了对咖啡豆的风味（醋酸（酸味）、丁酸乙酯和2-呋喃甲醇（甜味）、咖啡因（苦味））和咖啡豆的产地（巴布亚新几内亚、巴西、埃塞俄比亚和哥伦比亚——脱咖啡因）进行分类），合理组合 TFT 传输和动态响应曲线，捕获液体和气体依赖的电传输行为，并实现主成分分析 (PCA) 辅助机器学习 (ML)。 PCA 辅助的 ML 模型以 >92% 的预测准确度区分了四种目标口味。基于 ML 的回归模型以 >99% 的准确度预测风味化学物质浓度。此外，分类模型成功区分了四种不同类型的咖啡豆，准确率达到 100%。

"点击查看英文标题和摘要"

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

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.

更新日期：2024-04-06

点击分享查看原文

点击收藏

公开下载

阅读更多本刊新发论文本刊介绍/投稿指南