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Updated: Jan 15, 2026

Fruit Volatile Analysis Using an Electronic Nose

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

用变压器推进电子鼻子:危险气味和食品新鲜度的实时分类

Wejdan Deebani¹, Lubna Aziz², Wedad M Alawad³

¹Department of Mathematics, Faculty of Science & Arts, King Abdul Aziz University, Rabigh, Saudi Arabia.

Journal of food science

|October 15, 2025

概括

本研究介绍了一种人工智能电子鼻子 (E-nose),用于实时检测气体泄漏和食物腐烂. 这种新型系统实现了高精度,在室内环境中提供了更高的安全性和监控.

科学领域:

环境科学环境科学
人工智能的人工智能
传感器技术传感器技术

背景情况:

由于人类的嗅觉知觉不可靠,实时检测气体泄漏和食物腐烂是具有挑战性的.
现有的电子鼻子 (E-nose) 系统需要提高精度和边缘级处理.

研究的目的:

开发一种名为人工传感的AI驱动的新型电子鼻子系统,用于准确的气味分类和食品新鲜度评估.
增强室内环境监测和食品安全的边缘级处理能力.

主要方法:

将MQ系列和格罗夫多通道气体传感器v2与Seeed Studio Wio终端集成.
时间序列变压器模型的建议,具有多尺度自适应卷积 (MAC) 和多头自注意时间序列传感器数据.
实施采用温度和湿度数据的多式融合战略.

主要成果:

在气体泄漏检测方面获得了99.3%的F1分数,在食品腐败分类方面获得了94.8%.
与传统分类器 (SVM,随机森林,ANN) 和基线ViT模型相比,表现优越.
在Edge Impulse平台上提供120毫秒的延迟和低功耗的实时响应.

结论:

由人工智能驱动的E-nose系统显示出可靠的环境监测和食品安全应用的重大前景.
基于变压器的架构是有效的边缘级处理复杂的传感器数据.
该系统提供了一种可行的解决方案,用于检测室内气体泄漏和食品腐烂.

关键词:

格罗夫气体传感器v2 MQ系列传感器的传感器时间序列变压器时间序列变压器电子鼻子电子鼻子食物腐烂食物腐烂气体泄漏检测气体泄漏检测机器学习是机器学习.多式联络融合多式联络融合

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