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用CelluMOFs制成的嗅觉可视化传感阵列,使用深度学习来预测水果成熟度

Mingming Zhao^1,2, Huizi Lu^1,2, Zhiheng You^1,2

¹School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, P. R. China.

ACS applied materials & interfaces

|October 15, 2024

概括

这项研究介绍了一种新的人工气味系统,使用灵活的染料/CelluMOFs传感器阵列和DenseNet来准确检测水果成熟度. 该系统在识别水果香味指纹和预测成熟度水平方面达到99.09%的准确性.

科学领域:

材料科学材料科学材料科学
化学传感器化学传感器
人工智能的人工智能

背景情况:

开发敏感和准确的人工气味系统来检测水果的成熟度是一项挑战.
现有的方法往往缺乏现场评估所需的灵敏度和模式识别能力.
基于测色仪的系统具有潜力,但需要提高灵敏度和与先进算法的集成.

研究的目的:

构建基于染料/CelluMOFs的灵活传感器阵列,用于高灵敏度检测水果挥发性化合物.
整合一个密集连接的卷积网络 (DenseNet),以准确识别水果气味指纹.
通过嗅觉视觉感知系统来实现果实成熟度的精确分类.

主要方法:

在纸上通过g-cyclodextrin金属有机框架 (g-CD-MOFs) 的现场生长合成CelluMOFs.
制造一种灵活的,多孔的染料/CelluMOFs敏感膜,具有增强的染料负载能力.
将DenseNet模型与色度传感器阵列集成,用于模式识别和分类.
检测特有的水果气味,包括2-hexenal,并评估传感器的稳定性.

主要成果:

与过纸相比,CelluMOFs膜的特定表面积增加了62倍,染料负载能力增加了3倍.
传感器阵列表现出高灵敏度,低气体检测值为2-hexenal (8-1500 ppm).
综合系统在验证集上实现了99.09%的分类准确度,用于果实成熟度预测.

结论:

一个新的,灵活的染料/CelluMOFs传感器阵列与DenseNet相结合,可实现灵敏而准确的水果成熟度检测.
嗅觉视觉传感系统有效地识别独特的气味指纹,用于高精度的水果分类.
这种方法为果实质量的现场,非破坏性评估提供了一个有希望的解决方案.

关键词:

颜值测量传感器阵列的颜色测量.密集连接的卷积网络密集连接的卷积网络果实成熟度分类的果实成熟度分类金属-有机的框架.挥发性有机化合物挥发性有机化合物

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