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可适应的微塑料分类使用相似性学习从μFTIR焦平面阵列成像阵列收集的μFTIR光谱

Justin A Smolen¹, Gavin E Moore¹, Nicholas D Perez²

¹Department of Chemistry, Laboratory for Synthetic-Biologic Interactions, Texas A&M University, College Station, TX 77843.

Proceedings of the National Academy of Sciences of the United States of America

|October 14, 2025

概括

这项研究引入了微塑料分类的相似性学习,使用微里埃变换红外 (μFTIR) 光谱. 这种方法提高了准确性和稳定性,即使在有噪音的数据和有限的训练集.

科学领域:

环境科学环境科学
分析化学分析化学
数据科学数据科学数据科学

背景情况:

识别微塑料对于环境监测至关重要.
微塑料分类的深度学习模型面临着诸如大数据集需求和过度拟合等挑战.
目前的方法需要针对新的微塑料类型或显著不同的数据进行再培训.

研究的目的:

探索一种相似性学习方法,用于训练微塑料分类的深度学习模型.
解决微塑料识别中传统深度学习方法的局限性.
为了提高从μFTIR光谱分类微塑料的准确性和适应性.

主要方法:

一个一维的卷积神经网络 (CNN) 通过在45个微塑料样本 (11个组合) 的μFTIR光谱上使用相似性学习进行训练.
类似性学习CNN与交叉训练CNN和经典机器学习算法进行了比较.
对原始和"噪音"数据集 (过器上的微塑料与背景材料) 的性能进行了评估.

主要成果:

类似性学习CNN实现了最高的准确性,F1得分高达0.973.
即使在杂的数据集上,相似性学习CNN也保持了高精度 (高达0.905F1分数).
类似性学习可以检测初始培训套件中不包括的微塑料类别.

结论:

类似性学习为使用μFTIR光谱进行微塑料分类提供了强大而准确的方法.
这种方法克服了各种聚合物组成,有限的数据和背景噪声的挑战.
它为微塑料识别提供了更具适应性和效率的深度学习解决方案.

关键词:

机器学习是机器学习.微里叶变换红外光谱学微塑料微塑料的使用

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