[论文解读] A Review of 1D Convolutional Neural Networks toward Unknown Substance Identification in Portable Raman Spectrometer

M. Hamed Mozaffari, Li‐Lin Tay|arXiv (Cornell University)|Jun 18, 2020

Spectroscopy Techniques in Biomedical and Chemical Research参考文献 60被引用 35

一句话总结

这篇论文综述了如何在便携式拉曼光谱仪中使用1D CNN来识别未知物质，强调相对于传统光谱匹配的优势以及手持部署的注意事项。

ABSTRACT

Raman spectroscopy is a powerful analytical tool with applications ranging from quality control to cutting edge biomedical research. One particular area which has seen tremendous advances in the past decade is the development of powerful handheld Raman spectrometers. They have been adopted widely by first responders and law enforcement agencies for the field analysis of unknown substances. Field detection and identification of unknown substances with Raman spectroscopy rely heavily on the spectral matching capability of the devices on hand. Conventional spectral matching algorithms (such as correlation, dot product, etc.) have been used in identifying unknown Raman spectrum by comparing the unknown to a large reference database. This is typically achieved through brute-force summation of pixel-by-pixel differences between the reference and the unknown spectrum. Conventional algorithms have noticeable drawbacks. For example, they tend to work well with identifying pure compounds but less so for mixture compounds. For instance, limited reference spectra inaccessible databases with a large number of classes relative to the number of samples have been a setback for the widespread usage of Raman spectroscopy for field analysis applications. State-of-the-art deep learning methods (specifically convolutional neural networks CNNs), as an alternative approach, presents a number of advantages over conventional spectral comparison algorism. With optimization, they are ideal to be deployed in handheld spectrometers for field detection of unknown substances. In this study, we present a comprehensive survey in the use of one-dimensional CNNs for Raman spectrum identification. Specifically, we highlight the use of this powerful deep learning technique for handheld Raman spectrometers taking into consideration the potential limit in power consumption and computation ability of handheld systems.

研究动机与目标

• 调查在手持设备上基于拉曼光谱的未知物质识别中1D CNN的使用。
• 突出传统光谱匹配算法的局限性以及CNN如何解决它们。
• 考虑便携光谱仪的电力和计算等实际约束。
• 提供在现场分析和混合物识别中部署基于CNN的方法的指导。

提出的方法

• 回顾现有文献中应用于拉曼光谱数据的1D CNN。
• 将基于CNN的方法与传统逐像素相似性度量进行比较。
• 讨论影响手持实现的因素，如功耗和计算资源。
• 总结发现并指出在现场设置中未知物质识别的开放挑战。

实验结果

研究问题

• RQ1 使用1D CNN相比传统光谱匹配在拉曼光谱方面有哪些优势？
• RQ2 1D CNN在手持式拉曼光谱仪中的纯物质与混合物的表现如何？
• RQ3 部署到便携设备时哪些实际约束（电力、计算）会影响？
• RQ4 将1D CNN应用于现场场景中的未知物质识别存在哪些差距和未解决的挑战？

主要发现

• 1D CNN在处理光谱变异性和混合物方面相对于传统基于相关性的方法具有优势。
• 基于CNN的方法有潜力在手持光谱仪上部署，同时考虑有限的电力和计算资源。
• 该综述强调需要健壮的数据集和模型效率以便现场部署。
• 仍存在与数据库大小、类别覆盖和便携环境下实时推断相关的挑战。
• 该综述提供了1D CNN如何被改编用于便携设备的拉曼光谱识别的综合视图。