论文标题
对便携式拉曼光谱仪中对未知物质识别的1D卷积神经网络的综述
A Review of 1D Convolutional Neural Networks toward Unknown Substance Identification in Portable Raman Spectrometer
论文作者
论文摘要
拉曼光谱学是一种强大的分析工具，其应用从质量控制到最前沿生物医学研究。在过去十年中取得了巨大进步的一个特定领域是强大的手持拉曼光谱仪的发展。它们已被急救人员和执法机构广泛采用，用于对未知物质的现场分析。带有拉曼光谱的未知物质的现场检测和鉴定在很大程度上依赖于手头设备的光谱匹配能力。传统的光谱匹配算法（例如相关，点产品等）已通过将未知数与大参考数据库进行比较来识别未知的拉曼光谱。这通常是通过蛮力求和引用和未知频谱之间的像素差异来实现的。常规算法具有明显的缺点。例如，它们倾向于识别纯化合物，但对于混合物化合物而言较少。例如，相对于样本数量数量的有限参考光谱无法访问的数据库已成为拉曼光谱范围广泛使用用于现场分析应用程序的挫折。作为另一种方法，最先进的深度学习方法（特别是卷积神经网络CNN）具有比常规光谱比较算法的许多优势。通过优化，它们非常适合部署在手持式光谱仪中，以用于未知物质的现场检测。在这项研究中，我们介绍了一维CNN用于拉曼频谱识别的全面调查。具体来说，我们强调了这种强大的深度学习技术用于手持拉曼光谱仪，考虑到手持系统的功耗和计算能力的潜在限制。
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.
