A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances

A Review of 1D Convolutional Neural Networks toward Unknown Substance Identification in Portable Raman Spectrometer

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 co…

Raman spectral analysis of mixtures with one-dimensional convolutional neural network

Recently, the combination of robust one-dimensional convolutional neural networks (1-D CNNs) and Raman spectroscopy has shown great promise in rapid identification of unknown substances with good accuracy. Using this technique, researchers can recognize a pure compound and distinguish it from unknown substances in a mixture. The novelty of this approach is that the trained neural network operates automatically without any pre- or post-processing of data. Some studies have attempted to extend this technique to the classification of pure compounds in an unknown mixture. However, the application …

Identification of complex mixtures for Raman spectroscopy using a novel scheme based on a new multi-label deep neural network

With noisy environment caused by fluoresence and additive white noise as well as complicated spectrum fingerprints, the identification of complex mixture materials remains a major challenge in Raman spectroscopy application. In this paper, we propose a new scheme based on a constant wavelet transform (CWT) and a deep network for classifying complex mixture. The scheme first transforms the noisy Raman spectrum to a two-dimensional scale map using CWT. A multi-label deep neural network model (MDNN) is then applied for classifying material. The proposed model accelerates the feature extraction an…

面向标注高效细菌拉曼光谱聚类与分类的自校准双对比方法

Self-Calibrated Dual Contrasting for Annotation-Efficient Bacteria Raman Spectroscopy Clustering and Classification

本文提出自校准双对比（SCDC）方法，通过嵌入与类别双视角对比学习和自校准机制，在极少或无标注条件下实现稳健的细菌拉曼光谱聚类与分类。

基于掩码自编码器的拉曼光谱自监督学习方法

A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders

提出基于掩码自编码器的拉曼光谱自监督方法SMAE，无需标注预训练，重构去噪提升信噪比两倍以上，聚类准确率超80%，微调后识别精度达83.90%。