Koyun Onur Can, Keser Reyhan Kevser, Şahin Safa Onur, Bulut Damla, Yorulmaz Mustafa, Yücesoy Veysel, Töreyin Behçet Uğur
Signal Processing for Computational Intelligence Research Group (SP4CING), Informatics Institute, Istanbul Technical University, 34469 Istanbul, Turkey.
ASELSAN Inc, Yenimahalle, 06200 Ankara, Turkey.
ACS Omega. 2024 May 23;9(22):23241-23251. doi: 10.1021/acsomega.3c09247. eCollection 2024 Jun 4.
Raman spectroscopy is a noninvasive technique to identify materials by their unique molecular vibrational fingerprints. However, distinguishing and quantifying components in mixtures present challenges due to overlapping spectra, especially when components share similar features. This study presents "RamanFormer", a transformer-based model designed to enhance the analysis of Raman spectroscopy data. By effectively managing sequential data and integrating self-attention mechanisms, RamanFormer identifies and quantifies components in chemical mixtures with high precision, achieving a mean absolute error of 1.4% and a root mean squared error of 1.6%, significantly outperforming traditional methods such as least squares, MLP, VGG11, and ResNet50. Tested extensively on binary and ternary mixtures under varying conditions, including noise levels with a signal-to-noise ratio of up to 10 dB, RamanFormer proves to be a robust tool, improving the reliability of material identification and broadening the application of Raman spectroscopy in fields, such as material science, forensics, and biomedical diagnostics.
拉曼光谱是一种通过材料独特的分子振动指纹来识别材料的非侵入性技术。然而，由于光谱重叠，区分和量化混合物中的成分存在挑战，特别是当成分具有相似特征时。本研究提出了“拉曼变换器”（RamanFormer），这是一种基于变换器的模型，旨在加强对拉曼光谱数据的分析。通过有效地处理序列数据并整合自注意力机制，拉曼变换器能够高精度地识别和量化化学混合物中的成分，平均绝对误差为1.4%，均方根误差为1.6%，显著优于最小二乘法、多层感知器、VGG11和ResNet50等传统方法。在包括高达10 dB信噪比的噪声水平等不同条件下对二元和三元混合物进行了广泛测试，拉曼变换器被证明是一种强大的工具，提高了材料识别的可靠性，并拓宽了拉曼光谱在材料科学、法医学和生物医学诊断等领域的应用。
