期刊
ANALYTICAL CHEMISTRY
卷 93, 期 44, 页码 14609-14617出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.1c02071
关键词
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资金
- NSFC [22021001, 21790354, 21633005]
- MOST of China [2016YFA0200601]
A collaborative low-rank matrix approximation method was developed for fast hyperspectral Raman imaging and TERS imaging, demonstrating high signal-to-noise ratio and improved performance in signal extraction. The method significantly reduces noise, accelerates data acquisition, and provides a promising tool for fast TERS and conventional Raman imaging.
Fast acquisition of Raman images is essential for accurately characterizing the analytes' information. In this paper, we developed a collaborative low-rank matrix approximation method for fast hyperspectral Raman imaging as well as tip-enhanced Raman spectroscopy (TERS) imaging. This method combines high signal-to-noise ratio (SNR) data with the target data to perform collaborative singular value decomposition. The high-quality reference data can impose constraints on factorization, which will force its components to approximate the true signal or noise components. The simulation demonstrated that this method offers state-of-the-art signal extraction performance and, thus, can be used to accelerate data acquisition. Specifically, the results indicate that the CLRMA can largely decrease the root-meansquare error by 20.92-54.12% compared with the baseline method of our previous study. We then applied this method to the fast TERS imaging of a Au/Pd bimetallic surface and significantly decreased the integration time down to 0.1 s/pixel, which is about 10 times faster than that of conventional experiments. High-SNR TERS spectra and clear TERS images that are well consistent with scanning tunneling microscopy (STM) images can be obtained even under such a weak signal condition. We further applied this method to the fast Raman imaging of HeLa cells and obtained clear Raman images at a short integration time of 2 s/line, which is about 5 times faster than that of conventional experiments. This method offers a promising tool for TERS imaging as well as conventional Raman imaging where fast data acquisition is required.
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