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Single-Acquisition 2-D Multifocal Raman Spectroscopy Using Compressive Sensing.
Zhang, Pengfei; Wang, Guiwen; Zhang, Xiujuan; Li, Yong-Qing.
Afiliação
  • Zhang P; School of Precision Instruments and Optoelectronics Engineering , Tianjin University , Tianjin 300072 , P. R. China.
  • Wang G; Guangxi Academy of Sciences , 98 Daling Road , Nanning , Guangxi 530007 , P. R. China.
  • Zhang X; Guangxi Academy of Sciences , 98 Daling Road , Nanning , Guangxi 530007 , P. R. China.
  • Li YQ; The Laboratory of Biomedical Photonics & Engineering , Guangxi Medical University , Nanning , Guangxi 530021 , P. R. China.
Anal Chem ; 92(1): 1326-1332, 2020 01 07.
Article em En | MEDLINE | ID: mdl-31793766
Confocal Raman microscopy is a powerful method for nondestructive and noninvasive detection of chemicals with high spatial resolution, but its long acquisition time hinders its applications in large-scale monitoring of fast dynamics. Here, we report the development of a compressive sensing technique for single-acquisition multifocal Raman spectroscopy, which is capable of improving the speed of conventional confocal Raman spectroscopy by 2-3 orders of magnitude. A sample is excited with a 2-D multifocus pattern, and the Raman scatterings from the multiple foci were projected onto the spectrometer's entrance in a 2-D array. The superimposed spectra within each row of the array were processed with an algorithm such that the spectra from the individual foci were retrieved in a single acquisition and with reduced noise. The performances of the developed technique were demonstrated by parallel Raman spectroscopy of multiple individual particles as well as by single-acquisition confocal Raman imaging of a large scale with high spatial resolution when combined with spatially sparse sampling. The technique is expected to find wide applications in investigating fast dynamics in large-scale biological systems.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article