Fast reconstruction of Raman spectra based on global weighted linear regression

Raman spectroscopy has shown great potential in biomedical applications. However, slow data acquisition of Raman spectra has seriously hindered the expansion of its application. In this paper, we have completed the reconstruction of Raman spectra with multi-channel measurements based global weighted linear regression. This algorithm establishes a linear regression function by optimizing the training samples and making a global assignment weighted to the optimizing samples. Simultaneously, the normalization and polynomial regression are introduced in order to improve the accuracy of reconstructed spectra. It has evaluated the Raman spectra of several materials. According to the root mean square error, the fitness between reconstructed and original spectra is excellent. This algorithm can be used in quickly testing for potential sample component in a substance, where the sample component to be tested is known and provides a theoretical support for the application of Raman imaging technology in fast dynamic systems. •We propose a method for optimizing the training samples based on Tanimoto coefficient to delete the bad sample.•We perform polynomial regression on the multi-channel measurements in order to reduce the influence of nonlinear factors and introduce the normalization, cross-validation algorithms to improve the accuracy of reconstructed spectra.•We establish a linear regression function by optimizing the training samples and making a global assignment weight to the optimizing samples.•We verify the effectiveness and the feasibility of the methods by processing the Raman spectra of several materials.