Abstract 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.

中文翻译：

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基于全局加权线性回归的拉曼光谱快速重建

摘要 拉曼光谱在生物医学应用中显示出巨大的潜力。然而，拉曼光谱数据采集缓慢严重阻碍了其应用的扩展。在本文中，我们通过基于全局加权线性回归的多通道测量完成了拉曼光谱的重建。该算法通过优化训练样本并对优化样本进行全局分配来建立线性回归函数。同时引入归一化和多项式回归以提高重构谱的精度。它已经评估了几种材料的拉曼光谱。根据均方根误差，重建光谱与原始光谱之间的拟合度非常好。