Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, P. R. China.
Phys Chem Chem Phys. 2021 Aug 12;23(31):16998-17008. doi: 10.1039/d1cp02521j.
To achieve the goal of efficiently analyzing transient absorption spectra without arbitrary assumption and to overcome the limitations of conventional methods in fitting ability and highly noised backgrounds, it is essential to develop new tools to achieve more accurate and robust prediction based on the intrinsic properties of a spectrum even under strong noise. In this work, Lasso regression and neural network were combined to achieve an effective fitting. Compared to the conventional global fitting method, our network could automatically determine the exponential form on each wave unit, in which the accuracy was as high as 97%. Thereafter, the lifetime with the corresponding amplitude ratio could be easily predicted by the neural network on each wave unit. This kind of prediction is difficult to achieve by global fitting due to the limitation of computational resources. Furthermore, more accurate fitting even under weak signals could be achieved for the mean square error (MSE) decreasing by more than 100 times on average compared to conventional global fitting methods. Attributed to its improved accuracy and robustness, our developed algorithm could be readily applied to analyze time-resolved transient spectra.
为了实现高效分析瞬态吸收光谱的目标，避免任意假设，并克服传统方法在拟合能力和高度噪声背景下的局限性，开发新的工具以基于光谱的固有特性实现更准确和稳健的预测至关重要，即使在强噪声下也是如此。在这项工作中，我们结合了 Lasso 回归和神经网络来实现有效的拟合。与传统的全局拟合方法相比，我们的网络可以自动确定每个波单元的指数形式，其准确性高达 97%。此后，神经网络可以轻松地在每个波单元上预测具有相应幅度比的寿命。由于计算资源的限制，全局拟合很难实现这种预测。此外，与传统的全局拟合方法相比，均方误差（MSE）平均减少了 100 多倍，因此即使在较弱的信号下也可以实现更准确的拟合。由于其提高的准确性和稳健性，我们开发的算法可以很容易地应用于分析时间分辨瞬态光谱。
