Appl Spectrosc. 2014;68(2):155-64. doi: 10.1366/13-07018.
A new algorithm for the automatic recognition of peak and baseline regions in spectra is presented. It is part of a study to devise a baseline correction method that is particularly suitable for the simple and fast treatment of large amounts of data of the same type, such as those coming from high-throughput instruments, images, process monitoring, etc. This algorithm is based on the continuous wavelet transform, and its parameters are automatically determined using the criteria of Shannon entropy and the statistical distribution of noise, requiring virtually no user intervention. It was assessed on simulated spectra with different noise levels and baseline amplitudes, successfully recognizing the baseline points in all cases but for a few extremely weak and noisy signals. It can be combined with various fitting methods for baseline estimation and correction. In this work, it was used together with an iterative polynomial fitting to successfully process a real Raman image of 40,000 pixels in about 2.5 h.
提出了一种用于自动识别光谱中峰和基线区域的新算法。它是一项旨在设计一种特别适合于简单快速处理大量同类型数据的基线校正方法的研究的一部分，例如来自高通量仪器、图像、过程监测等的数据。该算法基于连续小波变换，其参数使用香农熵和噪声统计分布的标准自动确定，几乎不需要用户干预。它在具有不同噪声水平和基线幅度的模拟光谱上进行了评估，成功地识别了所有情况下的基线点，但对于一些非常弱和嘈杂的信号除外。它可以与各种拟合方法结合用于基线估计和校正。在这项工作中，它与迭代多项式拟合一起成功地处理了大约 2.5 小时内的 40000 像素的真实拉曼图像。
