Many spectra have a polynomial-like baseline. Iterative polynomial fitting (IPF) is one of the most popular methods for baseline correction of these spectra. However, the baseline estimated by IPF may have substantially error when the spectrum contains significantly strong peaks or have strong peaks located at the endpoints. First, IPF uses temporary baseline estimated from the current spectrum to identify peak data points. If the current spectrum contains strong peaks, then the temporary baseline substantially deviates from the true baseline. Some good baseline data points of the spectrum might be mistakenly identified as peak data points and are artificially re-assigned with a low value. Second, if a strong peak is located at the endpoint of the spectrum, then the endpoint region of the estimated baseline might have significant error due to overfitting. This study proposes a search algorithm-based baseline correction method (SA) that aims to compress sample the raw spectrum to a dataset with small number of data points and then convert the peak removal process into solving a search problem in artificial intelligence (AI) to minimize an objective function by deleting peak data points. First, the raw spectrum is smoothened out by the moving average method to reduce noise and then divided into dozens of unequally spaced sections on the basis of Chebyshev nodes. Finally, the minimal points of each section are collected to form a dataset for peak removal through search algorithm. SA selects the mean absolute error (MAE) as the objective function because of its sensitivity to overfitting and rapid calculation. The baseline correction performance of SA is compared with those of three baseline correction methods: Lieber and MahadevanâJansen method, adaptive iteratively reweighted penalized least squares method, and improved asymmetric least squares method. Simulated and real FTIR and Raman spectra with polynomial-like baselines are employed in the experiments. Results show that for these spectra, the baseline estimated by SA has fewer error than those by the three other methods.

中文翻译：

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EXPRESS：基于人工智能搜索算法的基线校正

许多光谱具有类似多项式的基线。迭代多项式拟合 (IPF) 是对这些光谱进行基线校正的最流行方法之一。然而，当光谱包含明显的强峰或在端点处有强峰时，IPF 估计的基线可能有很大的误差。首先，IPF 使用从当前光谱估计的临时基线来识别峰值数据点。如果当前光谱包含强峰，则临时基线与真实基线显着偏离。光谱的一些好的基线数据点可能会被错误地识别为峰值数据点，并被人为地重新分配为较低的值。其次，如果一个强峰位于光谱的端点，那么估计基线的端点区域可能由于过度拟合而存在显着误差。本研究提出了一种基于搜索算法的基线校正方法（SA），旨在将原始光谱样本压缩为具有少量数据点的数据集，然后将峰值去除过程转换为解决人工智能（AI）中的搜索问题通过删除峰值数据点来最小化目标函数。首先通过移动平均法对原始频谱进行平滑处理以降低噪声，然后在切比雪夫节点的基础上将其划分为数十个不等距的部分。最后，收集每个部分的最小点，形成一个数据集，通过搜索算法进行去峰。SA选择平均绝对误差（MAE）作为目标函数，因为它对过拟合和快速计算很敏感。将 SA 的基线校正性能与三种基线校正方法进行比较：Lieber 和 MahadevanâJansen 方法、自适应迭代重加权惩罚最小二乘法和改进的非对称最小二乘法。实验中采用了具有多项式基线的模拟和真实 FTIR 和拉曼光谱。结果表明，对于这些光谱，SA 估计的基线比其他三种方法的误差更小。和改进的非对称最小二乘法。实验中采用了具有多项式基线的模拟和真实 FTIR 和拉曼光谱。结果表明，对于这些光谱，SA 估计的基线比其他三种方法的误差更小。和改进的非对称最小二乘法。实验中采用了具有多项式基线的模拟和真实 FTIR 和拉曼光谱。结果表明，对于这些光谱，SA 估计的基线比其他三种方法的误差更小。