Abstract Due to its relatively simple and versatile nature, laser-induced breakdown spectroscopy experiments can yield enormous amount of data that normally needs to be preprocessed to remove background signal, electron continuum, and noise, and for some applications, correct for the instrument response function and normalize the signal prior to conducting spectroscopic analysis. In experiments where the focus is on the analysis of samples of similar composition, preprocessing can be repetitive and tedious. We show that preprocessing of such LIBS data can be performed in an automated or semi-automated manner using machine learning tools. To demonstrate this approach, we apply partial least squares regression and artificial neural networks on two laser-induced breakdown spectra datasets. The first dataset is used to develop predictive models for abundances of various elements in geological samples analyzed by a laboratory model of ChemCam. The second dataset consists of spectra obtained from ChemCam as it interrogates Martian targets. We show that using the two machine learning techniques, we can predict the preprocessed spectra of samples with a relatively high accuracy for both datasets.

中文翻译：

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使用偏最小二乘回归和前馈人工神经网络自动预处理激光诱导击穿光谱：在地球和火星数据中的应用

摘要 激光诱导击穿光谱实验由于其相对简单和通用的特性，可以产生大量数据，通常需要对其进行预处理以去除背景信号、电子连续谱和噪声，在某些应用中，还需要对仪器响应函数进行校正。并在进行光谱分析之前对信号进行归一化。在侧重于分析相似成分的样品的实验中，预处理可能是重复和乏味的。我们表明，可以使用机器学习工具以自动或半自动方式对此类 LIBS 数据进行预处理。为了演示这种方法，我们在两个激光诱导击穿光谱数据集上应用偏最小二乘回归和人工神经网络。第一个数据集用于开发由 ChemCam 实验室模型分析的地质样品中各种元素丰度的预测模型。第二个数据集包含从 ChemCam 获取的光谱，因为它询问火星目标。我们表明，使用两种机器学习技术，我们可以对两个数据集以相对较高的准确度预测样本的预处理光谱。