Electron Energy-Loss Spectroscopy (EELS) is a powerful and versatile spectroscopic technique used to study the composition and local optoelectronic properties of nanometric materials. Currently, this technique is generating large amounts of spectra per experiment, producing a huge quantity of data to analyse. Several strategies can be applied in order to classify these data to map physical properties at the nanoscale. In the present study, the Support Vector Machine (SVM) algorithm is applied to EELS, and its effectiveness identifying EEL spectra is assessed. Our results evidence the capacity of SVM to determine the oxidation state of iron and manganese in iron and manganese oxides, based on the ELNES of the white lines of the transition metal. The SVM algorithm is first trained with given datasets and then the resulting models are tested through noisy test data sets. We demonstrate that SVM exhibits a very good performance classifying these EEL spectra, despite the usual level of noise and instrumental energy shifts.

中文翻译：

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用于 EELS 氧化态测定的支持向量机

电子能量损失光谱 (EELS) 是一种强大且通用的光谱技术，用于研究纳米材料的成分和局部光电特性。目前，这项技术正在为每个实验生成大量光谱，产生大量数据进行分析。可以应用多种策略来对这些数据进行分类，以绘制纳米级的物理特性。在本研究中，支持向量机 (SVM) 算法应用于 EELS，并评估其识别 EEL 光谱的有效性。我们的结果证明了 SVM 能够根据过渡金属白线的 ELNES 确定铁和锰氧化物中铁和锰的氧化态。SVM 算法首先使用给定的数据集进行训练，然后通过嘈杂的测试数据集测试生成的模型。我们证明了 SVM 在对这些 EEL 光谱进行分类时表现出非常好的性能，尽管通常的噪声和仪器能量偏移水平。