Abstract We present results of the classification contest organized for the EMSLIBS 2019 conference. For this publication, we chose only the five best approaches and discussed their algorithm in detail. The main focus of the contest reflected both recent and long-term challenges of Laser-Induced Breakdown Spectroscopy (LIBS) data processing. The contest was designed with a purpose to raise a challenge in handling and processing a very large dataset, containing high-dimensional elemental spectra. For the contest, 138 samples were measured using a lab-based LIBS system. In total, the data set consisted of 70,000 spectra, separated into 12 classes according to their elemental composition. Due to its extensivity and complexity, the data set is unique within the LIBS community. The central idea was to simulate the so-called “out-of-sample” classification (i.e. according to similar elemental composition), implying various real-world applications. Even more, it reflects the current level of expertise in the LIBS community and the capability of the LIBS method itself.

中文翻译：

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具有挑战性的 LIBS 土壤样本数据的分类 - EMSLIBS 竞赛

摘要 我们展示了为 EMSLIBS 2019 会议组织的分类竞赛的结果。对于本出版物，我们仅选择了五种最佳方法并详细讨论了它们的算法。比赛的主要焦点反映了激光诱导击穿光谱 (LIBS) 数据处理的近期和长期挑战。比赛的目的是在处理和处理包含高维元素光谱的非常大的数据集方面提出挑战。在比赛中，使用基于实验室的 LIBS 系统测量了 138 个样品。该数据集总共包含 70,000 个光谱，根据其元素组成分为 12 类。由于其广泛性和复杂性，该数据集在 LIBS 社区中是独一无二的。中心思想是模拟所谓的“样本外”分类（即根据相似的元素组成），这意味着各种实际应用。更重要的是，它反映了 LIBS 社区当前的专业水平以及 LIBS 方法本身的能力。