Laser-induced breakdown spectroscopy (LIBS) is an analysis technique that determines the elemental composition of a target material. Metal scraps have a range of shapes and are contaminated with other substances such as paint or dirt. This makes it difficult to recognize each piece of metal scrap accurately and to obtain clear LIBS emission spectra of the target metals. In this study, two image processing algorithms are proposed to measure the three-dimensional shapes of metal scraps and to calculate the optimized (i.e., relatively clean and flat) surface areas of metal scraps. It was confirmed that 25% higher maximum classification accuracy was achieved when LIBS spectra were acquired from optimized rather than non-optimized (i.e., contaminated) surfaces.

激光诱导击穿光谱 (LIBS) 是一种分析技术，可确定目标材料的元素组成。金属废料有多种形状，并被油漆或污垢等其他物质污染。这使得很难准确识别每块金属废料并获得目标金属的清晰 LIBS 发射光谱。在这项研究中，提出了两种图像处理算法来测量金属废料的三维形状并计算金属废料的优化（即相对干净和平坦）的表面积。经证实，当从优化而非非优化（即污染）表面获取 LIBS 光谱时，最大分类精度提高了 25%。