We demonstrate here, for the first time to the best of our knowledge, the method of classification and identification of metals, metal alloys using the color CCD images of femtosecond (fs) laser-induced plasma emissions. The non-gated color CCD images of the plasma emissions were used to train the machine learning algorithm for identification. We have also compared the obtained results with the fs-laser-induced breakdown spectroscopy (LIBS) results. The green channel in the RGB image was used for the classification and prediction of metals and metal alloys. The present work explores the possibility of identification of the aluminum, copper, bronze, and steel using a simple instrument such as the CCD. Each sample formed extended clusters in the classification performed using principal component analysis (PCA). The extracted features from the PCA were used as input to train the support vector machine (SVM) and for prediction and the results are intriguing.

中文翻译：

---

使用激光诱导击穿发射的彩色 CCD 图像结合机器学习识别金属和合金

据我们所知，我们在这里首次展示了使用飞秒 (fs) 激光诱导等离子体发射的彩色 CCD 图像对金属、金属合金进行分类和识别的方法。等离子体发射的非门控彩色 CCD 图像用于训练机器学习算法进行识别。我们还将获得的结果与 fs 激光诱导击穿光谱 (LIBS) 结果进行了比较。RGB 图像中的绿色通道用于金属和金属合金的分类和预测。目前的工作探索了使用 CCD 等简单仪器识别铝、铜、青铜和钢的可能性。每个样本在使用主成分分析 (PCA) 执行的分类中形成扩展集群。