Key Laboratory of Optical Information Detection and Display Technology of Zhejiang, Zhejiang Normal University, Jinhua 321004, China.
Sensors (Basel). 2022 Apr 20;22(9):3129. doi: 10.3390/s22093129.
Laser-induced breakdown spectroscopy (LIBS) spectra often include many intensity lines, and obtaining meaningful information from the input dataset and condensing the dimensions of the original data has become a significant challenge in LIBS applications. This study was conducted to classify five different types of aluminum alloys rapidly and noninvasively, utilizing the manifold dimensionality reduction technique and a support vector machine (SVM) classifier model integrated with LIBS technology. The augmented partial residual plot was used to determine the nonlinearity of the LIBS spectra dataset. To circumvent the curse of dimensionality, nonlinear manifold learning techniques, such as local tangent space alignment (LTSA), local linear embedding (LLE), isometric mapping (Isomap), and Laplacian eigenmaps (LE) were used. The performance of linear techniques, such as principal component analysis (PCA) and multidimensional scaling (MDS), was also investigated compared to nonlinear techniques. The reduced dimensions of the dataset were assigned as input datasets in the SVM classifier. The prediction labels indicated that the Isomap-SVM model had the best classification performance with the classification accuracy, the number of dimensions and the number of nearest neighbors being 96.67%, 11, and 18, respectively. These findings demonstrate that the combination of nonlinear manifold learning and multivariate analysis has the potential to classify the samples based on LIBS with reasonable accuracy.
激光诱导击穿光谱（LIBS）光谱通常包含许多强度谱线，从输入数据集获取有意义的信息并压缩原始数据的维度已成为 LIBS 应用中的一个重大挑战。本研究旨在利用流形降维技术和集成 LIBS 技术的支持向量机（SVM）分类器模型，快速、非侵入式地对五种不同类型的铝合金进行分类。使用增广偏残差图来确定 LIBS 光谱数据集的非线性。为了避免维度灾难，使用非线性流形学习技术，如局部切空间排列（LTSA）、局部线性嵌入（LLE）、等距映射（Isomap）和拉普拉斯特征映射（LE）。还研究了线性技术（如主成分分析（PCA）和多维尺度（MDS））的性能，与非线性技术进行了比较。数据集的降维维度被分配为 SVM 分类器的输入数据集。预测标签表明，Isomap-SVM 模型具有最佳的分类性能，分类准确率、维度数和最近邻数分别为 96.67%、11 和 18。这些发现表明，非线性流形学习和多元分析的结合有可能基于 LIBS 以合理的精度对样品进行分类。
