Lithium compounds such as lithium hydride (LiH) and lithium hydroxide (LiOH) have a wide range of industrial applications, but are highly reactive in environments with H2O and CO2. These reactions lead to the ingrowth of secondary lithium compounds, which can alter the homogeneity and affect the application of particular lithium chemicals. This study performed an exploratory analysis of different lithium compounds using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Machine learning models are trained on the recorded spectral data to discriminate emission features that differ between LiH, LiOH, and Li2CO3 to perform high-fidelity classification. Support vector machine classifiers yield perfect prediction accuracy between the three compounds with optimal training time. Multivariate methods are then used to produce regression models quantifying the ingrowth of LiOH in LiH. Performing a mid-level data fusion of selected LIBS and Raman features with partial least-squares regression produces the superlative model with a root mean square error of 2.5 wt[Formula: see text] and a detection limit of 6.3 wt[Formula: see text].

中文翻译：

---

使用机器学习增强光谱技术分析锂老化

氢化锂 (LiH) 和氢氧化锂 (LiOH) 等锂化合物具有广泛的工业应用，但在具有 H 的环境中具有高度反应性2氧气和二氧化碳2。这些反应导致二次锂化合物向内生长，从而改变均匀性并影响特定锂化学品的应用。本研究使用激光诱导击穿光谱 (LIBS) 和拉曼光谱对不同的锂化合物进行了探索性分析。机器学习模型根据记录的光谱数据进行训练，以区分 LiH、LiOH 和 Li 之间不同的发射特征2一氧化碳3执行高保真分类。支持向量机分类器以最佳训练时间在三种化合物之间产生完美的预测精度。然后使用多变量方法生成回归模型，量化 LiH 中 LiOH 的向内生长。使用偏最小二乘回归对选定的 LIBS 和拉曼特征进行中级数据融合，生成均方根误差为 2.5 wt[公式：参见文本] 且检测限为 6.3 wt[公式：参见文本] 的最佳模型]。