The robustness of classification models is important in the real-world application of laser-induced breakdown spectroscopy (LIBS). This study using seven well-known chemometric models (ANN, CART, kNN, LDA, PLS-DA, SVM, and SIMCA) to classify LIBS spectral data from four types of typical plastics samples (ABS, Nylon, 3240, and its improved product FR-4). The robustness of these models for data acquired by different excitation sources (85 mJ pulse @ 1064 nm and 44 mJ pulse @ 532 nm) and over time (collected in November 2015, August 2016, December 2016 and September 2018) were evaluated and compared. The training set was constructed with the spectra acquired by 1064 nm wavelength laser excitation in August 2016. The effect introduced by 5 preprocessing methods (autoscaling, mean-centering, normalized by the total area, normalized by the maximum, and standard normal variate (SNV)) on the robustness of the model was investigated. The performance (accuracy and robustness) of different models was compared and analyzed. The results showed that the robustness of ANN, LDA, and PLS-DA model performed well and the ANN model was best. The experimental results demonstrated that the robustness of the model for LIBS spectra could be improved by using a suitable preprocessing method.

分类模型的鲁棒性在激光诱导击穿光谱法（LIBS）的实际应用中很重要。这项研究使用七个著名的化学计量学模型（ANN，CART，kNN，LDA，PLS-DA，SVM和SIMCA）对来自四种典型塑料样品（ABS，尼龙，3240及其改良产品）的LIBS光谱数据进行分类。 FR-4）。评估并比较了这些模型对于不同激发源（1064 nm处的85 mJ脉冲和532 nm处的44 mJ脉冲）以及随时间（2015年11月，2016年8月，2016年12月和2018年9月收集）采集的数据的稳健性。使用2016年8月通过1064 nm波长激光激发获得的光谱构建训练集。通过5种预处理方法（自动缩放，平均居中，通过总面积归一化，通过最大归一化，和标准正态变量（SNV））对模型的鲁棒性进行了研究。比较和分析了不同模型的性能（准确性和鲁棒性）。结果表明，ANN，LDA和PLS-DA模型的鲁棒性很好，而ANN模型则是最好的。实验结果表明，采用合适的预处理方法可以提高LIBS光谱模型的鲁棒性。