Abstract
Vacuum ultraviolet laser induced breakdown spectroscopy (VUV-LIBS) experiments were carried out on pharmaceutical samples and machine learning techniques were applied to analyze the samples. The motivation for the application of these machine learning techniques is the classification of analytes, allowing us to distinguish pharmaceuticals from one another based on their spectra. Three machine learning techniques have been compared, self-organizing maps (SOM), support vector machines (SVM) and convolutional neural networks (CNN). For multiclass and 1vs1 testing CNNs appeared to perform the best of the three machine learning techniques on the relatively small number of pharmaceutical LIBS spectra used in this study.
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