Machine vision system for automated spectroscopy
This paper describes a novel system based on the machine vision and machine learning techniques for fully automated, real-time identification of constituent elements in a sample specimen using laser-induced breakdown spectroscopy (LIBS) images. The proposed system is developed as a compact spectrum analyzer for rapid element detection using a commercially available video camera. We proposed a correlation-based pattern matching algorithm for analyzing single element spectra. However, the use of a high-speed laser and presence of numerous imperfections in the experimental setup require advanced techniques for analyzing multi-element spectra. We cast the element detection problem as a multi-label classification problem that uses support vector machines and artificial neural networks for multi-element classification. The proposed algorithms were evaluated using actual LIBS images. The machine learning approaches yielded correct identification of elements to an accuracy of 99%. Our system is useful in instances where a qualitative analysis is sufficient over a quantitative element analysis.
machine vision laser-induced breakdown spectroscopy libs multi-label classification element identification svm