Classification of Electromagnetic Spectrum in the Visible Range Using Machine Learning
Spectrophotometers are instruments that measure parameters of samples as a function of the wavelength and work with a source of broad spectrum light a diffractive element and a detector. Those instruments are widely used in chemistry, physics and medicine labs, among others. The design of a spectrophotometer in the visible range of the electromagnetic spectrum is presented in this work and consists of a white LED, a holographic grating and a Samsung camera as a detector; the spectrum generated by placing a liquid sample in the spectrophotometer is analyzed by three different artificial intelligence algorithms: artificial neural networks (ANNs), convolutional neural networks (CNNs), and support vector machines (SVMs). These types of algorithms are part of the machine learning techniques that are used to solve classification and regression problems, for example, facial and speech recognition, efficient searching engines and medical diagnostic, among others. In this manuscript, these algorithms were implemented to determinate which one is the best to classify the samples, considering the accuracy and execution time.
This research is partially supported by grant CIC-UMSNH-4.23. The authors also thank ABACUS Laboratorio de Matemticas Aplicadas y C´omputo de Alto Rendimiento del CINVESTAV-IPN, grant CONACT-EDOMEX-2011-C01-165873, for providing computer resources.
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