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Selecting Mid-infrared Filters for a Machine Learning Chemical Classifier by Genetic Optimization

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Abstract

We use a genetic algorithm to optimize six mid-infrared filters for a machine learning classifier that identifies chemicals from their infrared transmission. The model is tested by classifying 15 chemical classes via simulations with noise.

© 2022 The Author(s)

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