Using Machine Learning for Improvement of Reflected Spectrum Estimations of Colorimetric Probe

A machine learning-based application for estimating a reflected electromagnetic spectrum is presented in this article. Since the results of the spectrum estimation by our previously implemented cubic Hermite interpolation method had some drawbacks, and in line with the increasing application of machine learning in sensor technology, the new hypothesis is put forward, based on the assumption that better predictions could be achieved by introducing artificial neural networks. The input data of such networks consist of six measured values, which represent the intensities of reflected light at specific wavelengths, while the output is formed out of 36 points, which are used to predict the shape of the spectral curve in the range of 380-730 nm (to obtain a resolution of 10 nm). Colorimetric capabilities of the proposed method are compared with a commercial spectrophotometer and with our previously obtained results using the cubic Hermite interpolation. The machine learning-based method has proven to be an intelligent solution for the spectrum estimation, and the drawbacks from the previously used method were reduced significantly.

Automated summary

This article presents a machine learning-based application for estimating a reflected electromagnetic spectrum. By introducing artificial neural networks, the drawbacks from the previously used method were significantly reduced.