Calibration of temperature from the fluorescence spectra of lead–fluoride glass doped with erbium

Multivariate methods are applied to the calibration of temperature in the range from 299 to 423 K for the green fluorescence spectra of erbium in lead–fluoride glass doped with 0.5 mol % of erbium and 10 mol % of ytterbium. It is shown that the regression to latent structures using the combination of moving spectral windows is characterized, among the considered methods, by the lowest value (0.2 K) of the root-mean-squared error of prediction of temperature over the test set. Artificial neural networks using two principal components as input variables, the broadband regression to latent structures, the artificial neural network using all the spectral data samples as input variables, and regression to the principal components are inferior in accuracy of the temperature calibration.