Temperature correction of infrared spectroscopy, aiming at removing nonlinear temperature effects on the spectra, has been a basic problem in Infrared Spectroscopy Quantitative Analysis for a long time. By improving the linearity of spectrum, we can get a better prediction result from multivariate calibration models such as PLS. Previous temperature correction methods need to record all the collecting temperatures of the spectra, not only for those in the training set, but also for those in the test set, which brings a lot of difficulties to real application. In this paper, we propose a new model-based method for temperature prediction and temperature correction of infrared spectral data. It works by modeling the temperature effect in the spectrum, so that we can get a predicted collecting temperature of the spectrum in the test set by its variations. The predicted collecting temperature is then regarded as the real collecting temperature. In this paper, we carry out three experiments to support our theory. In the first part, we test the temperature prediction results and temperature correction results of some spectra, which are collected from the water-ethanol binary mixtures in the test set. The results show that our method is effective and efficient. In the second part, we compare our method with CPDS, a classic temperature correction method proposed by Wulfert. We repeat the experiment design in his experiment section, using our method and his data set. We carry out the temperature correction experiment in this paper and then compare the temperature correction results of our method with his. Results show that, despite the lack of collecting temperatures of the test set spectra, our method can also offer a satisfactory result of temperature correction as the classic CPDS method. In the last part, we combine the predicted concentrations of our model and those of PLS model, and then propose a series of concentration prediction methods based on iterative algorithm. The three experiments show that, despite the absence of measured collecting temperatures of the spectra in the test set, our method proposed in this paper is still effective and efficient to remove the temperature effect in the spectra, thereby making temperature correction easier to use in real application.
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