Ensemble methods and data argumentation by noise addition applied to the analysis of spectroscopic data

Summary

Near-infrared spectroscopy has gained great acceptance in the industry due to its multiple applications and versatility. Sometimes, however, the construction of accurate and robust calibration models involves the collection of a large number of samples with related reference analysis that can complicate and prolong the calibration stage. In this paper, ensemble methods and data augmentation by noise simulation have been applied to spectroscopic data in combination with PLSR to obtain robust models able to handle different types of perturbations likely to affect NIR data. Several types of noise have been investigated as well as different ensemble methods focused on obtaining robust PLS models able to predict both the original and the perturbed test data. The suitability of ensemble methods to perform robust calibration models has been investigated and compared to extended multiplicative signal correction (EMSC) and other calibration approaches in a real case of temperature compensation. Extended multiplicative signal correction (EMSC) and ensemble methods seem to be the most appropriate methods yielding the best results in terms of accuracy and prediction ability with a reduced calibration data set.