A new concept based on ensemble strategy and derivative for the quantitative analysis of infrared data

Preprocessing and variable selection are the most widely used strategies to develop accurate predictive models based on infrared spectroscopy. In our study, a new conception that the derivative combined with ensemble strategy based on competitive adaptive reweighted sampling (CARS), stability competitive adaptive reweighted sampling (SCARS), Monte Carlo uninformative variables elimination (MCUVE), and bootstrapping soft shrinkage (BOSS) is put forward. The proposed concept makes the best of the derivative spectra information and successfully combines the strengths of derivative spectra, CARS, SCARS, MCUVE, BOSS, and ensemble submodels. Compared with other methods in this study, this new method can establish good calibration models without increasing the complexity from the perspective of an end user. Also, overfitting issues can be prevented. Derivative1st-ECARS and Derivative1st-ESCARS have shown significant improvements in partial least regression calibration based on the experiments of three datasets. The proposed concept shows great potential of the chemometrics approaches applied to infrared data in multivariate calibration.