Variable Selection for Referenceless Multivariate Calibration: A Case Study on Nicotine Determination in Flue-Cured Tobacco Powder by Near-Infrared (NIR) Spectroscopy

Abstract

Variable (wavelength) selection is critical in the multivariate calibration of spectra that improves prediction performance and provides clearer interpretation. Most variable selection methods use Y information (chemical reference of interest) to evaluating the importance of variables. In this work, a novel variable selection is proposed that requires no reference information. Replicate spectra of a sample under various acquisition conditions were used with an index to evaluate the importance of variables. The results show that the proposed method achieves better results than the original partial least squares model with a few available calibration samples. When a large number of calibration samples are available, this method provided comparable results to the state-of-the-art variable selection methods while reduced the risk of model over-fitting.