Aim and Objective:
Near Infrared (NIR) spectroscopy data are featured by few dozen to
many thousands of samples and highly correlated variables. Quantitative analysis of such data usually
requires a combination of analytical methods with variable selection or screening methods.
Commonly-used variable screening methods fail to recover the true model when (i) some of the
variables are highly correlated, and (ii) the sample size is less than the number of relevant variables.
In these cases, Partial Least Squares (PLS) regression based approaches can be useful alternatives.
Materials and Methods :
In this research, a fast variable screening strategy, namely the
preconditioned screening for ridge partial least squares regression (PSRPLS), is proposed for
modelling NIR spectroscopy data with high-dimensional and highly correlated covariates. Under
rather mild assumptions, we prove that using Puffer transformation, the proposed approach
successfully transforms the problem of variable screening with highly correlated predictor
variables to that of weakly correlated covariates with less extra computational effort.
Results:
We show that our proposed method leads to theoretically consistent model selection
results. Four simulation studies and two real examples are then analyzed to illustrate the
effectiveness of the proposed approach.
Conclusion:
By introducing Puffer transformation, high correlation problem can be mitigated
using the PSRPLS procedure we construct. By employing RPLS regression to our approach, it can
be made more simple and computational efficient to cope with the situation where model size is
larger than the sample size while maintaining a high precision prediction.
The application of NIR spectroscopy in moisture determining of vegetable seeds
