Gray wolf optimizer(GWO) algorithm

which is based on swarm intelligence

is easy to implement due to its few parameters and simple structure. However

to our knowledge

few studies used GWO for the spectral analysis. In this study

the GWO was introduced into the variable selection of NIR spectra.Taking corn dataset as an example

the performance

numbers of iterationsnumbers of wolves and efficiency of GWO algorithm were investigated.Based on this

a partial least squares(PLS) model was established to determine the protein

fat

moisture and starch contents in corn samples.Results showed that GWO algorithm was very efficient.With optimized parameters

the retention variable numbers of GWO algorithm for protein

fat

moisture and starch were 19

19

14 and 34

respectively.Compared with root mean square error of prediction(RMSEP) values of the full wavelength PLS model for the four components

those of the GWO-PLS model decreased from 0.245 8

0.122 4

0.339 8 and 1.105 8 to 0.147 7

0.080 1

0.176 2 and 0.739 8

with their decreasing percentages of 40%

35%

48% and 33%

respectively.Meanwhile

the correlation coefficients were increased accordingly.Therefore

GWO algorithm could improve the prediction accuracy of the PLS model apparently with high efficiency and fewer selected variables.It is a promising method for variable selection of NIR spectroscopy.