Aiming at the demand for rapid detection of heavy metals in soil

a selection for characteristic wavelength variables was performed based on model population analysis method

and a method of X ray fluorescence spectroscopy was proposed for the detection of contents of heavy metals in farmland soils.X ray spectrum values of 91 configured soil samples were collected

and used to establish a soil heavy detection model.The characteristic wavelength variables were extracted by the multi feature series method.Firstly

the interval combination optimization algorithm(ICO) was used for rough selection of the wavelength.Secondly

the competitive adaptive reweighted sampling(CARS) was adopted to remove those irrelevant variables in the interval wavelength.Finally

the successive projections algorithm(SPA) was performed for wavelength reduction.The ICO-CARS-SPA algorithm with multiple features was used to select the feature variables in the X ray fluorescence spectrum to obtain 5(26

25

29

39

33) characteristic wavelength points.Based on this

a partial least squares(PLS) detection model for the contents of five heavy metals

i.e.Cu

Zn

As

Pb

Cr in soil was developed

which was compared with other traditional characteristic wavelength selection methods.Results showed that the variables selected by the ICO-CARS-SPA algorithm were combined with partial least squares(PLS) to have the best modeling effect.The determination coefficients of Cu

Zn

As

Pb

Cr were 0.993 3

0.992 6

0.995 6

0.993 2

0.988 6，respectively.The root mean square errors of five metals were 6.938 5

23698 4

3.632 6

8.510 6 and 14.764 5，and the mean relative biases of the prediction sets were 0.255 1

0.065 0

0.102 5

0.241 4

0.104 7

respectively.The ICO-CARS-SPA algorithm based on X-ray fluorescence spectroscopy combined with multi-feature series strategy could eliminate invalid wavelengths

thus increasing contribution of effective information

simplifying detection model complexity

and providing a theoretical support for selecting an appropriate feature band extraction method for the prediction model of soil heavy metal content.