A novel model transfer method based on least angle regression(LAR) combined with simple linear regression direct standardization(SLRDS) was proposed to solve the problem of poor generality in model analysis of near infrared spectroscopy technology.In this method

the near infrared spectral data of the samples were preprocessed by wavelet transform

then the wavelength points for the spectral characteristic of the pre processed samples were screened by LAR.Finally

the selected wavelengths of the samples were corrected by SLRDS.Near infrared spectroscopy data for gasoline and drug samples were used to verify the performance of LAR-SLRDS.The spectral differences for the gasoline datasets C7

C8

C9 and C10 were 0.002 8

0.002 7

0.002 6 and 0.002 7

and the prediction standard deviations were 0.410 6

0.849 2

1.034 9 and 1.215 8

respectively.The spectral differences in activity

hardness and weight components of the drug datasets were 0.030 0

0.031 8 and 0.033 6

and the predicted standard deviations were 1.933 8

0.440 2 and 2.130 9

respectively.The experimental results showed that the LAR-SLRDS algorithm could not only eliminate the difference between the main and instrument spectra

but also improve the accuracy and stability of the PLS quantitative model.