A multispectral features fusion technique(MFFT) was developed for the quantitative analysis of adulterated components in flour samples in this paper.The MFFT consisted of iterative wavelet transform(AWT)

competitive adaptive reweighted sampling(CARS) and partial least squares regression(PLS)

utilizing the molecular information in Raman spectroscopy and the atomic information in laser induced breakdown spectroscopy(LIBS) to extract important features efficiently.In the MFFT

AWT and CARS were combined to select features in the Raman spectra and LIBS spectra

respectively

which were fused for PLS modeling.As a result

the MFFT model was generated to quantitate the adulterant contents in flour samples.To verify the performance of the MFFT method

the titanium dioxide and aluminum potassium sulfate in flour samples were quantitated.When compared with the prediction models based on individual spectroscopy

the correction coefficients of the MFFT models for titanium dioxide and aluminum potassium sulfate were significantly improved from 0.884

0.877 of Raman models to 0.981

0.980

whose root mean square errors were also decreased from 0.151

0.154 of Raman models to 0.069

0.068

respectively.Results indicated that the MFFT is a promising tool for quantitative analysis of adulterants in flour samples

extracting the molecular information in Raman spectra and the element information in LIBS spectrua accurately.The MFFT could make Raman spectroscopy and LIBS spectroscopy complementary for mutual correction

suppressing the effects of flour matrix on the quantitative analysis of adulterated components effectively

improving the prediction accuracy of the model significantly.