Abstract: Objective　To establish an effective method for distinguishing and identifying normal people and breast cancer patients based on Fourier transform mid-infrared spectroscopy technology. Methods　The serum samples of 86 female normal people and 85 female breast cancer patients were collected, and the spectra of the serum samples were drawn for the two groups of people. The principal component information of the two types of serum samples were extracted. The principal component score 2D and 3D scatter plots were drawn, and the scores of principal component 1-10 (PC1-PC10) were further calculated. Using the principle of discriminant analysis to establish a discriminant analysis model, and all samples were judged based on Mahalanobis distance. The performance index scores of models built under different spectrum preprocessing methods were calculated within the wave number range of 3 931-619 cm-1, and the best preprocessing method was selected to build the model. Results　There were significant differences in the peak intensities of the spectrum at wave numbers 3 363 cm-1, 2 360 cm-1, 1 641 cm-1, 1 552 cm-1, and 663 cm-1 between female normal population and female breast cancer patients (P＜0.05). The results of principal component analysis showed that there were significant differences in PC1-PC4 between two groups (P＜0.05), and there were no significant differences in PC5~PC10 between two groups (P>0.05). Compared with the normal group, the Mahalanobis distance to N in the breast cancer group is higher, and the Mahalanobis distance to C is lower (P＜0.05), and the positive judgment rate of the validation set of the built model was 100%. The model built without any processing on the spectrum was the best, and the performance index score was 94.1 points. Conclusion　Fourier transform mid-infrared spectroscopy can be used to identify and distinguish normal people and breast cancer patients, and it is expected to become a method for assisting breast cancer diagnosis.

Key words: breast neoplasms, serum, diagnosis, spectroscopy, Fourier transform infrared, discriminant analysis, Fourier transform mid-infrared spectroscopy