Vis-NIR Spectroscopic Discriminant Analysis Applied to Serum Breast Cancer Screening

The research and development of fast, simple and accurate technology for breast cancer screening has important application value. In this paper, the discriminant analysis models for breast cancer and normal control samples were established using serum Vis-NIR spectroscopy combined with the equidistant combination-partial least squares-discriminant analysis (EC-PLS-DA) method. Standard normal variable (SNV) method was adopted for the spectral pretreatment of serum samples to improve spectral prediction. The parameters of the selected optimal EC-PLS-DA model were initial wavelength (I) = 1976 nm, ending wavelength (E) = 2396 nm, number of wavelengths (N) = 31, number of wavelength gaps (G) = 7 and the number of PLS latent variables (LV) = 10, respectively. In modelling, the calibration, prediction and total recognition accuracy rates were 96.0%, 97.5%, and 96.7%, respectively. Using independent validation samples not involved in modelling, the positive, negative, and total recognition accuracy rates were 85.0%, 90.0%, and 87.5%, respectively. The results showed the feasibility of serum Vis-NIR spectroscopic applied to discriminant analysis of breast cancer and normal control samples. The EC-PLS-DA method can extract information wavelengths, improve the recognition accuracy of discriminant analysis and reduce wavelength model complexity. The relevant wavelength model can provide valuable references for specialized spectrometer design and clinical application. The analytical technique is simple and novel, and has potential application in breast cancer screening.