Repeated interval random frog (RIRF) algorithm based on FTIR and application to quantitative analysis of serum proteins

Abstract

Infrared spectroscopy is widely used for biomarker detection due to its non-invasive, rapid, and sensitive nature, but current methods exhibit limited stability in feature band extraction. This study introduces the repeated interval random frog (RIRF) algorithm to optimize protein feature band extraction in serum. The FTIR spectra of 66 collected blood samples were used for the study, and the IRF algorithm was run several times in order to obtain different subsets of feature variables. The feature bands in these subsets were then analyzed and selected for fusion with high contribution to protein content prediction. The feature bands with high contribution rate and high stability were finally extracted, which in turn improved the prediction accuracy of the quantitative model. In comparative analysis, RIRF outperformed IRF, CARS, SPA, and UVE for apolipoprotein B extraction, increasing the training correlation coefficient (Rc) and prediction correlation coefficient (Rp) to 0.9144 and 0.8504, respectively, while reducing feature numbers from 137 to 77. Additionally, feature fusion of albumin, apolipoprotein, C-reactive protein, total protein, and immunoglobulin A showed significant improvements in model predictive power. The Rp of albumin was improved from 0.9515 to 0.9601 for the single best extraction; the Rp of apolipoprotein A1 was improved from 0.6526 to 0.7312; the Rp of C-reactive protein was improved from 0.7532 to 0.8214; and the Rp of total protein was improved from 0.9595 to 0.9691. The RIRF algorithm significantly improves the prediction accuracy and provides an important reference in the application of biomarker detection and infrared spectral analysis techniques.