Quantitative analysis of dried serum FTIR spectra based on correlation Analysis-Interval random Frog-Partial least squares

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 327; p. 125427
• Main Authors: Zhang, Ruojing, Zhang, Xianwen, Guo, Hongrui, Zhang, Zhushanying, Gao, Yuan, Xie, Qinlan, Cao, Huimin
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 15.02.2025
• Subjects: Adult; Algorithms; Blood Glucose - analysis; Blood Proteins - analysis; Correlation analysis; Female; Humans; Infrared spectrum; Interval random frog; Least-Squares Analysis; Lipids - blood; Male; Serum biochemical parameters; Spectroscopy, Fourier Transform Infrared - methods; Interval random frog; Serum biochemical parameters; Infrared spectrum; Correlation analysis
• DOI: 10.1016/j.saa.2024.125427
• ISSN: 1386-1425

[Display omitted] •The method proposed in this study can quickly and accurately quantify 9 major serum components simultaneously.•The correlation between 18 spectral absorption peaks and 26 serum biochemical parameters was preliminarily explored.•For the first time, the quantitative analysis of apolipoprotein A1 was achieved using spectroscopic methods. Serum biochemical markers are widely used in clinical practice but often require expensive, specific reagents, complex instruments, and prolonged result waiting times. Infrared spectroscopy offers multiple advantages for serum analysis, such as reagent-free testing and the ability to quickly and directly measure multiple parameters simultaneously. This study collected serum samples from 66 healthy subjects to explore the relationship between dried serum infrared spectra and biochemical parameters, and to investigate the feasibility of simultaneously quantifying nine major serum components using dried serum infrared spectra. Initially, correlation analysis was conducted between spectral data and biochemical parameters, and the correlation spectral bands of glucose, protein and lipid were determined according to the correlation results. Subsequently, the interval random frog (IRF) algorithm was utilized to select the optimal characteristic wavenumbers of the correlated spectral bands, extracting the most informative spectral variables and constructing partial least squares (PLS) quantitative models. This method successfully achieved rapid and accurate quantification of nine major components in serum, including glucose, total protein, albumin, apolipoprotein A1, apolipoprotein B, total cholesterol, triglycerides, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. The experimental results showed that the correlation coefficient (Rp) range in the test set was 0.8892–0.9941. Among them, the quantification of total cholesterol yielded the highest Rp, corresponding to a root mean square error (RMSEP) of 7.2425 mg/dL in the test set, while the quantification of glucose yielded the lowest Rp, with an associated RMSEP of 2.3683 mg/dL. The Correlation Analysis (CA)-IRF-PLS method developed in this study outperformed the conventional PLS method, the direct use of the successive projection algorithm (SPA)-PLS quantitative method and other reported quantitative techniques, providing a novel approach for the real-time determination of clinical parameters in serum.