Determination of seawater COD spectra using double-loop contraction and sorted frog optimization

Abstract

This study develops a novel double-loop contraction and C value sorting selection-based shrinkage frog-leaping algorithm (double-contractive cognitive random field [DC-CRF]) to mitigate the interference of complex salts and ions in seawater on the ultraviolet–visible (UV–Vis) absorbance spectra for chemical oxygen demand (COD) quantification. The key innovations of DC-CRF are introducing variable importance evaluation via C value to guide wavelength selection and accelerate convergence; a double-loop structure integrating random frog (RF) leaping and contraction attenuation to dynamically balance convergence speed and efficiency. Utilizing seawater samples from Jiaozhou Bay, DC-CRF-partial least squares regression (PLSR) reduced the input variables by 97.5% after 1,600 iterations relative to full-spectrum PLSR, RF-PLSR, and CRF-PLSR. It achieved a test R2 of 0.943 and root mean square error of 1.603, markedly improving prediction accuracy and efficiency. This work demonstrates the efficacy of DC-CRF-PLSR in enhancing UV–Vis spectroscopy for rapid COD analysis in intricate seawater matrices, providing an efficient solution for optimizing seawater spectra. HIGHLIGHT The double-contractive cognitive random field-partial least squares regression algorithm reduces input 97.5% with high seawater chemical oxygen demand (COD) prediction accuracy. Integrating variable selection and shrinkage frog leaping optimizes efficiency and robustness. An outer loop strategy and attenuation function balance convergence and efficiency. The C value criterion evaluates variables for seawater COD modeling. The algorithm improves ultraviolet–visible detection for rapidly determining seawater COD.;

Keywords

• chemical oxygen demand
• random frog
• seawater monitoring
• spectrum
• uv–vis spectrophotometry