Accurate prediction of hyaluronic acid concentration under temperature perturbations using near-infrared spectroscopy and deep learning.
Spectrochim Acta A Mol Biomol Spectrosc
; 317: 124396, 2024 Sep 05.
Article
en En
| MEDLINE
| ID: mdl-38733911
ABSTRACT
Accurate prediction of the concentration of a large number of hyaluronic acid (HA) samples under temperature perturbations can facilitate the rapid determination of HA's appropriate applications. Near-infrared (NIR) spectroscopy analysis combined with deep learning presents an effective solution to this challenge, with current research in this area being scarce. Initially, we introduced a novel feature fusion method based on an intersection strategy and used two-dimensional correlation spectroscopy (2DCOS) and Aquaphotomics to interpret the interaction information in HA solutions reflected by the fused features. Subsequently, we created an innovative, multi-strategy improved Walrus Optimization Algorithm (MIWaOA) for parameter optimization of the deep extreme learning machine (DELM). The final constructed MIWaOA-DELM model demonstrated superior performance compared to partial least squares (PLS), extreme learning machine (ELM), DELM, and WaOA-DELM models. The results of this study can provide a reference for the quantitative analysis of biomacromolecules in complex systems.
Texto completo:
1
Banco de datos:
MEDLINE
Idioma:
En
Revista:
Spectrochim Acta A Mol Biomol Spectrosc
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2024
Tipo del documento:
Article