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Updated: Aug 5, 2026

Characterization of Proteins by Size-Exclusion Chromatography Coupled to Multi-Angle Light Scattering (SEC-MALS)
Published on: June 20, 2019
Characterization of soybean proteins with various 7S/11S globulin ratios plus machine learning analysis for
Yilai Wan1, Xinyu Guo2, Zhen Liu2
1College of Life Sciences, Shihezi University, Shihezi 832003, China; Key Laboratory for Processing and Quality Safety Control of Characteristic Agricultural Products, The Ministry of Agriculture and Rural Affairs, Shihezi University, Shihezi, 832003, China.
Abstract:
Considering that the influence of 7S/11S globulin fraction-function relationships of the soybean proteins remains poorly understood, this study aimed to elucidate how 7S/11S globulin fraction governs the physicochemical, structural, and functional attributes of soybean proteins. Nine varieties of soybean proteins with various 7S/11S ratios ranging from 0.39 to 0.58 were chosen, followed by relevant physicochemical analysis. Meanwhile, their corresponding functional properties in terms of solubility, emulsifying and foaming capacity, and water/oil holding capacity were also evaluated. Results revealed that a higher ratio of 7S/11S promoted emulsification, foaming, and water/oil retention, whereas lower ratios favored gelation strength and thermal stability. Correlation and clustering analyses confirmed that particle size, ζ-potential, and β-sheet content were positively associated with improved functional attributes. It is crucial to reveal that the protein samples with higher β-sheet content tended to exhibit better functional properties compared to those with higher α-helix content. Importantly, machine learning (random forest+SHAP) further revealed that hydrophilic residues such as His and Glu positively regulated α-helix formation, while hydrophobic residues such as Val and Leu promoted β-sheet enrichment implying that secondary structure in term of α-helix/β-sheet ratio is one of the key structural characters for manipulating protein properties. This model can effectively predict the secondary structure and functional characteristics of soybean proteins (R2 = 0.89).
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