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Study on near-infrared spectral model transfer method of 7S and 11S protein content between different forms of soybean based on migration learning without standard samples.

Liqing Wang,Dandan Wu,Chang Liu,Hairong Zhang,Zhipeng Fan,Feng Liu,Dianyu Yu

Published 2025 in International Journal of Biological Macromolecules

ABSTRACT

The contents and ratios of 7S and 11S globulins are crucial for the nutritional value and functional properties of soybean proteins. Typically sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) is used to detect 7S and 11S globulin in soybeans however this method involves slow analysis procedures and high costs. Near-infrared (NIR) spectroscopy technology has emerged for detecting soybean protein content, enabling rapid non-destructive testing with advantages of convenient measurement, minimal sample processing requirements, and simultaneous determination of multiple components. To resolve the issue of shared quantitative prediction models between NIR spectroscopy-based 7S and 11S protein content predictions for various soybean seed and soybean powders, a transfer method of standard-free model based on transfer learning (TL) was proposed. Firstly, the NIR data of different forms of soybean samples were collected, and the near-infrared prediction models of 7S and 11S protein content were established. Secondly, the direct standardization (DS) and piecewise direct standardization (PDS) algorithms were improved to propose a DS-PDS-based model transfer method, with the influence of the sequence of preprocessing and model transfer algorithm on overall model transfer scheme was explored. Then, IRM is used to force the model to learn invariant features with causal relationship with labels by constraining the optimal classifier consistency of the model in different environments. Finally, aiming at standard sample sets corresponding to master-slave spectra required by traditional model transfer methods, the model transfer effect was investigated using a model transfer method based on standard-free migration learning. Results showed that the model transfer method based on without standard transfer learning was more suitable for 7S and 11S globulin content modeling between soybean seeds and soybean powders. It is intended to provide efficient and accurate 7S and 11S protein content detection methods for soybean processing enterprises and support quality control of soybean protein products and production of functional products.

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