Specific glycomacropeptide detection via polyacrylamide gel electrophoresis with dual imaging and signal-fusion deep learning.

Herein, we report a sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) method featuring dual imaging and signal-fusion deep learning for specific identification and analysis of glycomacropeptide (GMP) in milk sample. Conventional SDS-PAGE methods lack specificity because of the signle staining of protein bands, and the overlap between GMP and β-lactoglobulin (βLg). Our dual imaging method generated a pair of complementary detection signals by recruiting intrinsic fluorescence imaging (IFI) and silver staining. Comparing the IFI image with the staining image highlighted the presence of GMP and differentiated it from βLg. Additionally, we trained a signal-fusion deep learning model to improve the quantitative performance of our method. The model fused the features extracted from the paired detection signals (IFI and staining) and accurately classified them into different mixing ratios (proportion of GMP-containing whey in the sample), indicating the potential for quantitative analysis on the mixing ratios of GMP added into whey sample. The developed method has the merits of specificity, sensitivity and simplilcity, and has potential to analysis of protein/peptides with unique IFI properties in food safety, basic research and biopharming etc.