Nanopore Analysis of Malto-oligosaccharides from Amylose, Amylopectin, and Cyclodextrin Hydrolysates

Malto-oligosaccharides, structurally diverse glucose polymers comprising 2–10 monosaccharide units, play vital roles in food science, pharmaceuticals, and glycobiology. However, their precise structural characterization remains challenging due to subtle chain-length variations and isomeric complexity that confound conventional analytical techniques. Here, we utilize a phenylboronic acid (PBA)-functionalized heterooctameric Mycobacterium smegmatis porin A nanopore (MspA-90PBA) to identify saccharides from glucose to maltooctaose. By simultaneous consideration of six event features via machine learning, a model was established and an over 98% validation accuracy was achieved. The method was validated using γ-cyclodextrin acidolysis products and subsequently applied to the differentiation of amylose and amylopectin, demonstrating its capability to discriminate between linear and branched polysaccharides. Finally, this technique was extended to the analysis of starch directly extracted from crop materials, confirming its robustness in real-world sample analysis.