Color-based clustering algorithm as a novel image analytical method for characterizing maltose crystallinity in amorphous food models. (June 2021) - Wu, Yaowen; Mou, Tian; Ma, Keying; Fan, Fanghui
Graphical abstract: Highlights: Color-based clustering algorithm was established to recognize maltose crystals. Maltose mainly crystallized to anhydrate α -maltose and β -maltose monohydrate. Protein changed the mutarotation and recrystallization of unstable β -maltose. Crystal formation mechanism was governed by Tg -related molecular mobility. CCA method provided a rapid and quantitative measure for maltose crystallinity. Abstract: Maltose crystallization affects the processibility and stability of sugar-rich foods. This study introduced a color-based clustering algorithm ( CCA ) to analyze crystallinity from the images of amorphous maltose/protein models. The XRD and DSC were also implemented in maltose crystallization characterization and validated the CCA analysis. The results indicated that CCA could effectively recognize maltose crystals ( R  = 0.9942), and amorphous maltose mainly crystallized to anhydrate α -maltose and β -maltose monohydrate according to its morphological aspects measured by CCA, XRD, and DSC . However, protein could change the mechanism of maltose crystal formation by disturbing the mutarotation and recrystallization processes of unstable β -maltose. Besides, maltose crystal formation and crystallinity were governed by molecular mobility as the CCA -derived Avrami indexes changed with the Strength parameter. Compared to XRD and DSC, the proposed CCA can provide a rapid and quantitative measure for maltose crystallinity and has great potential applications in the online detection of sugar crystallization.