Color-based clustering algorithm as a novel image analytical method for characterizing maltose crystallinity in amorphous food models

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.