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

Area of Science:

• Food Science
• Materials Science
• Crystallography

Background:

• Maltose crystallization impacts food texture and shelf-life.
• Understanding crystallization kinetics is crucial for food processing.

Purpose of the Study:

• To develop and validate a color-based clustering algorithm (CCA) for analyzing maltose crystallinity.
• To investigate the influence of protein on maltose crystallization mechanisms.

Main Methods:

• Image analysis using a novel color-based clustering algorithm (CCA).
• X-ray Diffraction (XRD) and Differential Scanning Calorimetry (DSC) for crystallization characterization.
• Morphological analysis of maltose crystals.

Main Results:

• CCA effectively recognized maltose crystals with high accuracy (R=0.9942).
• Maltose primarily crystallized into anhydrate α-maltose and β-maltose monohydrate.
• Protein altered crystallization pathways by affecting β-maltose mutarotation and recrystallization.
• Maltose crystallization correlated with molecular mobility, as indicated by CCA-derived Avrami indexes.

Conclusions:

• CCA offers a rapid, quantitative alternative to XRD and DSC for assessing maltose crystallinity.
• The study provides insights into protein-mediated modulation of sugar crystallization.
• CCA shows significant potential for real-time monitoring of sugar crystallization in industrial applications.