Optimisation of High-Throughput Pre-Treatment for Amylose Quantification in Wheat and Its Correlation with Wheat Quality Traits

Abstract

Starch constitutes a major fraction of mature wheat kernels and holds significant nutritional value, with its content and structural composition serving as critical determinants of wheat quality attributes and processing performance. This study systematically reconfigured conventional pretreatment protocols for colourimetric amylose determination to evaluate the impact of various procedural combinations on quantification accuracy. Pretreatment efficiency was optimised, and the resulting method was validated using an expanded cultivar cohort. Concurrently, correlation analysis and hierarchical clustering with heatmaps were employed to elucidate associations between standard wheat quality parameters and amylose content. Results indicated that the presence or absence of a defatting step significantly influenced determination outcomes. The optimal protocol entailed milling, impurity removal, and ultrasonic defatting prior to dual-wavelength colourimetric analysis. This method maintained high data reproducibility while reducing defatting time from 6 hours (conventional method) to 1 hour via ultrasonic treatment, rendering it highly suitable for high-throughput batch analysis. Significant inter-cultivar variations were observed in both absolute amylose content and the amylose-to-amylopectin ratio. Notably, Zhoumai 49 exhibited the highest amylose content, while Zhoumai 33 displayed the highest amylose proportion; both cultivars demonstrate strong potential for high-amylose wheat breeding programs. Functional germplasm traits could be preliminarily assessed using routine quality parameters, as protein and moisture contents exhibited discernible trends correlating with amylose levels. These findings provide an optimised, efficient pretreatment protocol for amylose quantification and offer valuable methodological insights for targeted wheat starch quality improvement and cultivar screening.