Development of a texture analyzer-based method for waxiness evaluation and its application to phenotypic variation analysis in postharvest Chinese chestnut kernels

Abstract

Waxiness is a critical textural factor determining the taste and consumer preference of postharvest Chinese chestnut (Castanea mollissima Blume) kernels. However, its genetic improvement is hindered by the lack of an accurate, high-throughput phenotyping method and a clear understanding of its inheritance. In this study, a texture analyzer-based method was developed to objectively evaluate waxiness and analyze the associated phenotypic variations. Sensory waxy scores, starch composition, and texture parameters of 43 chestnut germplasms were evaluated. Notably, high-waxiness germplasms (waxy score > 80) exhibited significantly higher amylopectin content but had lower hardness and chewiness after steaming. Integrated analyses, including UPGMA clustering, principal component analysis, and Pearson correlation, convergently identified hardness as the optimal parameter for predicting waxiness. A robust regression model (Waxiness = 106.3041–0.3055 × Hardness) was established to quantify waxiness based on instrumental hardness measurements. Notably, the method effectively quantified continuous waxiness variation among 20 F₁ hybrid kernel populations and uncovered significant parental effects, thereby demonstrating its power as a reliable phenotyping tool when breeding for chestnut quality. This study is an important exploration of the phenotypic variation characteristics of the chestnut waxiness trait and lays a foundation for breeding high-quality chestnut cultivars.

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