Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity.

Area of Science:

• * Plant Science
• * Fruit Genetics
• * Biochemistry

Background:

• * Existing models for red-flesh apples (Malus domestica Borkh) do not fully explain variations in pigmentation intensity and distribution.
• * Current evaluation methods use discrete color notation, which is insufficient for continuous color variations and non-homogeneous development.
• * A precise method is needed to capture genotypic diversity and identify genes influencing the red-flesh trait.

Purpose of the Study:

• * To develop and implement a multivariable approach for evaluating the red-flesh trait in apples.
• * To quantitatively analyze red-flesh pigmentation using image and color analysis.
• * To investigate the relationship between biochemical factors and flesh color expression.

Main Methods:

• * Developed a Python pipeline for quantitative image and color analysis of apple flesh pigmentation.
• * Compared the effectiveness of RGB (Red Green Blue) and CIEL*a*b* color spaces.
• * Employed targeted-metabolite analysis (UPLC-UV) for phenolic compounds, pH, and water content, followed by multivariate analysis.

Main Results:

• * The multivariable approach successfully dissected red-flesh pigmentation quantitatively.
• * CIEL*a*b* color space was more effective than RGB in discriminating genotypes.
• * Anthocyanin, flavonol, flavanol concentrations, and pH were closely correlated with flesh pigmentation.

Conclusions:

• * Combining color descriptors with chemical analyses effectively discriminated apple genotypes based on flesh color.
• * The red-flesh trait in apples is complex and influenced by multiple biochemical factors.