A consumer-oriented approach to define breeding targets for molecular breeding.

Flavor is a crucial aspect of the eating experience, reflecting evolving consumer preferences for fruits with enhanced quality. Modern fruit breeding programs prioritize improving quality traits aligned with consumer tastes. However, defining fruit-quality attributes that significantly impact consumer preference is a current challenge faced by the industry and breeders. This study proposes a data-driven approach to statistically model the relationship between fruit-quality parameters and consumers' overall liking. Our primary hypothesis suggests that the interplay between fruit-quality attributes and consumer preferences may reach a critical value, serving as new empirical benchmarks for fruit quality. Using extensive historical datasets accounting for sensory, biochemical, and genomic information described in blueberry, we first demonstrated that multivariate adaptive regression splines (MARS) could be used to identify specific values of fruit-quality traits that significantly affect consumer perception by using nonlinear spline regressions on estimating threshold points. We harnessed genomic information and carried out genomic selection (GS) for five fruit-quality traits evaluated on the original scale and after classified via the MARS approach. This study provides a pioneering consumer-centric and data-driven approach to defining fruit-quality standards and supporting molecular breeding that has broad applications to breeding programs from any species.