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

Area of Science:

• Agricultural Science
• Food Science
• Genetics

Background:

• Consumer preferences drive fruit quality demands in modern breeding.
• Defining specific fruit quality attributes impacting consumer liking remains a challenge.
• Data-driven approaches are needed to link fruit traits with consumer perception.

Purpose of the Study:

• To statistically model the relationship between fruit quality parameters and consumer liking.
• To identify critical thresholds in fruit quality attributes that significantly influence consumer preference.
• To support molecular breeding programs with consumer-centric quality standards.

Main Methods:

• Utilized extensive historical datasets including sensory, biochemical, and genomic information from blueberries.
• Applied multivariate adaptive regression splines (MARS) for nonlinear spline regression to identify quality thresholds.
• Implemented genomic selection (GS) for five fruit quality traits, both raw and MARS-classified.

Main Results:

• MARS successfully identified specific fruit quality trait values significantly affecting consumer perception.
• Established empirical benchmarks for fruit quality based on consumer liking thresholds.
• Demonstrated the effectiveness of GS for fruit quality traits using both original and MARS-classified data.

Conclusions:

• A pioneering consumer-centric, data-driven approach to defining fruit quality standards has been developed.
• The study provides new empirical benchmarks for fruit quality, crucial for breeding programs.
• This methodology has broad applicability across different species for molecular breeding.