Location: Physiology and Pathology of Tree Fruits Research
Project Number: 2094-30600-001-000-D
Project Type: In-House Appropriated
Start Date: May 4, 2025
End Date: May 3, 2030
Objective:
Objective 1: Develop fruit harvest maturity assessment techniques that accurately project storage outcomes.
Sub-objective 1.A: Determine transcriptomic signatures that are relatable to fruit maturity in apple.
Sub-objective 1.B: Develop improved methods to build ML/AI predictive models for fruit quality.
Objective 2: Identify environmental conditions and management tools that economically reduce or eliminate fruit cold chain losses.
Sub-objective 2.A: Determine pear storage conditions that reduce storage physiological disorders and slow quality loss.
Sub-objective 2.B: Determine metabolic changes that project apple and pear physiological disorder risk during and following storage.
Sub-objective 2.C: Determine new sweet cherry cultivar-specific influences on storage longevity.
Approach:
Hypothesis 1.A: Experimentally imposed contrasts of at-harvest pome fruit maturity result in contrasts of fruit storability. Molecular phenotypes within this framework can be mined for gene activity signatures that are predictive of at-harvest maturity and fruit storability. Hypothesis 1.B: The accuracy of models that estimate trait values is dependent in the quality and granularity of trait data, and the use of computer vision tools increases the quality and granularity of trait data for visually scored traits. Project participants will select samples with apparent visual trait contrasts and capture images using specialized imaging stations. Images will be used to train and validate image analysis software to objectively rate images for chosen fruit maturity/quality variables, and analyze and correct for potential biases. Hypothesis 2.A.1: ‘d’Anjou’ pears can develop pithy brown core or cortex browning when stored at -0.5 °C in pO2 of less than 1 kPa. ‘d’Anjou’ pears will be harvested from different locations and at different maturities and stored in experimental CA chambers at different temperatures and atmospheric compositions. Disorder incidence, fruit quality and metabolic profile will be evaluated during and following 8 months of storage simulating commercial storage and post-storage cold chain conditions. Metabolic profile will be analyzed using a combination of extractions with LCMS and GCMS. Hypothesis 2.B.1: Specific metabolic changes are indicative of risk of internal browning related to CO2 sensitivity of apple. Experiments are directed at determining CO2 sensitivity in relation to pO2 using metabolic analysis and determining if monitoring those changes are applicable to detecting risk of internal browning during a real-world scenario, rapid CA conditioning ‘Honeycrisp’. Gene expression associated with biochemical changes already linked with CO2 sensitivity will be determined and, then monitored using LCMS and RNAseq during rapid CA conditioning. Metabolic changes will be compared with disorder outcome during and following 6 months of storage. Hypothesis 2.C: Sweet cherry metabolism and physiology influences fruit quality response to storage duration and storage temperature. Quality and aroma of commercially sorted and packed sweet cherries will be analyzed during simulated cold chains, both optimal or with disruptions to determine cultivars most resilient for long cold chains. will include rating mechanical or physiological fruit and stem damage or disorders [e.g. pitting (mechanical), pebbling (water loss due to respiration), shrivel (water loss due to transpiration), cracking (water absorption in excess of cuticle resilience)], stem quality (evaluation of abrasion, cutting, transpiration), stem loss, as well as firmness, acidity, and soluble solids content. For season 2 and 3, mesocarp samples will be flash frozen in LN2 and stored at -80 C for up to six months for volatile metabolite analysis. Volatiles representative of fruit aroma and potential off-flavors will be analyzed using GCMS.