Assessing Preharvest Field Temperature and At-harvest Fruit Quality for Prediction of Soft Scald Risk of ‘Honeycrisp’ Apple Fruit during Cold Storage

Abstract

Honeycrisp' apple is susceptible to the postharvest chilling disorder soft scald that renders fruit unmarketable. Reducing or preventing this disorder is an important component of 'Honeycrisp' postharvest management. In commercial settings, advanced fruit maturity and orchard history contribute to an estimation of soft scald susceptibility, but additional at-harvest information indicative of soft scald risk would enable better management decisions. In this study, we obtained fruit from commercial orchards for 3 successive years, and assessed field growing degree days (GDD), field chilling hours (CH), and fruit quality metrics at harvest, followed by soft scald incidence assessment at 12 weeks of cold storage. The analyses indicated starch index, soluble solids content (SSC), internal ethylene concentration, titratable acidity (TA), peel background color, firmness, GDD, or CH do not reliably indicate fruit susceptibility to soft scald. However, SSC and TA were elevated in fruit that later developed soft scald, and a higher number of GDD also sometimes preceded soft scald, which is consistent with advanced fruit maturity that can enhance soft scald risk. Overall, results suggest that other tools may be required to accurately predict postharvest soft scald on a quality control laboratory scale. The statistical analyses applied to the present study would have utility for assessing other soft scald prediction tools or markers.

Occurrence of physiological browning disorders in stored ‘Braeburn’ apples as influenced by orchard and weather conditions

“…In postharvest biology, ROC curves have found application in fruit sorting when VIS/NIR non-destructive equipped graders are used to assess internal fruit quality (Walsh et al, 2020). And recently, Leisso et al (2019) used ROC curves to assess the influence of preharvest factors like GDD, chilling hours and other fruit quality parameters as binary classifiers to predict the risk of disorders developing in 'Honeycrisp' apples. Here we have followed a similar approach.…”