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2017 ASHS Annual Conference

Utilizing Visible/Near-infrared Spectroscopy as a Non-destructive Phenotyping Method in the WSU Apple Breeding Program

Wednesday, September 20, 2017
Kona Ballroom (Hilton Waikoloa Village)
Jamie L. Coggins, Washington State University - Tree Fruit Research and Extension Center, Wenatchee, WA
Kate M. Evans, Washington State University - Tree Fruit Research and Extension Center, Wenatchee, WA
Washington state is the number one apple (Malus x domestica Borkh.) producer in the U.S. and grows at least 28 varieties commercially. Of those varieties, none have been bred for the Washington state growing region. The Washington State University Apple Breeding Program (WABP) aims to develop new and improved varieties with higher eating qualities that are better suited for the WA apple growing region. WABP uses a variety of destructive analytical tests, such as firmness, soluble solids and titratable acidity (instrumental), as well as appearance and eating quality (sensory) traits, to evaluate selections throughout the season, both at harvest and after several months of refrigerated storage. Young seedling trees typically have low fruit numbers; incorporating non-destructive evaluations could increase the number of traits evaluated per selection, including dry matter content (DM). In 2015 and 2016, fruit from the WABP advanced selections were harvested and stored for two months at 4oC. Fruit was evaluated after storage using the above-mentioned analytical and sensory tests with the addition of destructive DM measurements. Data was compared to both visible (VIS) and near-infrared (NIR) spectral outputs to determine non-destructive measurement models. Correlations between DM and sensory fruit quality traits were also calculated. A model was successfully developed for DM, confirming the previously published studies showing the potential of using the NIR spectroscopy region between 729-925nm to measure DM. Models for firmness, soluble solids content and titratable acidity were developed with varying degrees of predictive accuracy. The potential for utilizing these results in the WABP are presented.
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