2019 ASHS Annual Conference

Blueberry (Vaccinium sp.) is a well-recognized, health-protective fruit with functionality derived from micronutrient and phytochemical density, particularly anthocyanins. Despite their importance, little is known about the genetic underpinnings controlling anthocyanin accumulation in this crop. Further elucidation of these mechanisms could facilitate the development of new blueberry cultivars with improved nutritional qualities; therefore, the purpose of this research was to develop an ad-hoc phenotyping method suitable for screening the total anthocyanin content (TAC) in blueberry for genetic association studies. The most commonly used method for quantifying TAC involves extracting anthocyanins from whole blueberries, which may bias results given that anthocyanins are expressed mainly in the skin tissue. To assess this, 30 genetically diverse blueberry genotypes were selected and quantified for TAC using two methods: the commonly used whole-fruit method (“whole-method”), and one which uses only the skin tissue for extraction of anthocyanins (“skin-method”). In addition, fruit from each sample was photographed and then analyzed using an image processing software, GiNA, in order obtain total surface area (TSA) per sample. Fruit size using the whole-method provided a significantly higher correlation with the observed TAC (r = 0.85) when compared to that of the skin-method (r = -0.43), indicating a potential bias and lack of precision in using the whole-method for genetic studies. Finally, given that quantifying TAC using high-performance liquid chromatography (HPLC) is relatively expensive and time consuming, and that genetic studies often employ hundreds to thousands of samples for analysis, it is critical to develop a method that is high-throughput. To achieve this goal, a total of 57 genetically diverse blueberry genotypes were processed using the skin-method and scanned using a Perten DA 7250 near-infrared (NIR) analyzer. The resulting spectra were combined with corresponding reference analysis data (HPLC) to produce a calibration curve. A comparison of observed and predicted values in the calibration dataset revealed that the model accounted for 81% of observed variation in TAC (R-2 = 0.8073). This result demonstrated that NIR can be applied as a high-throughput method to measure TAC in blueberry.