Volatiles influencing sensory attributes and Bayesian modeling of the soluble solids-sweetness relationship in strawberry

Authors: FAN, ZHEN - University Of Florida; Plotto, Anne; Bai, Jinhe; WHITAKER, VANCE - University Of Florida

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/1/2021
Publication Date: 3/17/2021
Citation: Fan, Z., Plotto, A., Bai, J., Whitaker, V. 2021. Volatiles influencing sensory attributes and Bayesian modeling of the soluble solids-sweetness relationship in strawberry. Frontiers in Plant Science. 12:640704. https://doi.org/10.3389/fpls.2021.640704.
DOI: https://doi.org/10.3389/fpls.2021.640704

Interpretive Summary: Sensory descriptive analysis is a useful step in a breeding program to evaluate fruit eating quality before undergoing large consumer studies. In this report spanning over 7 years, 213 strawberry samples representing 56 cultivars were evaluated by a trained panel, and analyzed for sugars, acids and volatile compounds. Volatiles influencing the perception of sweetness (positive attribute), sourness (negative attribute) and other descriptors were identified. A new Bayesian approach correlating sweetness with soluble sugars revealed that the highest correlation was towards the end of the season, making it the optimum period to evaluate strawberries for sweetness in a breeding program. Finally, this analysis revealed that sweetness, sourness and firmness were affected by the genetics more than by the environment. Overall, this study provided additional information for strawberry breeders to target their breeding program for creating sweeter strawberries.

Technical Abstract: Descriptive analysis via trained sensory panels have great power to facilitate flavor improvement in fresh fruits and vegetables. When paired with an understanding of fruit metabolomics, descriptive analysis can help uncover the chemical drivers of sensory attributes. In the present study, 213 strawberry samples representing 56 cultivars and advanced selections were sampled over 7 seasons and subjected to both sensory descriptive and chemical analyses. PCA and K-cluster analyses of sensory data highlighted three groups of strawberry samples, with one classified as superior with high sweetness and strawberry flavor, and low sourness and green flavor. Partial least square models revealed twenty sweetness-enhancing volatile organic compounds and two sweetness-reducing volatiles, many of which overlap with previous consumer sensory studies. Volatiles modulating green, sour, astringent, overripe, woody and strawberry flavors were also identified. The relationship between soluble solids content (SSC) and sweetness was modeled with Bayesian regression, generating probabilities for sweetness levels from varying levels of soluble solids. A hierarchical Bayesian model with month effects indicated that SSC is most correlated to sweetness toward the end of the fruiting season, making this the best period to make phenotypic selections for soluble solids. Comparing effects from genotypes, harvest months and their interactions on sensory attributes revealed that sweetness, sourness and firmness were largely controlled by genetics. These findings help formulate a paradigm for improvement of eating quality in which sensory analyses drive the targeting of chemicals important to consumer-desired attributes, which further drive the development of genetic tools for improvement of flavor.