Volatiles Influencing Sensory Attributes and Bayesian Modeling of the Soluble Solids–Sweetness Relationship in Strawberry

Abstract

Descriptive analysis via trained sensory panels has great power to facilitate flavor improvement in fresh fruits and vegetables. When paired with an understanding of fruit volatile organic compounds, 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 seven seasons and subjected to both sensory descriptive and chemical analyses. Principal component analysis 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 20 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.