Strawberry sweetness and consumer preference are enhanced by specific volatile compounds

Abstract

Breeding crops for improved flavor is challenging due to the high cost of sensory evaluation and the difficulty of connecting sensory experience to chemical composition. The main goal of this study was to identify the chemical drivers of sweetness and consumer liking for fresh strawberries (Fragaria × ananassa). Fruit of 148 strawberry samples from cultivars and breeding selections were grown and harvested over seven years and were subjected to both sensory and chemical analyses. Each panel consisted of at least 100 consumers, resulting in more than 15,000 sensory data points per descriptor. Three sugars, two acids and 113 volatile compounds were quantified. Consumer liking was highly associated with sweetness intensity, texture liking, and flavor intensity, but not sourness intensity. Partial least square analyses revealed 20 volatile compounds that increased sweetness perception independently of sugars; 18 volatiles that increased liking independently of sugars; and 15 volatile compounds that had positive effects on both. Machine learning-based predictive models including sugars, acids, and volatiles explained at least 25% more variation in sweetness and liking than models accounting for sugars and acids only. Volatile compounds such as γ-dodecalactone; 5-hepten-2-one, 6-methyl; and multiple medium-chain fatty acid esters may serve as targets for breeding or quality control attributes for strawberry products. A genetic association study identified two loci controlling ester production, both on linkage group 6 A. Co-segregating makers in these regions can be used for increasing multiple esters simultaneously. This study demonstrates a paradigm for improvement of fruit sweetness and flavor in which consumers drive the identification of the most important chemical targets, which in turn drives the discovery of genetic targets for marker-assisted breeding.

Fig. 1. Cluster dendrogram of sensory attributes, volatiles, sugars and acids.

AU (approximately unbiased) p-values are in red and BP (bootstrap probability) p-values are in green

Fig. 2. Liking, sweetness, total sugars, and total volatiles of three cultivars as influenced by five-day average soil temperature prior to harvest.

The trend line was modeled with linear regression. Fruits were harvested on 2/22/2016 (five-day average 17.4 °C), 3/7/2016 (18.8 °C), 2/7/2017 (18.4 °C), and 2/14/2017 (19.5 °C). Sizable differences among genotypes and among temperatures indicate strong genotype and temperature effects on desired sensory attributes and total sugars

Fig. 3. Pairwise correlations of consumer attributes and chemical compounds.

Dots represent significant correlations after false discovery rate (FDR) correction. The intensity of color and size of each dot is propotional to the absolute value of the correlation coeffcient (r). Three highly correlated regions of the plot are enlarged

Fig. 4. A sensory-chemical network for strawberry.

Significant correlations are connected by edges. Sizes of nodes are proportional to centrality scores. Centrality scores were calculated for each node to reveal importance of the volatile in the cluster. Volatiles are colored by chemical class

Fig. 5. Evaluation of models incorporating volatiles.

Histograms of performance of six machine learning algorithms using sugars, acids, and volatiles to predict sweetness intensity (a) and consumer liking (b) compared to a model with sugars and acids only (leftmost in both figures). Black lines represent the average R² of nested cross validation while gray lines show the average R² when using a test dataset. Error bars represent standard deviations based on 100 iterations

Fig. 6. Principal component analyses of genotypes.

a PCA) plot of genotypes using Axiom® IStraw35 38000 single nucleotide polymorphisms (SNPs). Genotypes from different origins are grouped by color. Percentage of total variation explained by each component is shown in parentheses. b PCA plot of genotypes using volatiles, sugars and acids

Fig. 7. Genome-wide association of esters in strawberry fruit.

a Manhattan plots for hexanoic acid, ethyl ester and butanoic acid ethyl ester. A shared QTL was observed on linkage group 6 A. b A single shared marker explained a large portion of variation in the corresponding ester abundance. Different letters indicate significant differences at p = 0.05