Fruit Quality Measures from a Historical Trial of University of Florida Strawberry Cultivars

Abstract

The strawberry breeding program of the University of Florida develops varieties that are highly adapted to west-central Florida, where approximately 8,800 acres of strawberries were grown during the 2009–10 season. In order to gain insight into breeding progress over time, two advanced selections and ten released cultivars, from Florida Belle (1975) to Florida Radiance (2008), were compared for various fruit quality traits. Cultivars and selections were arranged in a randomized complete block design at each of two locations (Balm, FL and Dover, FL). Fruit size varied dramatically with a minimum average weight of 16.2 g for ‘Dover’ (1979) and a maximum of 30.8 g for ‘Elyana’ (2008). Regression of average fruit weight on year of release showed an average gain in fruit size of 0.26 g/yr (R² = 0.44). Improvements in fruit uniformity were also apparent, but gains for internal flesh color were not sustained. There was wide seasonal variability in sugars (sucrose, glucose, fructose, Brix) and acids (citric, malic, and titratable acidity); March tended to produce fruit with lower sugars. Sugars were highest in February while acids were highest in January. Also, correlations were found between sugars and volatiles having fruity and strawberry characters. Comparison of chemical data with sensory data from related studies indicates that instrumental measures, particularly for SSC/TA, could aid in breeding for improved flavor.

Keywords:

• Fragaria
• fruit color
• organic acids
• soluble solids content
• sugars
• titratable acidity

INTRODUCTION

The University of Florida strawberry (Fragaria × ananassa) breeding program develops cultivars primarily for west-central Florida. This region is the second leading production area in the United States behind coastal California, with a harvested area of ∼9,000 acres and a crop valued at $362 million during the 2009–10 season (www.nass.gov). A continuous breeding effort has existed since 1968. The University of Florida has released 10 cultivars during this period. Dr. Charles Howard released ‘Florida Belle’ (1975) and ‘Dover’ (1979). Dr. Craig K. Chandler released ‘Sweet Charlie’ (1992), ‘Rosa Linda’ (1996), ‘Earlibrite’ (2000), ‘Strawberry Festival’ (2000), ‘Carmine’ (2002), ‘Winter Dawn’ (2005), ‘Florida Radiance’ (2008), and ‘Florida Elyana’ (2008). In 2009–10, the most-utilized cultivars were Strawberry Festival and Florida Radiance, which occupied approximately 60% and 10% of planted acreage, respectively.

The breeding method of phenotypic recurrent selection has been employed over time to improve several important fruit quality traits. New cultivar releases have been compared to one or more established cultivars in order to document improvements in yield, fruit size, and fruit disease resistance (Chandler et al., 2009a, 2009b). Other traits influencing flavor perception, such as soluble solids content (SSC), titratable acidity (TA), individual sugars and acids, and volatile compounds, have been selected only indirectly by tasting fruit in the field. A recent evaluation of a limited number of Florida varieties and advanced selections revealed variation in eating quality through sensory evaluation as well as variation in SSC, TA, and volatile components (Jouquand et al., 2008).

Retrospective analyses can help to document breeding progress over time. Shaw and Larson (2008) compared two sets of cultivars released from the University of California-Davis strawberry breeding program from 1945–66 and from 1993–2004, showing significant progress for fruit size, yield, and appearance. Approximate selection responses for the different traits ranged from 1% to 3% per year. The purpose of this study was to evaluate relevant fruit quality attributes of University of Florida strawberry cultivars and two advanced selections. Examination of fruit quality traits and their temporal trends should help inform future strategies for genetic improvement.

MATERIALS AND METHODS

The ten cultivars released since 1968 and UF1 and UF2, two selections being considered for release at the time of this study, were evaluated during the 2009–10 season. Two other selections, UF3 and UF4, were included for chemical trait evaluations only. The experiment was conducted at Dover, FL and at the Gulf Coast Research and Education Center (GCREC) in Balm, FL, which are the past and present locations of the breeding operations, respectively. Four replicate plots (ten plants/plot) of each genotype were planted in a randomized complete block design at each location on 15 Oct. The plots were established and maintained according to typical plasticulture and drip fertigation protocols for central Florida.

Fruit Quality Traits—Physical

Fully-ripe fruit were harvested twice weekly from the beginning of Dec. 2009 to the end of Mar. 2010 and graded in the lab at the GCREC. Fruit that were commercially unmarketable due to irregular shape, small size (<10 g), disease, and/or surface blemishes were recorded as culls and removed, and further analyses were performed on marketable fruit only. Fruit size was calculated as the total weight in grams divided by the total number of marketable fruit for the entire season. On 10 Mar. 2010, internal color was evaluated using a handheld colorimeter (Chroma Meter CR-400, Minolta, Ramsey, NJ, USA) with a 1-cm aperture using the variable a* (green to red vector) according to Shaw (1991). Fruit uniformity was measured on 10 Mar. using a subjective 1–5 scale with half point increments (1 = non-uniform surface within and among berries; 5 = uniform surface and shape within and among berries) using five berries per replication.

Fruit Quality Traits—Chemical

Fruit from one harvest in January, February, and March were transported on the day of harvest to the USDA-ARS Citrus and Subtropical Products Laboratory in Winter Haven, FL for chemical analyses. A set of fruit was homogenized using a Waring blender (Waring Products Div., Dynamics Corp. of America, New Hartford, CO, USA) and frozen at −20°C for further analyses of SSC, TA, sugars, and acids. The supernatant of thawed homogenates, centrifuged at 12,100× g for 15 min, was analyzed for TA and SSC. For TA, 6 mL of the supernatant was diluted with 50 mL of DI-water and titrated with 0.1 N NaOH to a pH 8.1 endpoint using a Metrohm 808 Titrando and Metrohm 730 sample changer (Metrohm USA Inc., Westbury, NY, USA). Soluble solids content was determined with a digital ATAGO PR-101 refractometer, 0–45% Brix range (Atago Co, Tokyo, Japan). The HPLC quantification of individual sugars and acids were carried out using the same homogenates according to the methods of Baldwin et al. (2008, 2010). Another set of fruit were cut into pieces and 30 g of pieces were homogenized with 30 g of saturated sodium chloride (NaCl) for 20 s. Internal standard, 1 ppm of 3-hexanone, was added and 5 mL of the mixture was placed in 20-mL vials. Sample headspace was analyzed using a 2-cm SPME fiber (50/30 μm DVB/Carboxen/PDMS; Supelco, Bellefonte, PA, USA) exposed for 30 min at 40°C. Volatiles were analyzed by gas chromatography and mass spectrometry as described in Jouquand et al. (2008).

Statistical Analysis

Analyses of variance (ANOVA) were performed using general linear models with Genotype, Location, Block (nested within locations), and the Genotype × Location interaction as fixed effects. When the Genotype × Location and/or Genotype × Month interaction were significant (F-test, α = 0.05), the variables were analyzed separately by location and/or month; otherwise, the interaction was dropped from the model and the data was analyzed for Balm and Dover collectively. Using first order models, each characteristic was regressed on the year of release of the cultivars and prospective year of release for selections. The slope associated with this variable was considered an estimate of the long term annual change in each trait. The models also included a dichotomous variable to account for the variability introduced by the two locations in the study (0 = Balm, 1 = Dover). A principal component analysis (PCA) was performed using XLSTAT (Addinsoft, Paris, France) to determine a relationship among the variables (SSC, TA, SSC/TA, pH, individual sugars and acids) and the genotypes. Pearson correlations were performed among volatile and non-volatile compounds.

RESULTS AND DISCUSSION

Fruit Quality Traits—Physical

Fruit size varied dramatically from 30.8 g for ‘Elyana’ to 16.2 g for ‘Dover’ at Balm and from 28.3 g for UF2 to 16.6 g for ‘Dover’ at Dover (Fig. 1). A linear regression of fruit size on year-of-release revealed an average gain of 0.26 g per year since 1975 for the cultivars and selections tested (R ² = 0.44). The proportion of cull fruit exhibited a noticeable decrease over years, and there was a corresponding gradual increase in fruit uniformity. However, coefficients of determination for regressions on year-of-release for culls (R ² = 0.30) and uniformity (R ² = 0.25) were smaller. At Balm, ‘Florida Radiance’ had the lowest cull rate (12.3%) while ‘Dover’ had the highest (52.5%). Selection UF1 had the highest fruit uniformity score (3.7) while ‘Dover’ had the lowest (1.8) across both locations. Large fruit size and low cull rate are certainly important traits for industry acceptance, and they are easily selected in the course of breeding via rapid visual observations in the field. Therefore, improvements in these traits over time are expected and are consistent with our results.

FIGURE 1 Average fruit weight in grams of fruit of University of Florida strawberry cultivars and advanced selections from a field trial at two locations during the 2009–10 season. Genotypes are ordered according to release date. A regression of average weight on release date indicates an average yearly increase of 0.26 grams since 1975 (R ² = 0.44).

We assume that increased internal flesh color is desirable to consumers, and it has been shown that color in many foods can even affect consumers' perceptions of flavor (Jaros and Rohm, 2001). Two early releases, ‘Florida Belle’ (1975) and ‘Dover’ (1979) had a* values of 16.1 and 18.5, respectively. The cultivars—Festival (2000), Earlibrite (2000), and Carmine (2002)—had higher a* values of 27.0, 28.4, and 34.7, respectively, indicating more intense color. However, these increases were not sustained in later cultivars and selections (Fig. 2). Selection for internal color in the breeding program has become secondary to other important traits, such as yield and firmness, which could explain part of this trend.

FIGURE 2 Internal redness (colorimeter a* values) of fruit of University of Florida strawberry cultivars and advanced selections from a field trial during the 2009–10 season, arranged in order of release date. Bars represent standard errors.

Fruit Quality Traits—Chemical

Though there were significant differences among genotypes for all chemical traits measured, there was no clear association with any of these traits and year-of-release (R ² ≤ 0.09). Values of SSC in almost all cultivar × location combinations were highest in February and lowest in March, which may be partly due to increasing temperatures in March. This is consistent with late-season decreases in soluble solids seen in previous studies (Jouquand et al., 2008). The SSC/TA ratio ranged widely among genotypes, from a high of 15.6 for ‘Florida Belle’ in February at Dover to a low of 6.4 for ‘Winter Dawn’ in January at Balm. The cultivars Elyana, Florida Belle, and selection UF1 had the highest ratios across locations and months whereas cultivars Dover and Winter Dawn had the lowest ratios.

Interestingly, a separate sensory study using fruit from the historical trial showed that cultivars Elyana and Florida Belle as well as UF1 had consistently higher “sweetness” ratings than the cultivars Dover and Winter Dawn (Plotto et al., 2010). A 2006–07 study with University of Florida strawberry cultivars and advanced selections showed that SSC/TA was a strong indicator of sensory panel ratings, with high ratios associated with increased perception of sweetness and liking (Jouquand et al., 2008). This is consistent with previous studies using cultivars from other breeding programs (Pelayo-Zaldivar et al., 2005; Wozniak et al., 1997). Since we have shown significant genetic variation in UF germplasm for SSC, TA, and SSC/TA and since these have been associated with taste preference, it should be possible to breed for improved flavor by targeting optimal values of these traits in the breeding program.

A principal component analysis was conducted using the chemical traits for each cultivar/selection (Fig. 3). PC1 explained 45.2% of the variation, with SSC, glucose, fructose, SSC/TA, and pH having high positive loadings on that component. PC2 explained an additional 27.7% of the variation, with TA, citric, and malic acids. Figure 3 clearly shows that sugars, SSC, SSC/TA, and pH were not correlated with acids and TA. Fruit harvested in March (circles) generally had the lowest sugar content whereas fruit in February (triangles) had the highest sugar content. On the other hand, fruit harvested in January (diamonds) had the highest acid content. Glucose and fructose contents were highly correlated with one another (r = 0.92, P < 0.0001) and were also highly correlated with SSC (r = 0.93, P < 0.0001 and r = 0.81, P < 0.0001, respectively) (Table 1). The SSC/TA ratio is also positively associated with pH (r = 0.90, P < 0.0001). Certain groups of volatile compounds, such as esters, furans, and ketones, tended to be positively correlated with sugars whereas others, such as the volatile alcohols, were negatively correlated with malic acid, aldehydes, and esters (Table 1). Alcohols are intermediary volatiles in the lipoxygenase pathway (Sanz et al., 1997), and the level of enzymatic activity for alcohol dehydrogenase or alcohol acyltransferase would result in more/less alcohols and less/more aldehydes or esters, explaining the latter negative correlations. Furthermore, esters were highly and significantly correlated with SSC (r = 0.50, P < 0.0001). This apparent association between the ability of the fruit to produce sugars and the ability of the fruit to produce certain volatiles is worthy of further investigation, particularly because esters, furans, and ketones contribute to positive fruity notes in strawberries.

TABLE 1 Pearson Correlation Coefficients for Pairs of Fruit Chemical Traits from a Trial of University of Florida Strawberry Cultivars and Advanced Selections

Variables	SSC/TA	SSC	pH	TA	Gluc.	Fruct.	Citric	Malic	Volatile acids	Alcoh.	Aldeh.	Esters	Arom.	Furans	Ketones	Terpenes	Sulfurs
SSC/TA	1.00 ^z	0.64	0.90	−0.56	0.76	0.68	−0.31	−0.35	0.23	0.00	0.05	0.29	0.11	0.37	0.28	0.06	0.26
SSC		1.00	0.52	0.26	0.93	0.81	0.35	0.01	0.30	−0.18	0.11	0.50	0.35	0.28	0.39	0.20	0.23
pH			1.00	−0.56	0.59	0.50	−0.29	−0.39	0.34	0.07	0.05	0.18	0.11	0.43	0.40	0.18	0.19
TA				1.00	0.04	0.02	0.74	0.45	0.04	−0.23	0.10	0.15	0.24	−0.15	0.10	0.16	−0.05
Glucose					1.00	0.92	0.15	−0.01	0.21	−0.17	0.11	0.46	0.29	0.31	0.34	0.16	0.27
Fructose						1.00	0.13	−0.01	0.23	−0.19	0.14	0.39	0.20	0.29	0.34	0.14	0.29
Citric							1.00	0.25	0.17	−0.10	−0.02	0.09	0.15	0.00	0.20	0.23	0.08
Malic								1.00	−0.16	−0.34	0.06	0.28	−0.03	−0.16	0.03	0.11	−0.09
Volatile acids									1.00	0.08	0.07	0.10	−0.06	0.31	0.20	0.35	0.20
Alcohols										1.00	−0.58	−0.33	−0.10	0.19	−0.10	−0.05	0.00
Aldehydes											1.00	0.29	0.07	−0.10	0.18	0.13	−0.19
Esters												1.00	0.22	0.39	0.40	0.45	0.09
Aromatics													1.00	0.28	0.16	0.12	0.04
Furans														1.00	0.44	0.65	0.18
Ketones															1.00	0.47	0.17
Terpenes																1.00	0.25
Sulfurs																	1.00
^zValues in bold are different from 0 with a significance level ɑ = 0.05.

CONCLUSIONS

Genotype explained a significant portion of variation for all traits measured in this study. Our evaluations of easily-scored, high-priority traits, such as fruit size and uniformity, revealed noticeable breeding progress over time. These remain very important traits for industry acceptance, and selection pressure for these traits must be maintained. Since gains in internal color were not sustained, attention to this trait should be renewed. For chemical traits that affect flavor, no obvious patterns were discernable over time. These results may be reflective of the fact that selection has been indirect through tasting a limited portion of the breeding population. Comparison of our results with other studies indicates that direct instrumental measures of chemical constituents, such as SSC/TA, can help guide selection for better flavor. The apparent association between sugar content and some classes of volatile compounds is worthy of more extensive evaluation in the future.

ACKNOWLEDGMENT

The authors wish to acknowledge Catalina Moyer, Jim Sumler, and Yanina Peres for assistance with data collection.