Characterizing the Frequency Distributions for Fruit Firmness of Sweet Cherry Cultivars

Abstract

<jats:p>Fruit firmness is among the most important characteristics for the quality of sweet cherries. However, little has been published on its underlying frequency distribution. This research was undertaken to examine the firmness distributions (n = 48) from six cultivars [Sandra Rose, Summit, Lapins, Skeena, Sumtare (Sweetheart™), and 13S2009 (Staccato™)], two field treatments [with or without gibberellic acid (GA)], two storage times (0 and 7 days), and two growing seasons (2013 and 2014). Fruit was sampled (n = 300) at optimal maturity and firmness was evaluated using the FirmTech2 Fruit Firmness Tester. Firmness distributions were examined using descriptive statistics: mean, median, standard deviation (<jats:sc>sd</jats:sc>), minimum, maximum, range, skewness, and excess kurtosis. Nonnormality was assessed using skewness and kurtosis test statistics. Exponential models were fitted to the ascending and descending portions of the distributions and the proportion of “too soft” (percentage &lt; 2.56 N·mm<jats:sup>−1</jats:sup>) and “too hard” (percentage &gt; 4.71 N·mm<jats:sup>−1</jats:sup>) fruit was determined. A relatively high proportion of distributions were nonnormal (16/24 to 18/24), either skewed, peaked, or both. While most skewed distributions were skewed negatively, with a higher proportion of softer fruit, the distributions for ‘Sandra Rose’ were skewed positively, with a higher proportion of firmer fruit. Principal component analysis (PCA) showed seasonal, cultivar, treatment, and storage effects among three subsets of cultivars with differing characteristic firmness. The softer early-harvest cultivars (Sandra Rose and Summit) had a higher proportion of “too soft” fruit. GA and storage treatments increased mean firmness and reduced the proportion of “too soft” fruit. The firmer late-harvest cultivars (Skeena, Sumtare, and 13S2009) had a small proportion of “too hard” fruit (0% to 19.3%). The work gained insight into the nature of the firmness distributions for sweet cherries and the type of statistics that are most appropriate for analyzing the data.</jats:p>

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