Exploring shelf-life predictability of appearance traits and fruit texture in blueberry

Improving the shelf-life of blueberries (Vaccinium spp.) has become a crucial breeding priority for the industry. However, the breeders have sparse empirical data to select genotypes with extended shelf-life. In this study, a large set of cultivars was evaluated for mechanical texture and appearance characteristics at harvest and after storage to understand their relationship and test multiple statistical models to assess the predictability of shelf -life. Blueberries harvested from 61 cultivars with extensive phenotypic variation were stored at 2 oC and 80% relative humidity (RH) for six weeks. The results indicated that weight loss, texture change, and fruit wrinkling could be predicted using fruit characteristics measured at harvest (T0) or two weeks post-harvest (T2). The berry size at T0 was able to predict postharvest weight loss with high accuracy; the larger the initial berry size, the less weight loss. This trend plateaued with berries larger than 18 mm in diameter. For texture, the measurements at T0 and six weeks after storage (T6) were positively correlated in all mechanical texture parameters, indicating that the initial texture is highly related to the final texture after storage. The overall change of texture could be best predicted using the texture parameter 'distance to maximum force' (DFM) measured at T0. Although the prediction accuracy was relatively low (R2 = 0.34), the model still effectively predicted the cultivars with the most texture change and those with the least. Interestingly, the prediction power improved to a moderate level (R2 = 0.45-0.66) when using all the texture and appearance parameters measured at T0 and T2. Wrinkling was best predicted by either the initial fruit size or the texture parameter 'force linear distance' (FLD) with low accuracy (R2 = 0.35-0.37); the larger the berry or FLD at T0, the less wrinkle after storage. These findings provide empirical data that blueberry breeders could use to select for shelf-life in blueberry. Predicting the variation of shelf-life indicators in a germplasm can substantially reduce the cost and time required to phenotype shelf-life performance.

Automated summary

This study evaluated a large set of blueberry cultivars to understand the relationship between mechanical texture and appearance characteristics at harvest and after storage, and tested multiple statistical models to predict shelf-life. The results showed that fruit characteristics measured at harvest or two weeks post-harvest can predict weight loss, texture change, and fruit wrinkling. These findings provide empirical data that blueberry breeders can use to select cultivars with extended shelf-life.