Tissue-dependent seasonal variation and predictive models of strawberry firmness

Firmness is an important quality factor in strawberries. We investigated the tissue-dependent seasonal variation in strawberry firmness in epidermis, cortex, and pith tissues, and also developed statistical predictive models for the seasonal changes in firmness using environmental conditions and fruit properties. The experiment was conducted at three locations (sites A, B, and C) and in different harvesting seasons from winter (December) to spring (May) during 2 years. The fruit properties, including the total soluble solids (TSS), acidity, and fruit surface color decreased toward the end of the season (from winter to spring), with fruit harvested in April and May having the lowest values at all research sites. Similarly, the epidermis firmness decreased 0.73-fold toward the end of the season at all the research sites. The cortex firmness of site A showed a marked decrease of 0.64-fold toward the end of the season, but that of sites B and C remained at constant levels. The pith firmness tended to be higher for fruit harvested in December than in other months. We tested the training dataset using the stepwise multiple linear regression analysis to construct the statistical predictive models of firmness. The goodness-of-fit of the firmness predictive models, shown by the adjusted square correlation coefficient, was 0.47–0.54 in the model using the input data for daily mean environmental conditions several days before harvest as well as fruit properties, including fruit weight, color, and TSS. Additionally, in the regression analysis between predicted and actual values, the predictive models demonstrated accurate high performance with a low predictive error (0.06 as relative root mean square error). Thus, we concluded that strawberry firmness shows seasonal variation within the epidermis and pith tissues, and that their predictive models were of adequate accuracy and usefulness without the need for time-consuming, costly measurement equipment.