A linear regression freezing point prediction model for peach (Prunus persica) fruits
Peaches and nectarines in unsuitable cold temperature can suffer cold injury, but cold temperature storage can maintain better fruit quality than storage at higher temperatures. The aim of this study was to develop an optimal model to calculate fruit freezing points that have a direct relationship to cold temperature storage. Four types of peach fruit (nectarine, flat peach, juicy peach and yellow peach) were put into a cryostat with thermocouples at two opposite points on the fruit's equator to estimate the critical temperatures (freezing points) at which fruit can be held with the least danger of either freezing or internal breakdown. Freezing points of the peach cultivars were between -0.1 and -0.9°C except 'Nectaross' (-1.8°C, nectarine) which had a highly acidic flavor. Different freezing points existed in different types of peaches. The factors that affected freezing point were analyzed, including soluble solids content (SSC), individual fruit weight, fruit volume and density. Both gray relational grade analysis and correlation analysis showed that SSC was very closely related with freezing point. Three outlier samples were eliminated after analyzing studentized residuals, Cook distance and leverage methods. It was concluded that the linear regression equation (freezing point = -0.0996 × SSC + 0.6765) is an accurate and reliable regression model for calculating the freezing point of peach fruits.
Zhang, B.B., Ma, R.J., Cai, Z.X., Yan, Z.M. and Yu, M.L. (2021). A linear regression freezing point prediction model for peach (Prunus persica) fruits. Acta Hortic. 1304, 299-308
fruit, storage, influencing factor, quality character, gray relational grade analysis, correlation analysis