Towards flexible management of postharvest variation in fruit firmness of three apple cultivars

Abstract

Stochastic modeling provides a useful tool in managing biological variation in the postharvest chain.In the current study, the fruit-to-fruit variability in the postharvest firmness of apples was modeled.Apples from three cultivars (Jonagold, Braeburn, and Kanzi) were harvested at different levels of maturity, and stored at different temperatures and controlled atmosphere (CA) conditions. By using a kinetic model describing firmness breakdown as a function of time, temperature, controlled atmosphere conditions and endogenous ethylene concentration, the main stochastic variables were identified as the initial firmness and the rate constants for firmness breakdown and ethylene production. Treating these variables as random model parameters, the Monte Carlo method was used to simulate the propagation of the fruit-to-fruit variability in flesh firmness within a batch of apples during storage under different CA conditions and subsequent shelf life exposure. The model was validated using independent data sets from apples picked in a different season. The model developed in this study can be used to predict the probability of having apples of certain firmness after long term storage for different scenarios of temperatures and CA conditions.

Analysis of the spatiotemporal temperature fluctuations inside an apple cool store in response to energy use concerns

“…East et al (2013) similarly showed that temperatures during a 9 h on and 15 h off cycle remained within a range of ±1 °C with an approximate 40 % of electricity cost savings. It should be noted that such small fluctuations in produce temperature during apple storage has minimal impact in quality (Brown, 2011;East et al, 2013;Gwanpua et al, 2013;2014). Consequently, there is enough window for energy reduction in cold storage of apples, without negatively impacting on quality.…”

Water loss in table grapes: model development and validation under dynamic storage conditions

“…Hence, modelling techniques has been applied to describe changes in the quality attributes of many fresh products (Amodio et al, 2013(Amodio et al, , 2015Gwanpua et al, 2012Gwanpua et al, , 2013Hertog et al, 2004b, c;Schouten et al, 2007Schouten et al, , 2010. In some of the reported works, the transpiration process was related to changes in texture attributes (Smedt et al, 2002) and visible wrinkling of the fruit skin (Hertog, 2002).…”

Shelf life modelling for first-expired-first-out warehouse management

“…Over the past 20 years in post-harvest, where one deals with highly perishable products in relatively complex supply chains, an increasing awareness has been observed of the need for proper mechanistic models to support an integrated quality management [39][40][41]. While initially models focused on describing the specific product quality, increased emphasis has been placed on developing product quality models applicable to dynamic supply chain conditions [42][43][44][45]. The quality of food products is characterized by an inherent large amount of biological variance.…”