The evaluation of a MAP system for any given commodity should be accomplished by a systematic and comprehensive approach by first establishing an initial predictive theoretical model to represent and manipulate underlying principles, followed by validation through empirical study. Empirical study unsupported by theoretical models and performed through trial and error is a lengthy and expensive process that does not take into consideration microbial ecology, product safety, or interaction of underlying MAP system variables; shelf life and associated product quality are the primary factors examined. Mathematical models of interactions among variables that affect MAP package atmospheres have been proposed and used to design MAP systems. However, improvements are needed to create more comprehensive models, and little work has been done to create models for different MP fruits and vegetables.
Additionally, models are needed for more sophisticated MAP systems such as multiple pack nested or multiple commodity packaging, multiple barrier systems, or where the behavior of specific MAP system variables may be expected to be different, as with perforated films , films incorporating gas scavengers or generators, or systems utilizing superatmospheric O2 or novel gas mixtures. Models should generally consider the packaging internal and external environments, the product under storage, and the storage gases and packaging materials employed. Temperature, product respiration rate (both consumption of O2 and production of CO2), product weight, package headspace, film permeability to gases and water vapor, film surface area and thickness, product diffusion resistance, and product tolerance to low O2 and high CO2 are all important variables to consider. Typically, a film with specific gas and water vapor characteristics is selected to achieve the particular target EMA for a stored product having a specific respiration rate, at a specific temperature. Predictive models should be validated by empirical studies incorporating the particular produce commodity and testing one or more variables to optimize.
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