Innovative measurements and models for predicting shelf life of fresh foods during postharvest

Abstract

Using non-destructive sensors, quality changes of individual products can be monitored over time. This elucidates the biological variability within a batch. Repeated measures require proper statistical analysis as standard techniques from literature used with destructive sensors are no longer applicable. Therefore, the concept of mixed models is introduced. The mixed model concept allows quantitative analysis of different sources of variability, even as a function of storage time. An alternative model approach was developed by combining kinetic models with the concept of biological age as a random variable. The quantification of biological variability is of great importance but was never accounted for before. Data obtained with non-destructive sensors can be used to calibrate mathematical models that describe quality change under variable storage and handling conditions. These models allow shelf life prediction based on the preferences of different consumer groups which will be the basis for future supply chain management.