Towards sustainability in cold chains: Development of a quality, energy and environmental assessment tool (QEEAT)

Quantification of the impact of refrigeration technologies in terms of the quality of refrigerated food, energy usage, and environmental impact is essential to assess cold chain sustainability. In this paper, we present a software tool QEEAT (Quality, Energy and Environmental Assessment Tool) for evaluating refrigeration technologies. As a starting point, a reference product was chosen for the different main food categories in the European cold chain. Software code to predict the products temperature, based on validated heat and mass transfer models, were written in Matlab (The Mathworks Inc., Natick, USA). Also, based on validated kinetic models for the different quality indicators of the reference products, (including fruit, meat, fish, vegetables and dairy products) a software code was written to calculate the quality and safety evolutions of the food product, using the predicted product temperature as input. Finally, software code to calculate the energy usage and Total Equivalent Warming Impact (TEWI) value of different refrigeration technologies was also written in Matlab. All three software codes were integrated, and a graphical user interface was developed. Using the QEEAT, a user can tailor a cold chain scenario by adding cold chain blocks (different steps of a cold chain) and simulating the quality evolution, energy use and emission throughout the chain. Also, the user can modify properties of a cold chain block, by selecting different technologies, or changing set point values. Defaults are provided for input values, and are based on the current practice, and obtained by extensive literature studies and consultation with different experts of the cold chain. Furthermore, the user can build and simulate several chains simultaneously, allowing him/her to compare different chains with respect to quality, energy and emission

Extracting information on the evolution of living- and dead-cell fractions of Salmonella Typhimurium colonies in gelatin gels based on microscopic images and plate-count data

Aims: The aim of this study was to extract information on cell number and colony volume dynamics of Salmonella Typhimurium colonies. Methods and Results: Both cell number and colony volume of Salmonella Typhimurium in gelatin were monitored during the exponential and the stationary phase with varying pH and water activity, by plate counts and microscopic image analysis respectively. The exponential growth rates of cell numbers and colony volumes were correlated. The exponential growth rate of cell numbers was estimated based on this correlation and a secondary model that describes the effect of pH and water activity on the growth rate of the colony volumes. During the stationary phase, the cell number was constant, while colony volume increased, thus indicating the formation of a dead fraction. Models were developed to describe the living and dead population. Conclusions: By comparing colony volumes and cell numbers, the formation of dead fraction can be noticed from the beginning of the stationary phase, which indicates that the stationary phase is a dynamic - including both cell death and cell growth - rather than a static phase. Significance and Impact of the Study: This study was the first to investigate the proportion of living and dead bacteria within a stationary colony quantitatively

Modeling of ethylene biosynthesis during ripening and CA storage of 'Jonagold' apples

Simulation models can make the determination of optimal storage conditions for new cultivars of Malus domestica less expensive and less time consuming. On top of this, modeling creates the opportunity to apply batch dependent conditions. Two experiments were set up to develop a model to describe ethylene production during the ripening and storage of 'Jonagold' apples. Fruit were harvested at different dates around the optimal harvest date to obtain different ethylene production rates at different ripeness stages. Apples harvested at the three most extreme harvest dates were used for the storage experiment (CA conditions, 1% O-2, 3% CO2) and either treated or not treated with Smartfresh (TM), a commercially applied ethylene inhibitor. The ethylene production rate was determined by gas chromatography and S-adenosyl-L-methionine (SAM) concentrations were measured with capillary electrophoresis. During fruit ripening and during the storage of untreated fruit, an increase in ethylene production rate was noticed. SAM concentrations on the other hand were not influenced significantly during the 4 weeks period around optimal harvest time