DEVELOPMENT OF ASSESSMENT TOOLS OF PACKAGE/PRODUCT SYSTEMS FOR A SUSTAINABLE FOOD CHAIN

Post-harvest life of fresh produce is limited due to high metabolic activity and microbial spoilage. Modified atmosphere packaging (MAP) has proven to be one of the most effective techniques to extend the shelf life of fresh produce commercially. Obtaining of an optimum concentration of oxygen and carbon dioxide inside the package depends upon the product properties, the environmental conditions of the cold chain, the permeable film, some of which are subjected to natural variability during the food distribution chain. This variability may generate produce that is out of specification that will lead to food waste. Uncertainty analysis of this problem may lead to relevant interventions to prevent these losses. The hypothesis of this work was to create a mathematical model that predicts key quality factors for MAP packaged fresh products in the supply chain distribution, which will help to assess the food losses in relation to quality thresholds. The model developed simulated the respiration rate as function of O2 and CO2 concentration and produce temperature using Michaelis-Menten equations. The exchange of gases (O2, CO2) and water vapour between the fruit surface, package atmosphere and external atmosphere was modelled taking into account the process of transpiration and condensation. In the transpiration model, the fresh produce surface was assumed to be perfectly saturated and the energy of respiration was used to evaporate surface water. Temperature changes in the headspace due to metabolic heat, convective heat transfer and heat exchange by gas transmission through the package were accounted for. The quality attributes of fresh produce included weight loss and colour change (L, a, and b values) for mushroom, from Botrytis and its fermentative activity for strawberry and weight loss and spoilage for tomato. ii These conditions were simulated for real and variable i) export cold chain and ii) retail display storage to evaluate the effect of cold chain variability (temperature and relative humidity) on the quality of fresh produce and associated waste generation. The prediction of propagation of biological variance on the quality of fresh produce during storage was obtained using a mathematical model. Sensitivity analysis of the stochastic MAP model pointed out the influence of input parameters on the quality of fresh produce. The conclusions of the study showed that the toolbox developed is able to interpret cold chain data: 1) mathematical prediction of quality; 2) simulation of cold chain conditions allowing for different variability components; 3) estimation of waste generation kinetics based in all quality criteria and thresholds; 4) sensitivity analysis to identify the most sensitive technological parameters; and 5) identification of interventions that affect the benchmarked technological parameters

Robotics in meat processing

Scientists are currently investigating micro-robotics in the medical field with a potential to provide better medical technology in the near future. When it comes to the food industry, the use of robots has been traditionally limited to picking and palletization. Today, however, robots are used in material handling and secondary or tertiary packing. Recent developments with faster computers and sophisticated sensors have made it possible to use robotics in the meat processing sectors, where their application has reduced processing costs, occupational injuries, improved efficiency and hygiene associated with meat products. Compared to other industries, the working environment in the meat industry is not very conducive to robotics due to the noisy, damp and cold conditions. Slaughtering animals and cutting meat into pieces and disposing waste is an intensive physically demanding task. This chapter reviews the application of robotics in the meat industry and the advancements that have been made until now

Predicting quality attributes of strawberry packed under modified atmosphere throughout the cold chain

Modified Atmosphere Packaging (MAP) is used commercially to extend the shelf life of strawberries. The attainment of desired gas (O2, CO2) concentrations inside MAP relies on the product respiration and the mass transfer through packaging and will affect the quality. The objective of this work is to build a mathematical model for strawberries to assess the effect of the uncertainties on headspace gas concentration and quality: 1) cold chain related temperature and relative humidity variations and 2) variability associated to product respiration and quality based on literature. Weight loss was more influenced by the cold chain storage conditions (temperature and RH) whereas spoilage had similar influence of cold chain conditions and product parameters. Waste generated in the cold chain was estimated from industrial standard weight loss and spoilage thresholds. A sensitivity analysis of the stochastic MAP model showed the influence of input parameters on the quality pointing to interventions associated to a reduction of the respiration rate (e.g. modification of packaging) and reduction of water transfer (e.g. coating) may prove more successful than other interventions to which the waste generation of this product is not so sensitive to. As a conclusion this work presents a toolbox to interpret cold chain data: 1) develop mathematical models to predict fate of quality 2) simulate cold chain conditions allowing for uncertainty 3) estimate the waste generation kinetics based in quality criteria and thresholds 4) perform a sensitivity analysis to identify most sensitive technological parameters 5) identify interventions that will affect those technological parameters. Keywords: Mathematical modelling; Coating; Variability; Sensitivity analysis; Strawberr

Impact of Cold Chain and Product Variability on Quality Attributes of Modified Atmosphere Picked Mushrooms (Agaricus bisporus) Throughout Distribution

An integrated mathematical modelling approach was followed to model the heat and mass transfer processes taking place in modified atmosphere packaged mushrooms and its effect on the quality throughout distribution supply chain was simulated. The model equations were solved to obtain the concentration of gases (O2, CO2) and H2O in the headspace of the package. The change in the quality (colour and weight loss) during the distribution supply chain were monitored. The simulation results are in agreement with the experimental data. The model can study the effect of biological parameters and cold chain parameters on the quality of mushroom. Weight loss is influenced by the cold chain parameters whereas product lightness (L) value is influenced by the product uncertainty parameters. Sensitivity analysis was performed to quantify the effect of individual parameters on the quality of mushroom. Using this integrated model the changes in the quality of MAP mushroom during the supply chain can be predicted and the losses can be assessed at each step