Value-Chain Wide Food Waste Management: A Systematic Literature Review

© 2019, Springer Nature Switzerland AG. The agriculture value chain, from farm to fork, has received enormous attention because of its key role in achieving United Nations Global Challenges Goals. Food waste occurs in many different forms and at all stages of the food value chain, it has become a worldwide issue that requires urgent actions. However, the management of food waste has been traditionally segmented and in an isolated manner. This paper reviews existing work that has been done on food waste management in literature by taking a holistic approach, in order to identify the causes of food waste, food waste prevention strategies, and elicit recommendations for future work. A five step systematic literature review has been adopted for a thorough examination of the existing research on the topic and new insights have been obtained. The findings suggest that the main sources of food waste include food overproduction and surplus, food waste caused by processing, logistical inconsistencies, and households. Main food waste prevention strategies have been revealed in this paper include policy solutions, packaging solutions, date-labelling solutions, logistics solutions, changing consumers’ behaviours, and reuse and redistribution solutions. Future research directions such as using value chain models to reduce food waste and forecasting food waste have been identified in this paper. This study makes a contribution to the extant literature in the field of food waste management by discovering main causes of food waste in the value chain and eliciting prevention strategies that can be used to reduce/eliminate relevant food waste

The Phase Space as a New Representation of the Dynamical Behaviour of Temperature and Enthalpy in a Reefer monitored with a Multidistributed Sensors Network

The study of temperature gradients in cold stores and containers is a critical issue in the food industry for the quality assurance of products during transport, as well as forminimizing losses. The objective of this work is to develop a new methodology of data analysis based on phase space graphs of temperature and enthalpy, collected by means of multidistributed, low cost and autonomous wireless sensors and loggers. A transoceanic refrigerated transport of lemons in a reefer container ship from Montevideo (Uruguay) to Cartagena (Spain) was monitored with a network of 39 semi-passive TurboTag RFID loggers and 13 i-button loggers. Transport included intermodal transit from transoceanic to short shipping vessels and a truck trip. Data analysis is carried out using qualitative phase diagrams computed on the basis of Takens?Ruelle reconstruction of attractors. Fruit stress is quantified in terms of the phase diagram area which characterizes the cyclic behaviour of temperature. Areas within the enthalpy phase diagram computed for the short sea shipping transport were 5 times higher than those computed for the long sea shipping, with coefficients of variation above 100% for both periods. This new methodology for data analysis highlights the significant heterogeneity of thermohygrometric conditions at different locations in the container

Sea transport of bananas in containers -Parameter identification for a temperature model

Abstract: Losses of product quality during the sea transport of bananas in containers are related to the emergence of hot spots. In order to analyze critical conditions, a spatial temperature profile was recorded ashore in a container loaded with banana pallets. The identification of a structured system model showed that it is possible to reduce the information on the measured temperature curves to a set of only two index values. These can be interpreted as factors for coupling to the air stream and for the amount of heat generated by biological processes per banana box. The width of gaps between pallets was identified as the major influence on the spatial temperature profile. Boxes from which the unwanted banana ripening heat cannot be channeled away by the cooling unit can be detected by the quotient of the index values. coupling factor for the corner of a box k M coupling factor for the center of a box k P gain factor for the generated heat norm() Euclidean length of a vector containing a time series P G (t) generated respiration heat P W (t) exponential respiration heat term Q 10 change of heat production at a temperature change of 10 K time constants (center of the box) u 1 (t) mixed input signal for corner model u s (t) temperature of the supply air x 1 (t), x 2 (t) outputs or states of the delay elements (corner model) x 3 (t), x 4 (t) outputs or states of the delay elements (center model) y * model output y' measured value y' average over time y E (t) corner temperature y M (t) temperature of the center of the box Jedermann, R