Reducing food losses by intelligent food logistics

Abstract

The need to feed an ever-increasing world population makes it obligatory to reduce the millions of tons of avoidable perishable waste along the food supply chain. A considerable share of these losses is caused by non-optimal cold chain processes and management. This Theme Issue focuses on technologies, models and applications to monitor changes in the product shelf life, defined as the time remaining until the quality of a food product drops below an acceptance limit, and to plan successive chain processes and logistics accordingly to uncover and prevent invisible or latent losses in product quality, especially following the first-expired-first-out strategy for optimized matching between the remaining shelf life and the expected transport duration. This introductory article summarizes the key findings of this Theme Issue, which brings together research study results from around the world to promote intelligent food logistics. The articles include three case studies on the cold chain for berries, bananas and meat and an overview of different post-harvest treatments. Further contributions focus on the required technical solutions, such as the wireless sensor and communication system for remote quality supervision, gas sensors to detect ethylene as an indicator of unwanted ripening and volatile components to indicate mould infections. The final section of this introduction discusses how improvements in food quality can be targeted by strategic changes in the food chain.

Development of a Bioactive Food Additive for Controlling of Fungal Growth

“…On the other hand, according to FAO et al, (2014), roughly one-third of global food produced for human consumption is lost or wasted, because their quality has dropped below an acceptance limit, which amounted in 2011 about 1.3 billion tons (Jedermann et al, 2014). Considering the increasing trend of the world population growth, this is unacceptable.…”

Communication techniques and challenges for wireless food quality monitoring

“…In our first test, the implementation of this technique has already revealed a suppression of data transmission of up to 90%. (2) Simple shelf life models evaluate the effect of temperature deviations according to the Arrhenius Law for reaction kinetics or by a simple exponential relation to temperature [2]. to host such algorithms.…”

“…An important share of these losses is caused by improper handling and temperature mismanagement along the cold chain [2]. This share can be reduced if the information about the state of the transported product is provided without time delay after the appearance of a problem.…”

Options for reducing food waste by quality-controlled logistics using intelligent packaging along the supply chain

“…But also a lot of food is thrown away when a sample from a batch does not fulfil the specified quality levels, leading to destruction of a whole batch. These food losses can be reduced by monitoring the quality of foods throughout the whole supply chain and using this information efficiently (Verghese et al 2013;Jedermann et al 2014). Especially in medium-and high-income countries is food wasted primarily at the retailer and consumer levels (Gustavsson et al 2011).…”

“…An intelligent package can contain sensors and/or indicators that monitor quality indicator compounds of the product or quality indicator environmental conditions (Heising et al 2014b). The information from these sensors needs to be translated into a meaningful quality indication that is communicated to some or all the actors of the chain (Jedermann et al 2014). For this purpose, the measured signals can be converted by mathematical models into values indicating the quality condition of the food product.…”