Reducing food losses by intelligent food logistics

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.

[1]  J. Farquhar TIME‐TEMPERATURE INDICATORS IN MONITORING THE DISTRIBUTION OF FROZEN FOODS , 1977 .

[2]  P. Taoukis,et al.  Applicability of Time‐Temperature Indicators as Shelf Life Monitors of Food Products , 1989 .

[3]  P. S. Taoukis,et al.  The relationship between processing and shelf-life. , 1990 .

[4]  L. Tijskens,et al.  A generic model for keeping quality of vegetable produce during storage and distribution , 1996 .

[5]  K Koutsoumanis,et al.  Application of shelf life decision system (SLDS) to marine cultured fish quality. , 2002, International journal of food microbiology.

[6]  K. Alicke Strategisches Supply Chain Management , 2003 .

[7]  K Koutsoumanis,et al.  Development of a Safety Monitoring and Assurance System for chilled food products. , 2005, International journal of food microbiology.

[8]  Shoshanah Cohen,et al.  Strategic supply chain management : the five disciplines for top performance , 2005 .

[9]  F. P. Scheer,et al.  Optimising supply chain using traceability systems , 2005 .

[10]  O. Kooten,et al.  Profitability of 'ready-to-eat' strategies , 2006 .

[11]  Jan D. Gehrke,et al.  Transport scenario for the intelligent container , 2007 .

[12]  Frédéric Thiesse,et al.  Sensor Applications in the Supply Chain: The Example of Quality-Based Issuing of Perishables , 2008, IOT.

[13]  Walter Lang,et al.  The Benefits of Embedded Intelligence - Tasks and Applications for Ubiquitous Computing in Logistics , 2008, IOT.

[14]  Durk-Jouke van der Zee,et al.  Simulation modelling for food supply chain redesign; integrated decision making on product quality, sustainability and logistics , 2009 .

[15]  S. O. Tromp,et al.  Simulation modelling for food supply chain redesign , 2010 .

[16]  IMPROVED EFFICIENCY AND REAL TIME TEMPERATURE MONITORING IN THE FOOD SUPPLY CHAIN , 2010 .

[17]  J. Parfitt,et al.  Food waste within food supply chains: quantification and potential for change to 2050 , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[18]  Frédéric Thiesse,et al.  RFID in the Apparel Retail Industry: A Case Study from Galeria Kaufhof , 2011 .

[19]  U. Sonesson,et al.  Global food losses and food waste: extent, causes and prevention , 2011 .

[20]  Walter Lang,et al.  The “Intelligent Container”—A Cognitive Sensor Network for Transport Management , 2011, IEEE Sensors Journal.

[21]  Jean-Pierre Emond,et al.  Quality of Strawberries Shipped by Truck from California to Florida as Influenced by Postharvest Temperature Management Practices , 2011 .

[22]  O. Kooten,et al.  Towards a Diagnostic Instrument to Identify Improvement Opportunities for Quality Controlled Logistics in Agrifood Supply Chain Networks , 2011 .

[23]  Hajo Rijgersberg,et al.  Retail benefits of dynamic expiry dates—Simulating opportunity losses due to product loss, discount policy and out of stock , 2012 .

[24]  Selwyn Piramuthu,et al.  RFID in highly perishable food supply chains – Remaining shelf life to supplant expiry date? , 2013 .

[25]  Riikka Kaipia,et al.  Creating Sustainable Fresh Food Supply Chains through Waste Reduction , 2013 .

[26]  Michael Lütjen,et al.  Quality driven distribution of intelligent containers in cold chain logistics networks , 2013, Prod. Eng..

[27]  Brigitte Petersen,et al.  A predictive shelf life model as a tool for the improvement of quality management in pork and poultry chains , 2013 .

[28]  Junguo Liu,et al.  Food losses and waste in China and their implication for water and land. , 2013, Environmental science & technology.

[29]  Thomas Pötsch,et al.  Communication techniques and challenges for wireless food quality monitoring , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[30]  S Janssen,et al.  Two underestimated threats in food transportation: mould and acceleration , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[31]  Zetian Fu,et al.  C2SLDS: A WSN-based perishable food shelf-life prediction and LSFO strategy decision support system in cold chain logistics , 2014 .

[32]  Ismail Uysal,et al.  Improvement in fresh fruit and vegetable logistics quality: berry logistics field studies , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[33]  M. Geyer,et al.  Postharvest treatments of fresh produce , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[34]  Ismail Uysal,et al.  Shelf life modelling for first-expired-first-out warehouse management , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[35]  Walter Lang,et al.  Remote quality monitoring in the banana chain , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[36]  S Janssen,et al.  Ethylene detection in fruit supply chains , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[37]  M. Mack,et al.  Quality tracing in meat supply chains , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[38]  Lirong Zheng,et al.  Radio frequency identification enabled wireless sensing for intelligent food logistics , 2014, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[39]  Francesco Parola,et al.  Refrigerated container versus bulk: evidence from the banana cold chain , 2015 .