Food Loss Waste Research Articles

Food Waste Loss Trend Index (FWLTI), A New Tool to Enable Management Decisions

The main purpose of this research is to work out a new, innovative tool to describe trends of food waste and loss (FWL) in the food industry. The result can help evaluate efforts for lowering wastes. FWL prevention recently gained much attention and priority among governments and international organizations as a major means to achieve global food security and sustainability; this led to the very ambitious UN goal SDG 12.3, which aims to halve global food losses by 2030. It is an ethical obligation to achieve SDGs by 2030. It is not declared in law and is not required for profitability in the food industry. A new tool is proposed to evaluate efforts to halve food losses in this article, which one may be used in leadership easily and effectively. At first, FWL definitions were analyzed to get a clear picture of the content of FWL data sourced from FAOSTAT and EUROSTAT. These definitions were compared with others from different sources. It is concluded that we have so many different definitions for FWL because of the difference in the purpose of data collecting. The most justified definition – often newly created – is used in each case. FWL data received from FAOSTAT and EUROSTAT in the period of 2010 − 2018 were compared to find the correlation. It is inverse and very weak, so they are not comparable; they must be evaluated separately. The trend of the FWL data is increasing and is not significant on level 95% in EUROSTAT, and decreasing and important on level 95% in FAOSTAT, from 2010 to 2018. FWL data from EUROSTAT are in moderate correlation with GDP and HDI. A new index, Food Waste Loss Trend Index (FWLTI), was created to evaluate FWL in EU member states and analyzed them with cluster analyses and scatterplots. There was no connection among the EU28 member states with FWLTI, so every member state is an individual case; there are no standard rules for FLW generation.

Food Loss Waste in the United States Food Supply Chain: An Analysis of Its Functions, Oversight, and Recent Impacts from the Coronavirus Pandemic

Government oversight of the food supply chain consists of a complicated regulatory framework involving multiple executive branch agencies, congressional committees, and state governments. The agencies primarily involved with food safety issues are the Food and Drug Administration (FDA), the Food Safety and Inspection Service (FSIS), the US Department of Agriculture (USDA), the Environmental Protection Agency (EPA), and the National Marine Fisheries Service (NMFS). Although the above entities divide responsibility for different aspects of food safety and quality, according to a 2019 Government Accountability Office (GAO) report, the patchwork of statutes and regulations has led to “inconsistent oversight, ineffective coordination, and inefficient use of resources.”

Associations Between Peaks of Foodborne Infections and Food Recalls

Abstract Objectives Food delivery and supply chains represent complex dynamic systems susceptible to pathogen introduction. However, seasonal infections are rarely compared with the timing of food recalls. Gaps between peak infections and recalls illustrate inefficient food inspection and monitoring. We use seasonality features (peak timing and amplitude) to examine associations between peaks of pathogen infectiousness and food recalls. Methods Monthly counts of 3 foodborne infections (Salmonella, Listeria, and Shiga toxin-producing E.coli) were abstracted from the CDC's public database, FoodNet Fast, for 10 representative states (CA, CO, CT, GA, MD, MN, NM, NY, OR, TN) from 1996–2018. We applied Negative Binomial harmonic regression models with the δ-method to derive peak timing and amplitude estimates (with confidence intervals). Food recalls were extracted using the United States Department of Agriculture's (USDA) Food Safety and Inspection Service (FSIS) Recall Case Archive. We extracted recall dates and durations for each infection in each state. We compared lagged associations between infection peaks and recall dates using Spearman correlations. Results Seasonal peaks of Salmonella and Shiga Toxin-producing E.Coli (STEC) are tightly clustered (7.79 [7.71, 7.87], and 7.80 [7.65, 7.95], respectively) while Listeria falls later in August (8.35 [8.05, 8.65]). Peak timing estimates were positively synchronized between New York and Connecticut for Listeria (ρ = 0.780, P = 0.001), and between Georgia and Tennessee for STEC (ρ = 0.666, P = 0.009) indicating possible regional hotspots. From 1996–2018, inspection recalls tended to lag behind peaks of confirmed infections for all infections by 1–2 months, with most recalls occurring during August and September annually. Conclusions Measurements of infection seasonality coupled with food recall information shows lagged associations between population infectiousness and supply chain reporting. This suggests the need for improved food inspection scheduling and monitoring to minimize fiscal and food waste losses. Funding Sources The Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via 2017–17,072,100,002.

A focus on the state of the art of food waste/losses issue and suggestions for future researches

Today, the role of the supply chain (SC) strategies in Food-Waste/Losses (F-W/L) generation is neglected, because F-W/L are generally considered an unavoidable consequence of accidental events. Therefore, only operating plans are today put into practice, which aim at reducing the impact of product losses along the SC. This involves that structured analyses and strategic SC approaches are not available for the prevention/minimization of F-W/L. The paper aims at changing the perspective, and promoting the hypothesis that generation of F-W/L mainly depends on the SC strategies put into practice that are usually optimized on the basis of the market demand. The paper considers the F-W/L an intrinsic characteristic of the SC, which must be put in relation with the market demand and the shelf life of products, for given boundary conditions (legal constraints, politic decisions, climatic and micro macro-economic factors). Thus, the paper aims at reviewing the state of the art of the F-W/L issue, and proposing new research topics in the field of the prevention and minimization of F-W/L, by focusing on: (i) the forecasting of F-W/L, the implementation of adaptive-holistic approaches that model the non-stationary and holistic behaviour of the phenomenon; (ii) the determination of analytical conditions that entail the generation of F-W/L, in relation to the implementation of specific SC strategies; (iii) the study of mathematical models for the assessment of the shelf life, and the evaluation of innovative technologies, which enable the shelf life monitoring; (iv) the study of new shelf life-dependent demand models based on the consumer-utility theory; (v) the redesign of the logistic SC management models, by proposing new SC coordination models, planning and replenishment strategies, and pricing models, for the prevention/minimization of the predictable component of F-W/L; (vi) the design of new risk assessment plans for the management of the accidental component of F-W/L.

Measuring food waste in a dairy supply chain in Pakistan

A significant share of food grown is not ultimately eaten. According to the FAO, roughly one-third (by weight) of global food production is lost or wasted every year. Such level of inefficiency has serious sustainability implications. Yet today, organisations trying to quantify food waste meet several challenges, including varying definitions and quantification approaches creating lack of comparability. In this context, the United Nations Environment Programme and the World Resources Institute launched the Global Food Loss & Waste (FLW) Protocol in October 2013 in 2013 with the objective to develop a “FLW Standard” for accounting and reporting amounts of food waste across the food chain. Nestlé actively contributes to the development of the FLW Standard and conducted in 2014 a pilot project on food waste using its Pakistani dairy value chain as a case study. This case study is a first attempt to test, in a real-life setting, the main methodological principles of the FLW Protocol with a view to fuel ongoing discussions on the harmonization of food waste quantification and to contribute, with practice-based evidence, to scientific knowledge in this area. First, a literature review and interviews with experts of the Pakistani dairy sector were conducted. In addition, a one-week field trip took place in December 2014, in order to make first-hand observations throughout the supply chain. Data from Nestlé, farms, village milk centres and chilling centres, distributors, and retailers were collected. Although the overall amount of waste in a strict sense throughout the case study's supply chain is relatively limited (being ca. 1.4%),1 it appeared that the “unmet production potential” due to the gap between a low production and what could reasonably be attained is of significant magnitude and actually much bigger than the waste in a strict sense. Therefore, the quantification exercise demonstrated the vital importance of a thorough description and analysis of the supply chain in all its dimensions. Indeed, it allowed to identify issues (i.e. “unmet production potential”) that do not fall under the scope of food waste quantification in the sense of the FLW Protocol but that maybe of crucial importance at systemic level.

Effect of vermiculite addition on compost produced from Korean food wastes

To evaluate the effect of vermiculite addition on composting food wastes from Korean households, food wastes were composted in three small bins to which different additives were added. The following three bins were employed: in Case I, only recycled compost was composted; in Case II, food wastes with recycled compost; and in Case III, food wastes with recycled compost and vermiculite. In the experiment performed for 30 days, it was confirmed that the supplementary addition of vermiculite to the composting mixture did not significantly improve the weight loss rate and the decomposition rate of food wastes. Due to dilution through the use of inorganic vermiculite, the vermiculite addition reduced the organic matter concentration of the composting mixtures. Vermiculite addition did not raise the pH value. Weight losses of roughly 70% were observed based on calculating moisture loss as well as dry food waste loss and not considering additives, while dry food waste loss was 29.4% and 35.8% with and without the addition of vermiculite, respectively. For these experiments, the major portion of the weight loss was the loss of water. The results indicate a need to differentiate between weight loss percentages and decomposition percentages, and a need to indicate if either of these percentages includes or excludes the mass of additives.