Mark Swainson

The food industry is faced with the challenges of pursuing operational efficiency, resource efficiency, good product quality, regulatory compliance, and sustainability. Lean production and sustainability practices have found immense application in the food industry. However, their integration into validation activities is still under investigation. The aim of this study is to explore how lean production principles and sustainability practices can be integrated into equipment validation stages to improve resource efficiency in the food industry. Specifically, this study aims to investigate the relationship between lean production, equipment validation, and sustainability. A structured narrative literature review was conducted using Scopus, Web of Science, and Google Scholar, restricted to peer-reviewed English publications, and yielded 58 primary sources. No published framework systematically integrates lean tools, sustainability indicators, and the four qualification stages of equipment validation in the food manufacturing context. This study addresses this gap through the development of the Lean Sustainability Validation (LSV) framework, a six-layer conceptual model mapping lean tools, and sustainability Key Performance Indicator (KPI) to each validation stage. Validation activities represent an underutilized strategic level for achieving lean and sustainable objectives simultaneously, and further empirical research is needed to test and refine the LSV framework across diverse food manufacturing contexts.

Here we show how food and beverage manufacturers report more incisive sustainability and product fulfilment outcomes for their business enterprises when innovative processing technologies are used. The reported steam infusion technology heats food materials within a Vaction Pump device so that steam is directed into the food material within a much reduced volume, reducing the use of steam and processing time. This study reports how such technological interventions will enable supply chain stakeholders to demonstrate responsible consumption by connecting assessments for the reduction of greenhouse gas emissions with consumer-focused outcomes such as product quality. The technology reported in this research not only improves operational agility by improving processing speed, but also improves the responsiveness of factory production to changes in demand. Heating procedures are systemic processes in the food industry that can be used to pasteurize, achieve commercially viable shelf-life, and provide cleaning in place. The reported research defines how these technologies can reduce the carbon footprint of products, improve quality attributes, and lower operating costs across supply chains. They provide an important step in developing distributed manufacturing in the food system because the technologies reported here are modular and can be installed into existing operations. The specific technology can reduce energy consumption by 17.3% compared to basic direct steam heating, with a reduction of 277.8 processing hours and 8.7 tonnes GHG emissions per kettle production line each year. Food and beverage manufacturers are increasingly required to report across the sustainability, nutrition, and product quality outcomes of their business enterprises more incisively so that supply chain stakeholders can demonstrate responsible production and consumption. The steam infusion technologies assessed in this research enable alignment to the UN Sustainable Development Goals, specifically SDG12, Responsible Production and Consumption, using in situ data logging in factory trials for novel heating procedures used to process foods.

Processes that utilise low-value wastes and convert them to high-value food ingredients systemically add value across commercial operations. Current common disposal options include use as animal feed, anaerobic digestion, composting, incineration, and the worst-case options of landfill and wastewater disposal. The pressure is acute with food manufacturers needing to align with the UN Sustainable Development Goals and reach targets of zero waste to landfill. This research identifies black soldier fly larvae as a bioreactor that converts most food waste into high-value feed materials. Production of larvae and the regulatory framework for their use as animal feed is being assessed in several nations. The requirement to understand the availability of feedstocks for larvae production and the capability to establish feedstock supply chains was tested in this study using geographical information system and life cycle assessment methodologies, providing new research insights for resource utilisation in a circular economy.

Summary: This study focuses on foodborne outbreaks of microbial infection associated with fresh produce in Europe and North America from this millennium. A total of 277 outbreaks with 44 524 individual cases were identified. Foodborne pathogens associated with the most outbreak frequency include Cryptosporidium (20.5%) in Europe and Salmonella (52.2%) in North America although Norovirus (54.3%) and Salmonella (61.3%) were associated with the number of cases in Europe and North America, respectively. Vegetables were the most implicated fresh produce category with outbreak frequencies of 34.1% in Europe and 47.4% in North America. Increased consumption of fresh produce in Europe and North America, as measures to improve diets, correlates with the increased fresh produce‐related outbreaks of microbial infection. This systematic review suggests the need for more rapid methodologies for traceback investigations in order to determine trends and epicentres of foodborne infections related to fresh produce.

There exist various types of information on retail food packages, including use by date, food product name and so on. The correct coding of use by dates on food packages is vitally important for avoiding potential health risks to customers caused by erroneous mislabelling of use by dates. It is extremely tedious and laborious to check the use by dates coding manually by a human operator, which is prone to generate errors thus an automatic system for validating the correctness of the coding of use by dates is needed. In order to construct such a system, firstly it needs to correctly automatic recognize use by dates on food packages. In this work, we propose a novel dual deep neural networks-based methodology for automatic recognition of use by dates in food package photographs recorded by a camera, which is a combination of two networks: a fully convolutional network for use by date ROI detection and a convolutional recurrent neuron network for date character recognition. The proposed methodology is the first attempt to apply deep learning for automatic use by date recognition. From comprehensive experimental evaluations, it is shown that the proposed method can achieve high accuracies in use by date recognition (more than 95% on our testing dataset), given food package images with varying lighting conditions, poor printing quality and varied textual/pictorial contents collected from multiple real retailer sites.

Retail food packaging contains information which informs choice and can be vital to consumer health, including product name, ingredients list, nutritional information, allergens, preparation guidelines, pack weight, storage and shelf life information (use-by / best before dates). The presence and accuracy of such information is critical to ensure a detailed understanding of the product and to reduce the potential for health risks. Consequently, erroneous or illegible labeling has the potential to be highly detrimental to consumers and many other stakeholders in the supply chain. In this paper, a multi-source deep learning-based domain adaptation system is proposed and tested to identify and verify the presence and legibility of use-by date information from food packaging photos taken as part of the validation process as the products pass along the food production line. This was achieved by improving the generalization of the techniques via making use of multi-source datasets in order to extract domain-invariant representations for all domains and aligning distribution of all pairs of source and target domains in a common feature space, along with the class boundaries. The proposed system performed very well in the conducted experiments, for automating the verification process and reducing labeling errors that could otherwise threaten public health and contravene legal requirements for food packaging information and accuracy. Comprehensive experiments on our food packaging datasets demonstrate that the proposed multi-source deep domain adaptation method significantly improves the classification accuracy and therefore has great potential for application and beneficial impact in food manufacturing control systems.

Pressure points in global food supply where resilience in supply chains can be limited or controlled are the equivalent of Critical Control Points in food safety systems. The approach of using critical control in supply chains can provide insights for nutritional improvement, sustainable food trade and food waste reduction. The pressure points determine the provision of a secure and sustainable food system where the outcomes of reducing their criticality are identified in the UN Sustainable Development Goals and other international programmes. These seek to reduce climate change impact and improve public health provision. While policy makers are rightly focussed on these targets, the data analysis methods required to rank and associate resource flow pressure points with commercial food supply chains and nutritional goals remain untested. Here, we show how methodologies can identify where opportunities to tackle future criticality exist, and where they are currently being overlooked for food categories that have the greatest consumer and dietary protein demand. The analysis provides insights that identify where latent restrictions in resilience can occur, so that the future risk of food insecurity is reduced.