Developing supply chain innovations - requirements for research and challenges for the food industry

The European food system serves 480 million people each day with food and drink (Raspor, McKenna & de Vries, 2007). It is of intense current research interest to understand how food purchase choice will impact on resource use, climate change and public health (Deloitte, 2007). It is clear that the current food needs of consumers in developed nations are becoming more complex with consideration of environmental impact, social responsibility, functional foods, nutraceuticals, obesity and food miles, amongst many issues, driving the emergence of new products (UK Cabinet Office Strategy Unit, 2008). The research reported here shows how aspects of food manufacture can enhance the quality control, decrease environmental impact and improve traceability of products in food supply chains. We specifically use examples of accounting for carbon dioxide emissions, water use and food production / transport approaches in supply chains to show how manufacturers can improve their operational awareness of such factors and stimulate innovative solutions. The research presented also considers the impact of developing comprehensive sensory and consumer research when new manufacturing practices are utilised

Sensory quality control in the chilled and frozen ready meal, soup and sauce sectors

This chapter discusses the use of sensory evaluation in the assurance of product quality within the food production sectors of ready meals, soups and sauces. The chapter methodically reviews typical food processing stages, from recipe development through to end product supply, and considers how sensory assessment methods can be utilised to help assure the quality of the end products within these selected High Risk Chilled Food sectors

Opportunities for greater Lincolnshire's supply chains: summary report

A study of the key sector supply chains across Lincolnshire and the barriers and opportunities for growth

Opportunities for greater Lincolnshire's supply chains: full report

A study of the key sector supply chains across Greater Lincolnshire, and identification of barriers and opportuniteis for growth

Impact of food hazards in school meals on students' health, academic work and finance – Senior High School students' report from Ashanti Region of Ghana

The study investigated the types of food hazards, the incidences and effect of foodborne diseases (FBD) in Senior Secondary Schools in Ghana. A questionnaire was used to collect data from 180 boarding school level 1 and 2 students from 45 sampled public schools in the Ashanti Region of Ghana and analysed with SPSS Version 21. Stones and insects in food received the highest complaints alongside food allergy and intolerance. Out of 180 students, 51.7% had experienced FBD with 21.1% of these reporting to health centers within their 1–2 years in school. FBD incidence rate was 3–12 times per academic year and 12% of the students had been absent from active academic work for as long as 5 days due to FBD with 10% spending between GHC 30.00 → 50.00 on medication per each episode. Students recommended improved GHP including standard cleaning procedures, food temperature control, available hand washing facilities with detergents at the dining halls and kitchens. Mandatory requirement of routine hygiene and food safety training for food handlers was required in schools with heightened monitoring, surveillance and law enforcement on acceptable practices. Supplier control across the food chain to reduce physical and chemical contaminants in agro products and food vendor's access control was required. Improving the quality and variety of school meals could also reduce dependence on other sources for food and help in controlling food safety risks. There was a need to increase awareness on the appropriate channels to report FBD incidence in schools for effective control measures and infection treatment

Multi-source domain adaptation for quality control in retail food packaging

Peer reviewedPostprin

Impact of salt and sugar reformulation on processing parameters for orange juice and tomatoes using ohmic heating

The purpose of this research is in twofold: first, it aims to investigate how salt and sugar reduction in foods due to the pressure from the emerging food regulations will affect the physico-electrical properties (PEPs) of orange juice and tomatoes during a selected PEP-dependent thermal processing. Second, the authors are keen to understand how variations in salt and sugar ingredients will affect the time-temperature processing requirements. Physico-electrical properties of the food products (orange juice and tomatoes) were measured using the KD2 thermal analyser and RS conductivity meter. Both samples with varying salt and sugar levels were subjected to Ohmic Heating processing using a 10kW Ohmic Heater. Dehydration rates and processing times for pasteurisation were obtained. Electrical conductivity increases with added salt in tomato puree but decreases with added sugar in orange juice. Statistical evidence confirmed significant changes in heating rates and processing times of tomatoes and orange juice as their relevant salt and sugar levels change. Reduction in salt content in tomato puree led to an increase in time and energy for the thermal processes. While a reduction in added sugar in orange juice results led to a reduction in processing time and energy requirement for the processing operation. The study is limited to a small change in salt and sugar variations in order to reflect recommended limits. There were, therefore, no significant changes in thermal conductivity for the range investigated. Also, this study is focused on two food products. The current pressure on the need to reduce salt and sugar in foods necessitates research to increase food processing industry insight into the process and product impacts of such recipe changes, with particular regard to processing efficiency and product safety and quality. This study represents an attempt to understand the impact of salt and sugar variations on properties and processing requirements of tomato puree and orange juice

An adaptable deep learning system for optical character verification in retail food packaging

Retail food packages contain various types of information such as food name, ingredients list and use by dates. Such information is critical to ensure proper distribution of products to the market and eliminate health risks due to erroneous mislabelling. The latter is considerably detrimental to both consumers and suppliers alike. In this paper, an adaptable deep learning based system is proposed and tested across various possible scenarios: a) for the identification of blurry images and/or missing information from food packaging photos. These were captured during the validation process in supply chains; b) for deep neural network adaptation. This was achieved through a novel methodology that utilises facets of the same convolutional neural network architecture. Latent variables were extracted from different datasets and used as input into a k-means clustering and k-nearest neighbour classification algorithm, to compute a new set of centroids which better adapts to the target dataset’s distribution. Furthermore, visualisation and analysis of network adaptation provides insight into how higher accuracy was achieved when compared to the original deep neural network. The proposed system performed very well in the conducted experiments, showing that it can be deployed in real-world supply chains, for automating the verification process, cutting down costs and eliminating errors that could prove risky for public health

Deep Bayesian Self-Training

Supervised Deep Learning has been highly successful in recent years, achieving state-of-the-art results in most tasks. However, with the ongoing uptake of such methods in industrial applications, the requirement for large amounts of annotated data is often a challenge. In most real world problems, manual annotation is practically intractable due to time/labour constraints, thus the development of automated and adaptive data annotation systems is highly sought after. In this paper, we propose both a (i) Deep Bayesian Self-Training methodology for automatic data annotation, by leveraging predictive uncertainty estimates using variational inference and modern Neural Network architectures, as well as (ii) a practical adaptation procedure for handling high label variability between different dataset distributions through clustering of Neural Network latent variable representations. An experimental study on both public and private datasets is presented illustrating the superior performance of the proposed approach over standard Self-Training baselines, highlighting the importance of predictive uncertainty estimates in safety-critical domains