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The study aimed to investigate the effect of introducing texturized soy protein (TSP) at different levels (15% and 30%) with and without nutritional yeast as flavour enhancer on the sensory and instrumental quality of beef meatballs, compared to a soy and yeast-free control. Proximate analysis, colour, instrumental texture, cook loss, and sensory quality were investigated. Sixty participants assessed the samples using Check-all-that-apply (CATA) questions and hedonic scales. Overall, the texture of all TSP-containing samples received significantly higher acceptability scores than control, while 15% TSP with yeast received the highest flavour and overall acceptability scores. Penalty-lift analysis of CATA terms identified the main drivers for liking as “moist looking”, “juicy”, “soft” and “crumbly and easy to cut”. Control samples were significantly more often associated than the other recipes to the term “hard”, a key driver for dislike and the least associated to “soft” and “crumbly and easy to cut”. Adding 15–30% TSP with or without yeast inclusion could be beneficial for the development of future meat hybrids with acceptable sensory quality.

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.

Bacteria have evolved to become proficient at adapting to both extracellular and environmental conditions, which has made it possible for them to attach and subsequently form biofilms on varying surfaces. This has resulted in major health concerns and economic burden in both hospital and industrial environments. Surfaces which prevent this bacterial fouling through their physical structure represent a key area of research for the development of antibacterial surfaces for many different environments. Laser surface treatment provides a potential candidate for the production of antibiofouling surfaces for wide ranging surface applications within healthcare and industrial disciplines. In the present study, a KrF 248 nm Excimer laser was utilized to surface pattern polyethylene terephthalate (PET). The surface topography and roughness were determined with the use of a Micromeasure 2, 3D profiler. Escherichia coli (E. coli) growth was analyzed at high shear flow using a CDC Biofilm reactor for 48 h, scanning electron microscopy was used to determine morphology and total viable counts were made. Through this work, it has been shown that the surface modification significantly influenced the distribution and morphology of the attached E. coli cells. What is more, it has been evidenced that the laser-modified PET has been shown to prevent E. coli cells from attaching themselves within the laser-induced micro-surface-features.

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.

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