Percorrer por autor "Fagerlund, Annette"
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- Explorative analysis of microbial communities in chicken productionPublication . Måge, Ingrid; Rasmussen, Morten Arendt; Teixeira, Paula; Moen, Birgitte; Sousa, Clara Santana; Silva, Beatriz Nunes; Fagerlund, Annette
- Explorative analysis of microbial communities in chicken productionPublication . Måge, Ingrid; Rasmussen, Morten Arendt; Teixeira, Paula; Silva, Beatriz Nunes; Sousa, Clara Santana; Fagerlund, Annette; Moen, BirgitteContext: This study is part of the MICROORC project, which aims to reduce food waste by monitoring and utilizing microbiomes in the food processing chain. A key aspect of the project involves mapping microbiome variations along the chicken production line, to identify patterns and potential key control points. Research question: What is the best chemometric tool for exploring variations in complex industrial microbiome data, and interpreting these variations with respect to time, space, and microbial signatures? Methods: The data can be structured either in a two-way or three-way array. The rows in the batch mode represent 28 independent production batches (2 factories x 7 production days x 2 times a day). There are varying numbers of replicates per batch and sampling location, which are averaged in the three-way representation but kept intact in the two-way data. The two-way array was analysed using ASCA with factors Factory, Location and Time of day, while the three-way array was decomposed by PARARAC. The three-way array contains 4% missing elements, as some locations are missing for some batches. ASCA Results: Differences between factories and locations had the highest contribution to the microbial variation. Interestingly, there was also systematic variation du to time of day. SCA was performed to interpret these differences, revealing that the shift along the processing line is related to different genera. The difference between factories (top row) is largest in the clean zone, while the difference between early and late batches (bottom row) is largest in the scalding water. PARAFAC Results: A PARAFAC model with two components explains 23% of the variation in the data. In line with the ASCA result, the loadings reveal that the first component represents the difference between factories, which increases along the processing line. The second component represents variation that also differs between factories, and separates early and late batches in factory A. Conclusion: The main systematic variation is the difference between factories, which increases along the production line. This is represented in both models. The more subtle differences between early and late batches are more challenging to understand, and the methods provide different perspectives on this. Both methods indicate that the difference between early and late batches is largest in the scalding water but also evident in the latest locations. While the PARAFAC model indicates that this effect is only found in factory A, the ASCA results suggest that it is present in both factories. The large residual variation within both models stems from the natural uncertainty within industry level microbial data, as well as from variation between sampling days throughout the year, which is particularly interesting for the project and will be investigated further.
- MICROORC: orchestrating food system microbiomes to minimize food wastePublication . Silva, Beatriz Nunes; Monteiro, Maria João; Moen, Birgitte; Jensen, Merete Rusås; Barbosa, Joana; Mena, Cristina; Teixeira, Susana; Poças, Fátima; Teixeira, Paula; Fagerlund, Annette; Pettersen, Marit Kvalvåg; Langsrud, SolveigContext: Food loss and waste (FLW) pose significant challenges globally, affecting the environment, food security and economies. In 2022, the EU generated over 59 million tonnes of food waste, an average of 132 kg per inhabitant. Households account for 54% of this (72 kg per person), while the remaining 46% comes from the food supply chain. Food waste in the supply and consumption sectors represents about 10% of the food supplied in the EU. Tackling food waste is a triple win: it saves food for human consumption, reduces the environmental impact of food production and helps businesses and consumers save money. This is particularly important given that nearly 10% of EU citizens cannot afford a quality meal every two days. Portugal is the third country in the EU with the highest food waste, which also translates into a financial loss1. According to a Too Good To Go study, the average Portuguese citizen wastes 28€ per month on discarded food, rising to 33€ for young adults (18-24 years old). Over a year, this represents a 336€ loss per person due to poor food management2. Consequently, reducing FLW has become a priority on the sustainability agenda at all levels. Objectives and ambition: The MICROORC project will develop sustainable solutions that reduce and prevent food spoilage and food waste, with focus on technologies, services, tools, policies, and practices that are based on monitoring, utilizing, and targeting microbiomes in food and the food processing chain. Methodology: This will be achieved through two main pathways: 1) developing tools, technologies, and guidelines for improved process control, microbiome monitoring, and more accurate shelf life prediction and labelling and 2) extending shelf life through bioprotection and packaging technologies that control or limit the growth of spoilage or pathogenic organisms, thereby reducing avoidable food waste. The first pathway focuses on reducing food waste linked to date marking by developing microbiome-based tools for better process control and shelf life prediction. The second pathway targets microbiome-based protection technologies (food cultures and fermentates) to replace synthetic chemicals for controlling pathogens and spoilage bacteria, extending shelf life. Additionally, sustainable packaging systems will be developed, balancing shelf life, safety, food waste, and environmental impact. Sustainability and policy assessments will guide innovation, with new regulations and policy recommendations co-created with stakeholders. Dissemination strategies will maximize the project's impact. Three selected products serve as case studies: fresh chicken, smoked salmon, and plant-based meat analogues. MICROORC is organized into seven work packages. Through collaboration between cutting-edge companies and experienced research institutions, MICROORC will fulfil the development needs of the industry to reach TRLs > of 6 and 7 or above.
- The role of the hygiene of the process environment on shelf life of poultry meatPublication . Langsrud, Solveig; Moen, Birgitte; Silva, Beatriz Nunes; Teixeira, Paula; Fagerlund, Annette; Måge, Ingrid; Pursti, Sophie; Rusas Jensen, Merete; Bjørkøy, Solfrid; Lian, InaAim: Poultry meat is prone to spoilage by microorganisms, limiting the length of the shelf life and potentially leading to unnecessary food waste. The air chiller has been suggested as an important source of spoilage organisms in previous investigations. The aim of this study was to determine the role of the hygienic level of the chilling room on the shelf life of chicken. Method: Samples were taken from 30 batches of chicken breasts (stored until the end of shelf life at 4 ºC) produced in three different factories (two Norwegian, one Portuguese) and corresponding swab samples from the chilling room surface. The microbial numbers were determined by spreading on PCA and the microbiota by partial 16S amplicon sequencing (Illumina). Results: For the two Norwegian chicken processing plants (respectively 18 batches and 6 batches), a covariation between microbial levels in the chilling room and the final product was found. Carnobacterium and Lactobacillus dominated in the end-of-shelf-life products while Acinetobacter, Psychrobacter and Pseudomonas were abundant in the air chiller in both plants. For the Portuguese plant (6 batches), no covariation was found between the bacterial levels in the air chiller and final products. Brochotrix and Serratia dominated the end-of-shelf-life products, while Psychrobacter and Micrococcaceae were abundant in the air chiller. Although the same bacterial genera found in the final products were also detected in the air chillers, they were not the most abundant in the air chiller environment. Conclusion: The findings suggest that for some chicken processing factories, the air chiller can be regarded as a critical control point to monitor as its microbial load and microbiota covaries with those of end-of-shelf-life chicken products. However, the air chiller did not appear as an important contamination source for spoilage organisms, and covariation was not found for all factories. Further work will be performed to reveal the main contamination sources in the poultry production environment.