Milk microfiltration process dataset annotated from a collection of scientific papers

PO2/TransformON, an ontology for data integration on food, feed, bioproducts and biowaste engineering

We are witnessing an acceleration of the global drive to converge consumption and production patterns towards a more circular and sustainable approach to the food system. To address the challenge of reconnecting agriculture, environment, food and health, collections of large datasets must be exploited. However, building high-capacity data-sharing networks means unlocking the information silos that are caused by a multiplicity of local data dictionaries. To solve the data harmonization problem, we proposed an ontology on food, feed, bioproducts, and biowastes engineering for data integration in a circular bioeconomy and nexus-oriented approach. This ontology is based on a core model representing a generic process, the Process and Observation Ontology (PO2), which has been specialized to provide the vocabulary necessary to describe any biomass transformation process and to characterize the food, bioproducts, and wastes derived from these processes. Much of this vocabulary comes from transforming authoritative references such as the European food classification system (FoodEx2), the European Waste Catalogue, and other international nomenclatures into a semantic, world wide web consortium (W3C) format that provides system interoperability and software-driven intelligence. We showed the relevance of this new domain ontology PO2/TransformON through several concrete use cases in the fields of process engineering, bio-based composite making, food ecodesign, and relations with consumer's perception and preferences. Further works will aim to align with other ontologies to create an ontology network for bridging the gap between upstream and downstream processes in the food system.

Recent innovations and emerging technological advances used to improve quality and process of plant-based milk analogs

The worldwide challenges related to food sustainability are presently more critical than ever before due to the severe consequences of climate change, outbreak of epidemics, and wars. Many consumers are shifting their dietary habits toward consuming more plant-based foods, such as plant milk analogs (PMA) for health, sustainability, and well-being reasons. The PMA market is anticipated to reach US$38 billion within 2024, making them the largest segment in plant-based foods. Nevertheless, using plant matrices to produce PMA has numerous limitations, including, among others, low stability and short shelf life. This review addresses the main obstacles facing quality and safety of PMA formula. Moreover, this literature overview discusses the emerging approaches, e.g., pulsed electric field (PEF), cold atmospheric plasma (CAP), ultrasound (US), ultra-high-pressure homogenization (UHPH), ultraviolet C (UVC) irradiation, ozone (O3), and hurdle technology used in PMA formulations to overcome their common challenges. These emerging technologies have a vast potential at the lab scale to improve physicochemical characteristics, increase stability and extend the shelf-life, decrease food additives, increase nutritional and organoleptic qualities of the end product. Although the PMA fabrication on a large scale using these technologies can be expected in the near future to formulate novel food products that can offer green alternatives to conventional dairy products, further development is still needed for wider commercial applications.

Integrating collective know-how for multicriteria decision support in agrifood chains-application to cheesemaking

Agrifood chain processes are based on a multitude of knowledge, know-how and experiences forged over time. This collective expertise must be shared to improve food quality. Here we test the hypothesis that it is possible to design and implement a comprehensive methodology to create a knowledge base integrating collective expertise, while also using it to recommend technical actions required to improve food quality. The method used to test this hypothesis consists firstly in listing the functional specifications that were defined in collaboration with several partners (technical centers, vocational training schools, producers) over the course of several projects carried out in recent years. Secondly, we propose an innovative core ontology that utilizes the international languages of the Semantic Web to effectively represent knowledge in the form of decision trees. These decision trees will depict potential causal relationships between situations of interest and provide recommendations for managing them through technological actions, as well as a collective assessment of the efficiency of those actions. We show how mind map files created using mind-mapping tools are automatically translated into an RDF knowledge base using the core ontological model. Thirdly, a model to aggregate individual assessments provided by technicians and associated with technical action recommendations is proposed and evaluated. Finally, a multicriteria decision-support system (MCDSS) using the knowledge base is presented. It consists of an explanatory view allowing navigation in a decision tree and an action view for multicriteria filtering and possible side effect identification. The different types of MCDSS-delivered answers to a query expressed in the action view are explained. The MCDSS graphical user interface is presented through a real-use case. Experimental assessments have been performed and confirm that tested hypothesis is relevant.

Membrane applications in the food industry

Current trends in the food industry for the application of membrane techniques are presented. Industrial solutions as well as laboratory research, which can contribute to the improvement of membrane efficiency and performance in this field, are widely discussed. Special attention is given to the main food industries related to dairy, sugar and biotechnology. In addition, the potential of membrane techniques to assist in the treatment of waste sources arising from food production is highlighted.