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This study aims to examine, through stakeholders consultation, the widely used definitions of four terms related to plastics sustainability: 'bio-based plastics', 'bioplastics', 'biodegradable plastics' and 'plastics recycling' and to mitigate their potential ambiguity for diverse scientific communities and sectors of activity. For the three terms 'bio-based plastics', 'biodegradable' and 'recycling', consolidated definitions were elaborated based on the feedback of online survey and analysis of the pro and con arguments given by face-to-face interviews with 18 experts followed by an online survey of 122 stakeholders. Acceptance of the consolidated definitions was higher than the official ones with an increase of acceptance from 43% to 81% for bio-based plastics, from 47% to 61% for biodegradable plastics, and from 28% to 60% for plastics recycling. The terms 'biodegradable' and 'recycling' remain ambiguous even after consolidation of the definition. This highlights that more discussions are necessary to achieve a consensual and fair definition of such complex properties and mechanisms. In the term 'Bioplastics' the prefix 'bio', referring either to the origin of the resources or the end of life of the material, remains difficult to understand and we prefer to advise against its use, especially with non-expert people (e.g. consumers and the public at large), in favour of the use of 'bio-based plastics' or 'biodegradable plastics'. The issue of this study is to help consolidate wider efforts to develop new strategies for replacing oil-based plastics and improving end-of-life options. [Display omitted] • Bio-based plastics, bioplastics, biodegradable plastics, plastics recycling. • Interviews and survey were undertaken in scientific and societalspheres. • Consensual meanings and consolidated definitions are proposed. • The use of the prefix 'bio' and the term recycling revealed ambiguity. • It is advised against the use of 'bioplastics' term. [ABSTRACT FROM AUTHOR]

Abstract Food packaging plays a crucial part in the post-harvest environmental impact of fresh foods. Packaging is usually wrongly considered as additional economical and environmental costs. However, by minimizing food waste and losses, it could significantly contribute to decrease the overall environmental impact of the food itself. A good balance between environmental burden (resource consumption and additional waste management issues) and real benefit in usage condition (reduction of food losses) should be thus defined when dimensioning a packaging for a given application. Beyond food waste and environmental impact reduction, various kinds of considerations about packaging, sometimes conflicting, are generally expressed by the stakeholders (food and packaging industries, health authorities, consumers, waste management authority, etc.) related to safety, practicality, perceptions of the packaging material, etc. Therefore, to help the parties deciphering all these arguments, we designed an argumentation-based tool to take into account the conflicting preferences expressed. The requirements concerning packagings are modeled by several arguments provided by the stakeholders expressing their viewpoints and expertise. Based on a new attack relation, the argumentation tool computes sets of compatible arguments which are used to rank alternative packagings under debate. In this paper, we present a complete workflow implemented as a software prototype starting by defining a structured representation of experts arguments and poll results, and ending by a ranking of packaging solutions. We show and discuss the results obtained by the software on a use case study (fresh strawberries) to determine the justifiable choices between several packaging materials based on stakeholders' arguments. Highlights • An argumentation system dedicated to the selection of packaging solutions well suited for a given food product • Arguments may be either manually entered or automatically generated from a set of responses to a given web survey • A module designed to allow the ranking of packaging solutions using a set of arguments and a prioritization of requirements • An evaluation of the argumentation tool within the Pack4Fresh project with an interdisciplinary collaboration [ABSTRACT FROM AUTHOR]

We introduce a formal model of explanatory dialogue called EDS . We extend this model by including argumentation capacities to facilitate knowledge acquisition in inconsistent knowledge bases. To prove the relevance of such model we provide the d a l e k (DiALectical Explanation in Knowledge-bases) framework that implements this model. We show the usefulness of the framework on a real-world application in the domain of Durum Wheat sustainability improvement within the ANR (French National Agency) funded Dur-Dur project. The preliminary pilot evaluation of the framework with agronomy experts gives a promising indication on the impact of explanation dialogues on the improvement of the knowledge’s content. [ABSTRACT FROM AUTHOR]

Here we present the Xart system based on a three-step hybrid method using data mining approaches and syntactic analysis to automatically discover and extract relevant data modeled as n-ary relations in plain text. A n-ary relation links a studied object with its features considered as several arguments. We addressed the challenge of designing a novel method to handle the identification and extraction of heterogeneous arguments such as symbolic arguments, quantitative arguments composed of numbers and various measurement units. We thus developed the Xart system, which relies on a domain ontology for discovering patterns, in plain text, to identify arguments involved in n-ary relations. The discovered patterns take advantage of different ontological levels that facilitate identification of all arguments and pool them in the sought n-ary relation. [ABSTRACT FROM AUTHOR]

A new method to extract knowledge structured as n-Ary relations from scientific articles is presented. We designed and assessed different approaches to reconstruct instances of n-Ary relations extracted from scientific articles in experimental domains, driven by an Ontological and Terminological Resource (OTR) and based on multi-feature representation of relations and their arguments. The proposed method starts with the identification of partial n-Ary relations in tables of scientific articles and then seeks to reconstruct them with argument instances in the article texts. Based on the so-called Scientific Publication Representation (SciPuRe) of textual arguments and Scientific Table Representation (STaRe) of n-Ary relations representation of an n-Ary relation called STaRe (Scientific Table Representation, originating from partial n-Ary relations extracted from document tables), here we propose and evaluate different approaches for the selection of textual argument instances that could complement partial n-Ary relations: structural, frequentist and word embedding models. The application domain concerns food packaging, especially composition and permeability data. Experiments were conducted on a corpus of 332 relation instances composed of 1547 arguments. Corpora of full and partial relations recognized in document tables and argument instances extracted from texts are available online. Different methods and strategies were measured with an f-score ranging from. 34 to. 74. These results show that n-Ary relations reconstruction approach depends on the number of selected candidate argument instances. • A generic method to extract n-Ary relations based on a domain ontology. • An approach using partial n-Ary relation in tables completed by text arguments. • Multi-criteria approaches to complete partial n-Ary relation with argument instances. • A complete pipeline for the extraction of n-Ary relations driven by an OTR. • A corpus in the food packaging field to assess the method. [ABSTRACT FROM AUTHOR]

This paper presents a workflow for the design of transformation processes using different kinds of expert's knowledge. It introduces POND (Process and observation ONtology Discovery), a workflow dedicated to answer expert's questions about processes. It addresses two main issues: (1) how to represent the processes inner complexity, and (2) how to reason about processes taking into account uncertainty and causality. First, we show how to use a semantic model, an ontology, and its associated data to answer some of the expert's questions concerning the processes, using semantic web languages and technologies. Then, we describe how to learn a predictive model, to discover new knowledge and provide explicative models by integrating the semantic model into a probabilistic relational model. The result is a complete workflow able to extensively analyze transformation processes through all their granularity levels and answer expert's questions about their domains. An example of this workflow is given on biocomposites manufacturing for food packaging. • Due to their complexity, reasoning with transformation processes is challenging. • POND is a workflow aiming to represent and reason with transformation processes. • Representation is achieved with the ontology PO² and its associated softwares. • Reasoning under uncertainty is achieved with probabilistic graphical models. • Expert knowledge integration allows knowledge and causal discovery. [ABSTRACT FROM AUTHOR]

Abstract: A preliminary step to risk in food assessment is the gathering of experimental data. In the framework of the Sym''Previus project (http://www.symprevius.org), a complete data integration system has been designed, grouping data provided by industrial partners and data extracted from papers published in the main scientific journals of the domain. Those data have been classified by means of a predefined vocabulary, called ontology. Our aim is to complement the database with data extracted from the Web. In the framework of the WebContent project (www.webcontent.fr), we have designed a semi-automatic acquisition tool, called @WEB, which retrieves scientific documents from the Web. During the @WEB process, data tables are extracted from the documents and then annotated with the ontology. We focus on the data tables as they contain, in general, a synthesis of data published in the documents. In this paper, we explain how the columns of the data tables are automatically annotated with data types of the ontology and how the relations represented by the table are recognised. We also give the results of our experimentation to assess the quality of such an annotation. [Copyright &y& Elsevier]

Various predictive models of microbial behavior have been created and extensive data collection has been done by numerous private or public laboratories. However, significant differences between predicted and observed values in foods have been observed and need to be stressed, understood and explained as much as possible. In this paper, we present a software tool (currently at the level of a prototype) able: (i) to store in a database all relevant information expressed on one hand as qualitative or quantitative data and on the other hand as precise or imprecise data; (ii) to retrieve the more relevant information from the database using queries where criteria may be expressed as fuzzy values in order to enhance the flexibility of the search; (iii) to compute, in addition to the nearest data, an estimation of searched values using statistical models. The architecture of this software tool is structured as a category-based reasoning system. Example queries about Listeria monocytogenes (L. monocytogenes) illustrate the functionalities of this tool. [Copyright &y& Elsevier]

This article presents an overview of five use-cases that illustrate food-system modeling approaches, most of them combining quantitative and qualitative methods. The cases deal with issues as diverse as: the modeling of winemaking fermentation; the choice of a food packaging material; the design of solid foam cereal products; argumentation modeling to support multistakeholder decision-making, and the control of freeze-drying for eco-design purposes. They tackle several challenges to modeling and knowledge engineering in food systems: capitalizing data and knowledge; structuring a shared vocabulary and domain knowledge in an ontology; formalizing viewpoints and contradictions from food-related debates; supporting multi-criteria decision-making. [ABSTRACT FROM AUTHOR]

This paper presents an ontological approach of scientific experimental data integration across complementary sub-domains, i.e., agricultural production and food processing, with an application to viticulture and winemaking. The two main steps in this approach are (i) to integrate preexisting ontologies to create a so-called ontology network and (ii) to populate the ontology network with experimental data from various sources. The Agri-Food Experiment Ontology (AFEO), a new ontology network, was developed, based on two ontological resources, i.e., AEO (Ontology for Agricultural Experiments) and OFPE (Ontology for Food Processing Experiments). It contains 136 concepts which cover various viticulture practices, as well as winemaking products and operations. AFEO was used to guide the data integration of two different data sources, i.e., viticulture experimental data stored in a relational database, and winemaking experimental data stored in Microsoft Excel files. Two applications illustrate the approach. The first one is on wine traceability and the second one is related to the influence of irrigation practices and winemaking methods on GSH concentration in wine. These examples show that data integration guided by an ontology network can provide researchers with the information necessary to address extended research questions. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2) is an important gas used in modified atmosphere packaging of non-respiring foods where it solubilizes into the aqueous and lipid phases of food and exerts an antimicrobial effect. Prediction of CO2 solubility within food is thus of paramount importance to anticipate its benefit on food preservation. In the present study, machine learning algorithms were applied on a set of 362 values of CO 2 solubilities collected from the scientific literature to tentatively predict the solubility as a function of food composition (water, protein, fat and salt content) and temperature. The best option kept was a random forest algorithm that was used to predict CO 2 solubility in four food case studies (ham, salmon, cheese and pâté) that were further used as input parameters in the MAP' OPT tool, predicting the evolution of headspace gas composition. Predicted CO 2 solubilities used as input parameters succeeded in representing the CO 2 headspace dynamic as a function of time in the four case studies. [ABSTRACT FROM AUTHOR]