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.

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.

CO2 solubility and composition data of food products stored in data warehouse structured by an ontology.

This data paper presents the values of CO2 solubility at different temperatures and main compositional parameters (protein, fat, moisture, sugars and salt content) for food products from different categories: dairy products, fishes and meats. It is the result of an extensive meta-analysis gathering the results of different major papers published on the domain on the period of 1980 to 2021, presenting the composition of 81 different food products corresponding to 362 solubility measures. For each food product, the compositional parameters were either extracted directly from the original source, or extracted from open-source databases. This dataset has also been enriched with measurements made on pure water and oil for comparison purposes. In order to ease the comparison between different sources, data have been semantized and structured by an ontology enriched with domain vocabulary. They are stored in a public repository and can be retrieved through the @Web tool, a user-friendly interface allowing to capitalize and query the data.

Biocomposites from poly(3-hydroxybutyrate-co-3-hydroxyvalerate) and lignocellulosic fillers: Processes stored in data warehouse structured by an ontology.

Due to the rising amount of plastic waste generated each year, multiple questions are emerging about their harmful long-term effects on the environment, the eco-systems and human health. One possible strategy to mitigate these issues is to substitute conventional plastics by materials fully biodegradable in natural conditions, such as poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV). In order to decrease the overall cost and environmental impact of PHBV-based materials while modulating their technical performance, PHBV can be combined with lignocellulosic fillers. In this article, a total of 88 formulations of PHBV-based biocomposites has been collected, distributed over 5 interdisciplinary projects involving computer scientists, data scientists and biomass processing experts for food and bio-based material production. Available data concern the technical process descriptions, including the description of each step and the different observations measured. These data are stored in a knowledge base that can be queried on the Web.

Partial n-Ary relation instances on food packaging composition and permeability extracted from scientific publication tables.

This dataset is dedicated to text mining and is composed of partial n-Ary relation instances concerning food packaging composition and gas permeability. It was created from 31 tables derived from 10 English-language scientific articles in html format from several international journals hosted on the ScienceDirect website. This dataset includes two sets of data: manual table annotation results and automatic data extraction results. The tables were first annotated by one annotator and cross-curated by three different annotators. The annotation task aimed to identify all table data dealing with packaging permeability measurements and compositions. An Ontological and Terminological Resource (OTR) was used for the annotation process. The annotation guidelines were drawn up through a collective iterative approach involving the annotators, and they may be accessed alongside the data. This dataset of n-Ary relations can be used in natural language processing (NLP) approaches implemented in experimental fields, especially for n-Ary relation extraction research. It can also be useful for training or evaluation of methods for the extraction of experimental data from tables and text in scientific documents, especially in experimental domains such as food packaging.

Milk microfiltration process dataset annotated from a collection of scientific papers.

Milk microfiltration process plays a key role in the dairy industry. Crossflow microfiltration of skimmed milk using a membrane with 0.1 µm mean pore size is widely used to fractionate the two main groups of dairy proteins: casein micelles (~150 nm) and serum proteins (~2-15 nm). Retentate, containing mainly casein micelles, is generally used to enrich vat milk for cheese making. Permeate, containing serum proteins, lactose and minerals, is usually ultrafiltered in order to produce protein-rich concentrate with a high nutritional value dedicated to specific populations such as infants and seniors. The great interest in these protein fractions explains the increasing number of microfiltration equipments in the dairy industry. This data article contains data associated with milk microfiltration process experiments and properties of the resulting dairy fractions annotated from a collection of scientific documents. These data are stored in INRAE public repository (see Data accessibility in the Specification Table for direct links to data). They have been structured using MILK MICROFILTRATION ontology and are replicated in @Web data warehouse providing additional querying tools (https://www6.inrae.fr/cati-icat-atweb/).

Food packaging permeability and composition dataset dedicated to text-mining.

This dataset is composed of symbolic and quantitative entities concerning food packaging composition and gas permeability. It was created from 50 scientific articles in English registered in html format from several international journals on the ScienceDirect website. The files were annotated independently by three experts on a WebAnno server. The aim of the annotation task was to recognize all entities related to packaging permeability measures and packaging composition. This annotation task is driven by an Ontological and Terminological Resource (OTR). An annotation guideline was designed in a collective and iterative approach involving the annotators. This dataset can be used to train or evaluate natural language processing (NLP) approaches in experimental fields, such as specialized entity recognition (e.g. terms and variations, units of measure, complex numerical values) or sentence level binary relation (e.g. value to unit, term to acronym).

Milling itineraries dataset for a collection of crop and wood by-products and granulometric properties of the resulting powders.

Lignocellulosic biomass represents a readily available reservoir of functional elements that can be an alternative to fossil resources for energy, chemicals and materials production. However, comminution of lignocellulosic biomass into fine particles is required to reveal its functionalities, improve its reactivity and allow practical implementation in the downstream processing steps (carrying, dosage, mixing, formulation, shaping…). The sources of lignocellulosics are diverse, with two main families, being agricultural and forest by-products. Due to plant specificity and natural variability, the itineraries of particle size reduction by dry processing, the behavior upon milling and therefore the characteristics of resulting powders can deeply vary according to various raw biomasses [[1], [2]]. This data article contains milling itineraries and granulometric properties of the resulting powders obtained from a collection of by-products from crops (flax fibers, hemp core, rice husk, wheat straw) and woods (pine wood pellets, pine bark, pine sawdust, Douglas shavings, chestnut tree sawdust) representative of currently used lignocellulosic biomass. Samples provided in the form of large pieces (hemp core, pine bark, Douglas shavings) were successively milled using different mills to progressively reduce the matter into coarse, intermediate and finally fine powders. The other samples, supplied as sufficiently small format, were directly processed in the fine powder mill. The machine characteristics and their operating parameters were recorded. The granulometric properties of the powders were analyzed with a laser granulometer and the main indicators related to the particle size distribution (PSD) are presented: (i) d10, d50 (or median diameter) and d90 which are the 10th, 50th and 90th percentiles of the cumulative volume distribution; (ii) the span, which evaluates the width of the particle size distribution; (iii) the calculated specific surface area of the powders which represents the sum of total surface exhibited by the particles per unit of gram and for some powders. The whole particle size distribution of a subset of produced powder samples are also provided for different milling times to illustrate the kinetics of particle size reduction. These data are stored in INRAE public repository and have been structured using BIOREFINERY ontology [3]. These data are also replicated in atWeb data warehouse providing additional query tools [[3], [4]].

Annotation data about multi criteria assessment methods used in the agri-food research: The french national institute for agricultural research (INRA) experience.

This data article contains annotation data characterizing Multi Criteria Assessment (MCA) Methods proposed in the agri-food sector by researchers from INRA, Europe's largest agricultural research institute (INRA, http://institut.inra.fr/en). MCA can be used to assess and compare agricultural and food systems, and support multi-actor decision making and design of innovative systems for crop production, animal production and processing of agricultural products. These data are stored in a public repository managed by INRA (https://data.inra.fr/; https://doi.org/10.15454/WB51LL).

The Next Generation of Sustainable Food Packaging to Preserve Our Environment in a Circular Economy Context.

Packaging is an essential element of response to address key challenges of sustainable food consumption on the international scene, which is clearly about minimizing the environmental footprint of packed food. An innovative sustainable packaging aims to address food waste and loss reduction by preserving food quality, as well as food safety issues by preventing food-borne diseases and food chemical contamination. Moreover, it must address the long-term crucial issue of environmentally persistent plastic waste accumulation as well as the saving of oil and food material resources. This paper reviews the major challenges that food packaging must tackle in the near future in order to enter the virtuous loop of circular bio-economy. Some solutions are proposed to address pressing international stakes in terms of food and plastic waste reduction and end-of-life issues of persistent materials. Among potential solutions, production of microbial biodegradable polymers from agro-food waste residues seems a promising route to create an innovative, more resilient, and productive waste-based food packaging economy by decoupling the food packaging industry from fossil feed stocks and permitting nutrients to return to the soil. To respond to the lack of tools and approach to properly design and adapt food packaging to food needs, mathematical simulation, based on modeling of mass transfer and reactions into food/packaging systems are promising tools. The next generation of such modeling and tools should help the food packaging sector to validate usage benefit of new packaging solutions and chose, in a fair and transparent way, the best packaging solution to contribute to the overall decrease of food losses and persistent plastic accumulation.

Consumer perception data and scientific arguments about food packaging functionalities for fresh strawberries.

This data article contains data characterizing consumer perception and scientific arguments about food packaging functionalities for fresh strawberries. These data are associated with the article "Choice of environment-friendly food packagings through argumentation systems and preferences" (see Yun et al., 2018). These data are stored in a public repository structured by an ontology. These data could be retrieved through the @Web tool, user-friendly interface to capitalize and query data (Buche et al., 2013; Guillard et al., 2017). The @Web tool is accessible online at http://pfl.grignon.inra.fr/atWeb/.

Developing data interoperability using standards: A wheat community use case.

In this article, we present a joint effort of the wheat research community, along with data and ontology experts, to develop wheat data interoperability guidelines. Interoperability is the ability of two or more systems and devices to cooperate and exchange data, and interpret that shared information. Interoperability is a growing concern to the wheat scientific community, and agriculture in general, as the need to interpret the deluge of data obtained through high-throughput technologies grows. Agreeing on common data formats, metadata, and vocabulary standards is an important step to obtain the required data interoperability level in order to add value by encouraging data sharing, and subsequently facilitate the extraction of new information from existing and new datasets. During a period of more than 18 months, the RDA Wheat Data Interoperability Working Group (WDI-WG) surveyed the wheat research community about the use of data standards, then discussed and selected a set of recommendations based on consensual criteria. The recommendations promote standards for data types identified by the wheat research community as the most important for the coming years: nucleotide sequence variants, genome annotations, phenotypes, germplasm data, gene expression experiments, and physical maps. For each of these data types, the guidelines recommend best practices in terms of use of data formats, metadata standards and ontologies. In addition to the best practices, the guidelines provide examples of tools and implementations that are likely to facilitate the adoption of the recommendations. To maximize the adoption of the recommendations, the WDI-WG used a community-driven approach that involved the wheat research community from the start, took into account their needs and practices, and provided them with a framework to keep the recommendations up to date. We also report this approach's potential to be generalizable to other (agricultural) domains.

CO2 and O2 solubility and diffusivity data in food products stored in data warehouse structured by ontology.

This data article contains values of oxygen and carbon dioxide solubility and diffusivity measured in various model and real food products. These data are stored in a public repository structured by ontology. These data can be retrieved through the @Web tool, a user-friendly interface to capitalise and query data. The @Web tool is accessible online at http://pfl.grignon.inra.fr/atWeb/.

Flexible querying of Web data to simulate bacterial growth in food.

A preliminary step in microbial risk assessment in foods is the gathering of experimental data. In the framework of the Sym'Previus project, we have designed a complete data integration system opened on the Web which allows a local database to be complemented by data extracted from the Web and annotated using a domain ontology. We focus on the Web data tables as they contain, in general, a synthesis of data published in the documents. We propose in this paper a flexible querying system using the domain ontology to scan simultaneously local and Web data, this in order to feed the predictive modeling tools available on the Sym'Previus platform. Special attention is paid on the way fuzzy annotations associated with Web data are taken into account in the querying process, which is an important and original contribution of the proposed system.

Semantic annotation of Web data applied to risk in food.

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.

Combining fuzzy querying of imprecise data and predictive microbiology using category-based reasoning for prediction of the possible microbial spoilage in foods: application to Listeria monocytogenes.

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.