SSD2 and FoodEx2 compliant real‐time registration and classification of food sampling data ‐ Improving Data Quality for Risk Assessment (IDRisk). Issue 10 (13th October 2022)

• Record Type: Journal Article
• Authors: Nabais, Pedro; Carmona, Paulo; Monteiro, Sarogini; Melo de Vasconcelos, Filipa; Tomé, Sidney; Ravasco, Francisco; Brazão, Roberto; Dias, Maria da Graça; Sokolic, Darja; Basic, Sandra
• Is Part Of: EFSA supporting publications. Volume 19:Issue 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-13
• Subjects: Food -- Europe -- Safety measures -- Periodicals; Food -- Safety measures; Europe; Periodicals; Periodicals; 363.192094
• DOI: 10.2903/sp.efsa.2022.EN-7633
• Languages: English
• ISSNs: 2397-8325

Abstract

Abstract: The present report describes the work done under the IDRisk project (Improving Data quality for RISK assessment), within the grant agreement GP/EFSA/ENCO/2018/03, from the sign in on 12/12/2018. The main goal of this project was to improve quality of raw occurrence data for risk assessment by reducing error, incrementing completeness and timeliness both in data fields and food classification, and simultaneously reducing the workload and time‐consuming manual tasks and therefore allowing scientists more time for data analysis and for performing risk assessment. The improvements are reflected on the strengthening of food safety risk assessment capacity of the countries involved and contributing to a better evaluation on risks associated with the food chain by EFSA. The objectives proposed and results achieved by this project presents a solution to improve data collection, management and interoperability, facilitating data exchange, with robust methodologies and tools, allowing the competent authorities to substantially enhance their own National Data Management Systems (NDMS). The proposed solution consists of the implementation of a system capable of real‐time sample data collection, based on preparatory digital forms, as well as an automatic approach to FoodEx2 classification of food samples using the existing knowledge and NDMS's databases. The aim of such system is to automate the whole execution of the official control plans and data transmission to EFSA, while mitigating the errors that normally accumulate throughout the process as a result of data manually handled by several people and of the consequent amount of inaccurate information that is produced. It was expected that the proposed solution could increase the data quality, through robust sample collection that could be monitored online and in real time, reducing the risk of misidentification/management. This document also describes the challenges encountered during the implementation of the project, and provides a general analysis on its limitations and potential future developments.