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Authors: Clara Tramuta, Lucia Decastelli, Elisa Barcucci, Sandra Fragassi, Samantha Lupi, Enrico Arletti, Melissa Bizzarri, Daniela Manila Bianchi

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Introdution: Food allergies occurs, in the Western World, 2% of adults and up to 8% of children. The protection of allergic consumers is guaranted, in Eurrope, by Regulation (EU) No 1169/2011 of the European Parliament which governs the consumer's right to information and identifies 14 food allergens to be mandatory indicated on the label. Among these, mustard is a popular spice added to enhance the flavour and taste of foods. It is frequently present as an ingredient in spice blends, marinades, salad dressings, sausages, and other products. Hypersensitivity to mustard is a public health problem since the ingestion of even low amounts can trigger severe allergic reactions. In order to protect the allergic consumer, high performance methods are required for the detection of allergenic ingredients. Food safety laboratories rely on validated methods that detect hidden allergens in food to ensure the safety and health of allergic consumers. Here we present the test results for the validation and accreditation of a Real time PCR assay (RT-PCR: SPECIALfinder MC Mustard, Generon), for the detection of mustard traces in food. Materials and Methods. The method was tested on five classes of food matrices: bakery and pastry products (chocolate cookies), meats (ragù), ready-to-eat (mixed salad), dairy products (yogurt), grains, and milling products (rice and barley flour). Blank samples were spiked starting with the mustard samples (Sinapis Alba), lyophilized and stored at -18 °C, at a concentration of 1000 ppm. Serial dilutions were then prepared to a final concentration of 0.5 ppm, using the DNA extracted by ION Force FAST (Generon) from the blank samples. The Real Time PCR reaction was performed by RT-PCR SPECIALfinder MC Mustard (Generon), using CFX96 System (BioRad). Results. Real Time PCR showed a limit of detection (LOD) of 0.5 ppm in grains and milling products, ready-to-eat, meats, bakery, pastry products, and dairy products (range Ct 25-34). To determine the exclusivity parameter of the method, the ragù matrix was contaminated with Prunus dulcis (almonds), peanut (Arachis hypogaea), Glycine max (soy), Apium graveolens (celery), Allium cepa (onion), Pisum sativum (peas), Daucus carota (carrots), and Theobroma cacao (cocoa) and no cross-reactions were observed. Discussion. In terms of sensitivity, the Real Time PCR confirmed, even in complex matrix, a LOD of 0.5 ppm in five classes of food matrices tested; these values are compatible with the current regulatory situation that does not consider, at international level, to establish a quantitative criterion for the allergen considered in this study. The Real Time PCR SPECIALfinder kit for the detection of mustard proved to be easy to use and particularly appreciated for the rapid response times considering that the amplification and detection phase has a duration of less than 50 minutes. Method accuracy was rated satisfactory for sensitivity (100%) and specificity (100%) and was fully validated and accreditated. It was found adequate for the needs of the laboratory as it met the purpose for which it was applied. This study was funded in part within a project of the Italian Ministry of Health (IZS PLV 02/19 RC).

Keywords: allergens, food, mustard, real time PCR

Procedia PDF Downloads 134

Authors: Tatiana Rizova

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The migrant crisis, which peaked in 2015-2016, posed an unprecedented challenge to the European Union’s (EU) newest member states, including Bulgaria and Hungary. Their governments had to formulate sound migration policies with expediency and sensitivity to the needs of millions of people fleeing violent conflicts in the Middle East and failed states in North Africa. Political leaders in post-communist countries had to carefully coordinate with other EU member states on joint policies and solutions while minimizing the risk of alienating their increasingly anti-migrant domestic constituents. Post-communist member states’ governments chose distinct policy responses to the crisis, which were dictated by factors such as their governments’ partisan stances on migration, their views of the European Union, and the decision to frame the crisis as a security or a humanitarian issue. This paper explores how two Bulgarian governments (Boyko Borisov’s second and third government formed during the 43rd and 44th Bulgarian National Assembly, respectively) navigated the processes of EU migration policy making and managing the expectations of their electorates. Based on a comparative analysis of refugee policies in Bulgaria and Hungary during the height of the crisis (2015-2016) and a temporal analysis of refugee policies in Bulgaria (2015-2018), the paper advances the following conclusions. Drawing on insights of the Copenhagen school of security studies, the paper argues that cultural concerns dominated domestic debates in both Bulgaria and Hungary; both governments framed the issue predominantly as a matter of security rather than humanitarian disaster. Regardless of the similarities in issue framing, however, the two governments sought different paths of tackling the crisis. While the Bulgarian government demonstrated its willingness to comply with EU decisions (such as the proposal for mandatory quotas for refugee relocation), the Hungarian government defied EU directives and became a leading voice of dissent inside the EU. The current Bulgarian government (April 2017 - present) appears to be committed to complying with EU decisions and accepts the strategy of EU burden-sharing, while the Hungarian government has continually snubbed the EU’s appeals for cooperation despite the risk of hefty financial penalties. Hungary’s refugee policies have been influenced by the parliamentary representation of the far right-wing party Movement for a Better Hungary (Jobbik), which has encouraged the majority party (FIDESZ) to adopt harsher anti-migrant rhetoric and more hostile policies toward refugees. Bulgaria’s current government is a coalition of the center-right Citizens for a European Development of Bulgaria (GERB) and its far right-wing junior partners – the United Patriots (comprised of three nationalist political parties). The parliamentary presence of Jobbik in Hungary’s parliament has magnified the anti-migrant stance, rhetoric, and policies of Mr. Orbán’s Civic Alliance; we have yet to observe a substantial increase in the anti-migrant rhetoric and policies in Bulgaria’s case. Analyzing responses to the migrant/refugee crisis is a critical opportunity to understand how issues of cultural identity and belonging, inclusion and exclusion, regional integration and disintegration are debated and molded into policy in Europe’s youngest member states in the broader EU context.

Keywords: Copenhagen School, migrant crisis, refugees, security

Procedia PDF Downloads 99

Authors: Diogo José Morgado Rebelo, Francisco António Carneiro Pacheco de Andrade, Paulo Jorge Freitas de Oliveira Novais

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Not everything in AI-power consumer credit scoring turns out to be a wonder. When using AI in Creditworthiness Assessment (CWA), opacity and unfairness ‘sins’ must be considered to the task be deemed Responsible. AI software is not always 100% accurate, which can lead to misclassification. Discrimination of some groups can be exponentiated. A hetero personalized identity can be imposed on the individual(s) affected. Also, autonomous CWA sometimes lacks transparency when using black box models. However, for this intended purpose, human analysts ‘on-the-loop’ might not be the best remedy consumers are looking for in credit. This study seeks to explore the legality of implementing a Multi-Agent System (MAS) framework in consumer CWA to ensure compliance with the regulation outlined in Article 14(4) of the Proposal for an Artificial Intelligence Act (AIA), dated 21 April 2021 (as per the last corrigendum by the European Parliament on 19 April 2024), Especially with the adoption of Art. 18(8)(9) of the EU Directive 2023/2225, of 18 October, which will go into effect on 20 November 2026, there should be more emphasis on the need for hybrid oversight in AI-driven scoring to ensure fairness and transparency. In fact, the range of EU regulations on AI-based consumer credit will soon impact the AI lending industry locally and globally, as shown by the broad territorial scope of AIA’s Art. 2. Consequently, engineering the law of consumer’s CWA is imperative. Generally, the proposed MAS framework consists of several layers arranged in a specific sequence, as follows: firstly, the Data Layer gathers legitimate predictor sets from traditional sources; then, the Decision Support System Layer, whose Neural Network model is trained using k-fold Cross Validation, provides recommendations based on the feeder data; the eXplainability (XAI) multi-structure comprises Three-Step-Agents; and, lastly, the Oversight Layer has a 'Bottom Stop' for analysts to intervene in a timely manner. From the analysis, one can assure a vital component of this software is the XAY layer. It appears as a transparent curtain covering the AI’s decision-making process, enabling comprehension, reflection, and further feasible oversight. Local Interpretable Model-agnostic Explanations (LIME) might act as a pillar by offering counterfactual insights. SHapley Additive exPlanation (SHAP), another agent in the XAI layer, could address potential discrimination issues, identifying the contribution of each feature to the prediction. Alternatively, for thin or no file consumers, the Suggestion Agent can promote financial inclusion. It uses lawful alternative sources such as the share of wallet, among others, to search for more advantageous solutions to incomplete evaluation appraisals based on genetic programming. Overall, this research aspires to bring the concept of Machine-Centered Anthropocentrism to the table of EU policymaking. It acknowledges that, when put into service, credit analysts no longer exert full control over the data-driven entities programmers have given ‘birth’ to. With similar explanatory agents under supervision, AI itself can become self-accountable, prioritizing human concerns and values. AI decisions should not be vilified inherently. The issue lies in how they are integrated into decision-making and whether they align with non-discrimination principles and transparency rules.

Keywords: creditworthiness assessment, hybrid oversight, machine-centered anthropocentrism, EU policymaking

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