Maxime Lefrançois
email: maxime.lefrancois@inria.fr|fabien.gandon@inria.fr

Fabien Gandon

ULiS: An Expert System on Linguistics to Support Multilingual Management of Interlingual Semantic Web Knowledge bases

Keywords: Semantic Web, Explanatory Combinatorial Lexicology, Interlingual Lexical Ontology, Semantic decomposition, Interlingual Lexical Primitives, Meaning Text Theory

We are interested in bridging the world of natural language and the world of the semantic web in particular to support multilingual access to the web of data. In this paper we introduce the ULiS project, that aims at designing a pivot-based NLP technique called Universal Linguistic System, 100% using the semantic web formalisms, and being compliant with the Meaning-Text theory. Through the ULiS, a user could interact with an interlingual knowledge base (IKB) in controlled natural language. Linguistic resources themselves are part of a specific IKB: The Universal Lexical Knowledge base (ULK), so that actors may enhance their controlled natural language, through requests in controlled natural language. We describe a basic interaction scenario at the system level, and provide an overview of the architecture of ULiS. We then introduce the core of the ULiS: the interlingual lexical ontology (ILexicOn), in which each interlingual lexical unit class (ILU c ) supports the projection of its semantic decomposition on itself. We validate our model with a standalone ILexicOn, and introduce and explain a concise human-readable notation for it.

Introduction

In this paper we introduce and illustrate the recently begun ULiS project, which aims at redesigning a pivot-based NLP technique, 100% using the semantic web formalisms, and being compliant with the Meaning-Text theory. ULiS stands for Universal Linguistic System, and is a system through which multiple actors could interact with interlingual semantic web knowledge bases in multiple controlled (i.e., restricted and formal) natural languages. Each controlled natural language (dictionary, grammar rules) would be described in a part of a universal linguistic knowledge base (ULK). Besides this, the ULK consists in one specific interlingual knowledge base. Actors could then enhance their controlled natural language through different actions in controlled natural language (e.g., create, describe, modify, merge, or delete lexical units in the dictionaries and grammar rules; connect situational lexical units to interlingual lexical units; add linguistic attributes with their associated rules, etc.).

The aim of this paper is to overview our proposal for the architecture of ULiS, and to introduce and validate the cornerstone of the universal linguistic knowledge base: the interlingual lexical ontology (ILexicOn).

Related Work

The Meaning-Text Theory (MTT). The MTT is a theoretical linguistic framework for the construction of models of natural language. As such, its goal is to write systems of explicit rules that express the correspondence between meanings and texts (or sounds) in various languages [START_REF] Kahane | The Meaning-Text Theory, Dependency and Valency[END_REF]. Seven different levels of linguistic representation are supposed for each set of synonymous utterances: a semantic representation that is a network; the deep and surface syntactic representations (DSynR and SSynR) that are trees; the deep and surface morphological representations (DMorphR and SMorphR) that are lists of annotated tokens; and the the deep and surface phonological representations (DPhonR and SPhonR) that are also lists of annotated tokens. (Mel'čuk, 1998). Thus, twelve modules containing transformation rules are used to transcribe representations of a level into representations of an adjacent level. The main constituent of the MTT is the dictionary model where lexical units are described, which is called the Explanatory Combinatorial Dictionary (ECD), and has been the object of many works on lexical functions, e.g., [START_REF] Mel'čuk | Introduction à la lexicologie explicative et combinatoire[END_REF].

Lexical ontologies and meaning representation languages. Lexical ontologies are ontologies of lexicalized concepts, widely used to model lexical semantics. Some have broad coverage but shallow treatment (i.e., with no or little axiomatization) such as Princeton WordNet (e.g., [START_REF] Miller | Introduction to wordnet: An on-line lexical database[END_REF], and some have small coverage but are highly axiomatized such as FrameNet [START_REF] Baker | The berkeley framenet project[END_REF]. They use different theories of lexical semantics but most of them do not describe phrasemes nor lexical collocations. The French Lexical Network (Lux-Pogodalla & Polguère, 2011) is a growing ECD-compliant lexical resource, but it does not use the semantic web formalisms, and the definitions of the lexical units are not fully formalized.

On the other hand, the Universal Networking Language (UNL) is a meaning representation language, originally designed for pivot techniques Machine Translation. Its dictionary is an interlingual lexical ontology based on so-called Universal Words ++, but the lack of argument frames and lexical functions in the UNL dictionary was pointed out in (Bogulsavsky, 2002;Bogulsavsky, 2005). This is when the idea of an ECD-compliant interlingual lexical ontology was first mentioned. After the semantic web formalisms were introduced at the W3C, an attempt to port the UNL to semantic web formalisms was the topic of the W3C Common Web Language Incubator Group (XGR-CWL, 2008), but no improvement was made to the lexical ontology.

SPARQL Inferencing Notation (SPIN). Grammar rules are not part of the Common

Web Language (CWL) framework, in fact, the construction of grammar modules may be done in any programming language. Knublauch et. al. (2011) introduced SPIN: an RDFS schema to represent SPARQL rules and constraints.

Positioning of the ULiS project.

The lexical resource we propose to develop is an interlingual lexical ontology coupled with a situational (i.e., a generalization of language-specific) lexical ontology, both using semantic web formalisms, and that together form an ECD-compliant dictionary. Benefits of using semantic web formalisms are high as it enables us to construct an axiomatized graph-representation of a lexical ontology, with validation and inference rules. Using SPIN, we propose to include transformation rules directly in an RDF format, on top of the ECD-compliant lexical ontologies, thus obtaining an expert system on linguistics.

The ULiS model is somehow similar to the FunGramKB (Periñán-Pascual & Arcas-Túnez, 2010) which is a lexico-conceptual knowledge base for NLP. However, the two projects have different inspiring influence. We choose to comply with the Meaning-Text theory, which gives a thorough understanding of lexical functions that are ubiquitous in every natural language. We also choose to describe the whole ULiS with the semantic web formalisms. This thus potentially enables the enhancement of the system itself through controlled natural language interactions.

Basic Interaction Scenarios with the ULiS

The three basic scenarios of ULiS are illustrated on Figure 1 below. An actor in a situation c inputs some utterance (e.g., in English: "Who killed Mary?") that is first transformed into an RDF situational representation, which undergoes different language-specific process, and which is finally transformed into a CWL-like interlingual representation.

Machine translation. At this stage, depending on the context, the interlingual representation of the utterance may be translated into another utterance in situation d (e.g., in the French situation: "Qui a tué Mary?") through a situational representation (Out-put1 TEXT on Figure 1.

Management of Interlingual Knowledge

Bases. Another possibility is that the interlingual representation of the utterance is transformed in a SPARQL request that is applied on an interlingual knowledge base (IKB), which eventually produces an RDF output (e.g., ex:John01). This RDF output is then first transformed into an interlingual representation, then into a situational representation and finally into an output utterance: Output2 TEXT on Figure 1 (e.g., "John killed Mary").

Management of the Universal Linguistic Knowledge base. Finally, the third scenario is the human-computing scenario: the SPARQL request is applied on the Universal Linguistic Knowledge base, which is the Interlingual Knowledge Base where the whole ULiS is described. Human actors may thus enhance the controlled natural languages through actions stated in controlled natural language. Thus the interlingual representation format acts as a pivot not only for natural languages, but any interlingual representation may be translated into a SPARQL request, and any RDF graph may be translated to an interlingual representation.

4

The ULiS components

Overview

Figure 2 below illustrates the ULiS, with its three different layers:

The second row represents interlingual layer (section 4.2), with a meta-ontology that describes the interlingual lexical ontology (ILexicOn): the cornerstone of the whole Universal Linguistic Knowledge base. The ILexicOn enables inference in interlingual semantic representations (ISemRs, on the right).

The first row represents the interlingual knowledge base (IKB) layer, with facts (on the right) and an ontology or thesaurus (on the left), augmented with anchors and transformation rules (section 4.4), that enable the transformation of facts into ISemRs, and vice versa. The IKB enables situation-independent inference on utterance representation.

The third row represents the situational layer (section 4.3), with a meta-ontology that describes the situational lexical ontology (SLexicOn), that itself enables situation-dependent linguistic inference on utterances' situation-dependent representations (Situational representations, SRs, on the right). Situation-annotated links and transformation rules define transformation of utterances among SRs.

IKB

RDF SPARQL RDF + X RDF X RDF
The RDF-World

RDF Output SPARQL Request

RDF situational representations

RDF interlingual representations

Input TEXT

IR RDF SR c RDF Output2 TEXT SR d RDF IR RDF Output1 TEXT SR d RDF Fig. 2.
Overview of the architecture of the ULiS. From top to bottom: the interlingual layer, the interlingual layer, the situational layer.

From left to right: meta-ontologies; ontologies; facts and different representations.

Architecture in the interlingual layer

The interlingual layer of ULiS is divided in three components:

The meta-ontology. The interlingual lexical meta-ontology (ILexiMOn) is the schema that the ILexicOn must satisfy to be compliant with the pure semantic features of the Explanatory Combinatorial Dictionary (ECD). It defines meta-classes, uses RDFS and some of OWL full's axioms, and contains ad hoc SPIN validation and inference rules for the ILexicOn and the interlingual semantic representations (ISemRs).

The ontology. The interlingual lexical ontology (ILexicOn) is the interlingual dictionary where interlingual lexical unit classes (ILU c s) are formally defined as instances of the ILexicalUnit meta-class from the ILexiMOn. The ILexicOn contains all the pure semantic features of the Explanatory Combinatorial Dictionary (ECD). Any concept expressible in a natural language or a jargon is defined in the ILexicOn that contains:

• The formal definitions of the ILU c s (described in section 5.2)

• The definitions of interlingual attribute classes (IAtts) (e.g., plural, future, 1 st person, indefinite, etc.);

X RDF

Other features of the ECD

• The definitions of the interlingual semantic relations (ISemRels), that are used in the formal definitions of the ILU c s and to construct interlingual semantic representations (ISemRs); • Interlingual lexical functions: every purely-semantic lexical links such as synonymy, and purely-semantic generic constructions such as the lexical function Centr(X), i.e., ( the center of X ) , or Fin(X), i.e., ( stop being X ) .

The interlingual semantic representations. ISemRs are RDF graphs with nodes being interlingual lexical unit instances (ILU i s), and arcs being ISemRels. ILU i s may also be instances of IAtts. Arcs are interlingual semantic relations (ISemRels).

4.3

To and from Natural Language facts Situations. Interlingual-based lexical resources consider connecting language specific dictionaries to some interlingual dictionary. We generalize this by using situations (i.e., the situations of understanding and use of some linguistic element).

The situation of a linguistic element is part of the pragmatics of its use: it represents not only the language used (e.g., EN, FR), but also sociolectal marks (e.g., biologists, architects, official, slang, reverential), topolectal marks (e.g., U.S., Canada), chronolectal marks (e.g., old, neologic), and even individual marks (e.g., a particular group of people). The intersection of situations is also a situation (EN-U.S.slang), and so is the union of situations (FR-Canada OR FR-France-old).

Architecture of the situational layer. This architecture purposefully mirrors the interlingual layer:

A situational lexical meta-ontology (SLexiMOn) describes the SLexicOn, A situational lexical ontology (SLexicOn), contains all non-purely semantic features of the ECD. A non-exhaustive list is the following: • Definitions of situational lexical unit classes, called SLU c s, by means of a link to an ILU c , which is annotated by a specific situation. • Situational lexical functions such as Instr(X), i.e., the preposition that governs the keyword X and means: ( by means of ) . • Situational attribute classes (e.g., invariable English nouns, French 1 st verb group, German dative, etc.), their associated situations and rules. • Situational relations: relations that link two instances of the SLU c s, thus defining the dependency syntax of the utterance, or the order of the words in an utterance. Situational representations (SRs). The data consist of situational representations (SRs): RDF graphs having situational lexical unit instances (SLU i s) as nodes and situational relations as arcs. A SR thus represents the different representations of the Meaning-Text theory.

Transformation rules. Contrary to the Common Web Language (CWL), where no grammar rules representation is proposed, we plan to introduce transformation rules in the SLexiMOn. Transformation rules form a subclass of the SPIN rules and are attached to a SLU c to define a correspondence between a generic pattern from a representation level, to another pattern at a deeper or to a higher representation level. Thus, each situation may define its own analysis and production grammar, both made of six sets of transformation rules.

Transformation rules may be sorted according to their level of genericity: transformation rules that are attached to ISemRels, or to IAtts, are less specific than rules that may be triggered only when a complex ISemR patterns is met; also, rules that may be triggered in generic situations are less specific than those that may only be triggered in more specific situations. The important point is that a rule must be triggered if and only if there is not a more specific rule that can be triggered instead. This implies that an algorithm different from the simple forward-chaining algorithm must be proposed. It will be very important to optimize the application of such an algorithm with a whole set of rules. We therefore plan to construct a Rete network [START_REF] Forgy | Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem[END_REF] on top of each set of transformation rules, which is eased by the SPIN framework as each rule is modeled as an RDF graph.

Finally, a set of generic transformation rules must be designed to ensure that for each situation, every SR is transformable to an ISemR, and that every ISemR is transformable to a SR. When a new situation is introduced (e.g., a new language), this criterion is a priori not met. This is the reason why we suggest the introduction of the universal situation, and transformation rules that produce Notation3-like output. We claim that a small set of rules will suffice to produce and analyze simple controlled natural languages.

4.4

To and from Interlingual Knowledge Bases facts.

Interlingual knowledge bases. The main criterion that an interlingual knowledge base must meet is that any RDF graph inside it must be transformable into an interlingual semantic representation (ISemR). We thus propose to form interlingual knowledge bases by augmenting classic knowledge bases with anchors and transformation rules:

• An anchor is a triple that links an RDF resource to an ILU c . For instance, the RDF resource rdfs:Class will be anchored to a specific ILU c ilexicon:RdfClass that formally defines the concept of an RDF class, and that is itself linked to an English SLU c that is a pluralizable noun realized by the string "class"; • The transformation rules are stored in the interlingual knowledge base and form two separated sets of rules: one for producing RDF from an ISemR, the other for producing an ISemR from RDF. Here again, transformation rules may be sorted according to their level of genericity, and the most generic rules must be inhibited when more specific ones can be triggered.

Augmenting classic semantic web formalisms. The output of an ISemR must be a valid SPARQL request, and the output of any RDF graph must be a valid ISemR. This criterion will be satisfied by the introduction of different anchors and generic trans-formation rules in the classic semantic web vocabularies: RDF, then RDFS, OWL and SPIN, and finally SKOS. Thus an RDF class that has no anchor, e.g., foaf:Person, has a correspondence with an ISemR that itself has a correspondence to the textual representation for the EN situation: "The RDF class foaf:Person".

5

Modeling Choices in the Interlingual Layer

Overview

Fig. 3. The three compoents of the interlingual layer, with details of the whole core-ILexiMOn that we introduced, and overview of the light standalone ILexicOn and the data.

Figure 3 illustrates the architecture of our work, with its integration in the semantic web formalisms. To validate our approach, we designed a light core-ILexiMOn 1 , a light standalone ILexicOn 2 , and simple ISemRs 3 .

A is a subClass of B

A B From top to bottom: 1) the semantic web formalisms, with a few OWL classes and properties that are useful for our work; 2) the detailed core-ILexiMOn; 3) an overview of the light standalone ILexicOn; and 4) an overview of data from the interlingual data component. Notice that: i) ILU i s from the data are instances of ILU c s described in the ILexicOn, that are themselves instances of the ILexicalUnit meta-classes described in the ILexiMOn; and ii) properties used to link two resources in a layer are described in an upper layer. Figure 4 above concisely describes the light standalone ILexicOn using a notation inspired from Sowa's conceptual graphs [START_REF] Sowa | Conceptual structures: information processing in mind and machine, System programming series[END_REF]. Each rectangle represents the definition of the ILUc that is written in its top-left corner. The lexicographic definition of lexical units

A is an instance of B

In the ILexicOn is propose a novel approach to the lexicographic definition of an ILU c that consists in projecting the minimal semantic decomposition of the ILU c on the ILU c using Conceptual Participant slots (ConP-slot): the implicit semantic link that exists between an ILU c L and one of the participants of the minimal semantic decomposition of L (Mel'čuk, 2004 ; [START_REF] Lefrançois | ILexicOn: toward an ECD-compliant interlingual lexical ontology described with semantic web formalisms[END_REF].

Interlingual lexical units (classes and instances): ILU c s are instances of the ILexi-calUnit meta-class from the ILexiMOn (c.f., Figure 3). They are defined in the ILex-icOn (c.f., Figure 4, e.g., Entity, Person, State, Alive, Event, Cause). In our notation, symbol < represents the rdfs:subClassOf axiom that may be used to state inheritance between ILU c s (e.g., Person<Entity, Alive<State, Cause<Event). For instance, The ILU c Person is a sub-class of the ILU c class Entity, and the ILU c Entity is the parent of the ILU c Person. Complex ILU c s may be constructed through owl:intersectionOf and owl:unionOf. Finally, interlingual lexical unit instances (ILU i s) are instances of ILU c s and are used in the data component as nodes of the interlingual semantic representations.

Interlingual semantic relations: ISemRels are instances of the ISemRelation metaclass of the ILexiMOn, and thus instances of owl:ObjectProperties. They are introduced in the LexicOn and used in the data to link ILU i s (see Figure 3&4). In our notation, symbol < represents the rdfs:subPropertyOf axiom that may be used to define a new ISemRel as being a sub-ISemRel of one or more ISemRels (e.g., hasExperienc-er<hasAgent, hasKilled). Symbol / represents the owl:propertyChainAxiom axiom that may also be used to state that a ISemRel is a super-ISemRel of the composition of two or more ISemRels (e.g., hasState/hasEntity<hasDead). These two axioms may be combined to define complex ISemRels (e.g., hasEvent/hasTime<hasKillTime<hasTime).

Interlingual lexical primitives: An ILU c L is a ILP c if and only if it derives from no other ILU c but has at least one ConP-slot. Non-lexical primitives then derive from one or more lexical primitives following the ConP-slot inheritance and introduction principle:

An ILU c L inherits from its parents' ConP-slots, and may also introduce new ConP-slots; One may thus consider only participants that are necessary and sufficient to the minimal projection of L. ILP c s are defined as instances of the ILexicalPrimitive metaclass from the ILexiMOn (c.f., Figure 3). An ILP c must be linked through: i) the onI-SemanticRelation property to exactly one ISemanticRelation; ii) the allValuesFrom property to exactly one ILexicalUnit; and iii) the isObligatory property to exactly one xsd:boolean.

Conceptual participant slots: In Figure 4, each line with an arrow in the definition of an ILU c represents a conceptual participant slot (ConP-slot) that restricts the use of a specific ISemRel for this ILU c and its descendants. Actually, such a line means that the defined ILU c is a sub-class of an ILP c . For instance, the line State-(hasEntity)→1.Entity states that any instance of the State class is linked exactly once through the hasEntity relation to an instance of the Entity class. Let us focus on the notation used on Figure 4: • Inheritance. ConP-slots may be newly defined (black font, e.g., State-(hasEntity)→1.Entity), fully inherited (grey font, e.g., Relation<State-(hasEntity)→1.Entity) or partially inherited (grey font for the inherited part, e.g., Alive<State-(hasEntity)→1.Person). The ILU c on the right hand side of the line is called the current range of the ConP-slot.

• Obligatory vs. optional. A ConP-slot may be obligatory (symbol 1, e.g., Alive<State-(hasEntity)→1.Person) or optional (symbol ?, e.g., Kill<Cause-(hasBeneficiary)→?.Person). When an optional ConP-slot is inherited, it may be restricted to being obligatory.

• Domain/range of the ISemRel. As an ISemRel is an rdf:Property, it may restrict its domain and its range i.e., what ILU c the subject (resp. the object) of a triple that involves this ISemRel does belong to. When an ISemRel is underlined, it means that its domain is set to the defined ILU c , and that its range is set to the current ILU c range of the ConP-slot. (e.g., State-(hasEntity)→1.Entity).

• ISemRel subproperty and composition axioms. As we stated in section 4.2.2, complex ISemRel may be defined thanks to inheritance and composition. There are benefits in using such ISemRel to qualify a new ConP-slot. In fact, this combined with the maximum cardinality of ConP-slots restricted to 1, imposes the equality of ILU i in the data. We illustrate these inferable equalities by dotted lines on the right of ConP-slots.

The ISemRel inheritance and composition is what enables the projection not only of trees, but also graphs, onto one node. Thus, each ILU c described in the ILexicOn contains the projection of its semantic decomposition graph. We illustrated this on Figure 4 with complex ILU c such as ilexicon:Suicide (the killer is the person killed) and ilexicon:Infanticide (the killer is the parent of the person killed).

Conclusions and discussions

We introduced a universal linguistic system (ULiS) through which multiple actors could interact with an interlingual knowledge base (IKB) in controlled natural language. We explained an interaction scenario with ULiS, which can serve for machine translation and for multilingual management of interlingual knowledge bases. We then gave an overview of the layers ULiS is made of: the interlingual layer; the situational layer; and an interlingual knowledge base.

The main novelty of our proposal is that the characteristics of each controlled natural language are stored in a specific interlingual knowledge base. Thus, actors could enhance their controlled natural language through requests expressed in controlled natural language.

We introduced and illustrated a novel approach to formally define ILU c s: we make ILU c s support a projection of their semantic decomposition. We introduced a humanreadable notation to represent ILexicOn, and we used this notation to validate our approach with a simple standalone ILexicOn. We thus showed that simple and complex ILU c s may be formally defined with our novel approach.

We are currently working on the formalization of lexical functions in the ILexicOn and of the SLexicOn, and we are to partly populate our lexical resources with lexical units from other lexical resources such as the French Lexical Network. We finally plan to validate our results by the design and the experimentation of a web-based prototype with a simple interlingual knowledge base (e.g., the "interlingualaugmented" wine ontology), and a few situations based on English and French.

Fig. 1 .

 1 Fig. 1. ULiS: The basic interaction scenario with an interlingual knowledge base.

(

  ex:John01,ikb:kill,ex:Mary01) InterlingualKnowledgeBase (ikb:kill,rdfs:range,ikb:Person) (ikb:kill,rdfs:domain,ikb:Person) IKB = KB + anchors + transformation rules + Links + Transformation rules

Fig. 4 .

 4 Fig. 4. The light standalone ILexicOn and one ISemR described with our notation.

  hasEntity)→1.Entity Relation<State -(hasEntity)→1.Entity -(hasObject)→1.Entity Parent<Relation -(hasEntity)→1.Person -(hasObject)→1.Person Alive<State -(hasEntity)→1.Person Event -(hasTime)→1.Time Cause<Event -(hasTime)→1.Time -(hasAgent)→1.Person -(hasEvent)→1.Event End<Event -(hasTime)→1.Time -(hasState)→1.State Die<End -(hasTime)→1.Time -(hasState)→1.Alive -(hasEntity)1.Person -(hasState/hasEntity<hasDead)→1.Person Kill<Cause -(hasTime)→1.Time -(hasAgent)→1.Person -(hasEvent)→1.Die -(hasTime)→1.Time -(hasDead)→1.Person -(hasEvent/hasDead<hasKilled)→1.Person -(hasEvent/hasTime<hasKillTime<hasTime)→1.Time -(hasBeneficiary)→?.Person Suicide<Kill -(hasKillTime)→1.Time -(hasBeneficiary)→?.Person -(hasAgent)→1.Person -(hasKilled)→1.Person -(hasExperiencer<hasAgent, hasKilled)→1.Person Infanticide<Kill -(hasKillTime)→1.Time -(hasBeneficiary)→?.Person -(hasAgent)→1.Person -(hasKilled)→1.Person -(hasParent)→1.Parent -(hasEntity)→1.Person -(hasObject)→1.Person -(hasParent/hasObject<hasKillerParent<hasAgent)→1.Person -(hasParent/hasEntity<hasKilledChild<hasKilled)→1.Person John kills Mary: Kill: k01 -(hasAgent)→Person: John01 -(hasKilled)→Person: Mary01


Biagio ; Fallico
email: bfallico@unict.it

E Chiappara

E Arena

G Ballistreri

Assessment of the exposure to Allura red colour from the consumption of 1 red juice-based and red soft drinks in Italy 2 3

Keywords: Chromatography -HPLC, Exposure -prob modelling, Exposure assessment Additives/Contaminants: Colours Food Types: Beverages, Fruit juices Allura red, beverages, risk analysis, EDI, ADI, probabilistic approach, @risk

Assessment of the exposure to Allura red colour from the consumption of red juice-based and red soft drinks in Italy

Food colourants, both naturally derived and synthetic, are widely used by the food industry. 8

Allura red (2-hydroxy-1-(2-methoxy, 5-methyl, 4-sulphonatophenylazo)-naphthalene-6-9 sulphonate) disodium salt, also known as EU additive 129 (E129) or FD&C Red No. 40 10 (USA), is one of the most widely used synthetic diazo colourants. Directive 94/36/EC on 11 colours for use in foodstuffs fixes a limit of 100 mg/l for E129 in non-alcoholic flavoured 12 drinks. Safety aspects have been assessed and reported by the Joint FAO/WHO Expert 13 Committee on Food Additives [START_REF] Jecfa | FAS 6/NMRS 54A-JECFA 18. Toxicological evaluation of some food 9 colours, enzymes, flavour enhancers, thickening agents, and certain other food additives[END_REF](JECFA, , 1980[START_REF] Jecfa | FAS 16-JECFA 25. Toxicological evaluation of certain food additives[END_REF], as well as by the EU Scientific 14 Committee for Food [START_REF] Scf | Reports of the Scientific Committee for Food (1 st series)[END_REF][START_REF] Scf | Reports of the Scientific Committee for Food (14 th series)[END_REF][START_REF] Scf | Reports of the Scientific Committee for Food (21 th series)[END_REF]). Both Committees have established an 15 acceptable daily intake (ADI) of 0-7 mg/kg of body weight/day (mg/kg bw/day). The studies 16 on which the JECFA and SCF assessments were based showed a very low acute toxicity of 17 Allura red as measured in different species of animals (LD50: 10,000 mg/kg bw for rats and 18 rabbits and 5000 mg/kg bw for dogs (Weir, 1965a[START_REF] Weir | JECFA 1980. Acute oral administration -dogs, five experimental non-20 toxic red colors[END_REF][START_REF] Weir | Re d Z-4576. 23 Unpublished report 165-119[END_REF]. Recently, Sasaki et al. 19 (2002) tested 39 food additives and defined a LD50 for Allura red of > 2000 mg/kg. Studies 20 on sub-chronic and chronic toxicity, as well on carcinogenity [START_REF] Serota | Lifetime 16 carcinogenic study in the albino rat[END_REF]; Borzelleca 21 et al. 1989Borzelleca 21 et al. , 1991)), showed that the incidence of tumours or lesions in control and treated 22 animals were very similar. Details concerning the safety aspects of Allura Red can be found 23 in the JECFA (1980,1981) and WHO reports (2009), respectively. 24 However, after the publication of the book Why your Child is Hyperactive? (Feingold,25 widely debated. [START_REF] Vorhees | Developmental 12 toxicity and psychotoxicity of FD & C red dye No 40 (Allura Red AC) in rats[END_REF] showed that Allura red in the diet significantly reduced 3 reproductive success and organ development, and that it modified the behaviour of young 4 rats, although it was not possible to confirm the Feingold hypothesis. Generally speaking, 5 there is a strong feeling that a gap in the data exists regarding the assessment of exposure of 6 infants and children to Allura red. Often, specific toxicity tests are asked to include data on 7 the relative sensitivity of this sub-population [START_REF] Reed | Assessing risks to infants and children[END_REF]. Studies concerning different age 8 susceptibilities to carcinogenesis [START_REF] Hattis | Age-related differences in 1 susceptibility to carcinogenesis: a quantitative analysis of empirical animal bioassay 2 data[END_REF], as well as studies on paediatric 9 pharmacokinetics [START_REF] Ginsberg | Pediatric pharmacokinetic data: 22 implications for environmental risk assessment for children[END_REF], have been carried out. Recently, in vivo Comet 10 studies [START_REF] Tsuda | DNA 9 damage induced by red food dyes orally administered to pregnant and make mice[END_REF][START_REF] Sasaki | The comet assay with 8 mouse organs: results with 39 currently used 8 food additives[END_REF] excluded a general toxicity of Allura red, but 11 highlighted specific DNA damage in the colon and in the stomach at the very low Allura red 12 levels of 10 mg/kg and 100 mg/kg, respectively. Moreover, a new study reported that 13 exposure to two mixtures of four synthetic colours (mixture A: E102, E124, E110, E122; 14 mixture B: E110, E122, E104, E129) plus a sodium benzoate preservative in the diet resulted 15 in increased hyperactivity in 3-year-old and 8-9-year-old children in the general population 16 (McCann et al., 2007). The colours, and generally the additives, may have not only the above 17 mentioned effects, but can increase intolerance and allergy in children [START_REF] Bourrier | Intolérances et allergies aux colourants et additifs[END_REF]. Other 18 studies have been carried out to assess the daily intake of some food colourants in several [START_REF] Weir | JECFA 1980. Acute oral administration -dogs, five experimental non-20 toxic red colors[END_REF] foodstuffs and non-alcoholic beverages. One study designed a screening method to identity 20 food colourants used in non-alcoholic beverages, which could potentially be consumed at 21 levels exceeding the ADIs, by using a tiered approach [START_REF] Tennant | Screening potential intakes of colour additives used in non-alcoholic 4 beverages[END_REF]. Other studies 22 concluded that children were exposed to levels of some food colourants, which exceeded the 23 ADIs [START_REF] Rao | Exposure assessment to 19 synthetic food colours of a selected population in Hyderabad, India[END_REF][START_REF] Husain | Estimates of dietary exposure of children to artificial food colours in Kuwait. 5 Food Additiv[END_REF] was concluded that this study provided limited evidence that the two different mixtures tested 5 had a small and statistically significant effect on activity and attention in the selected children, 6 and that the clinical significance of the observed effects remains unclear. Therefore, the panel 7 concluded that the results of the McCann study cannot be used as the basis for altering the 8 ADIs of the respective food colourants or sodium benzoate (EFSA, 2008(EFSA, , 2009)). 9

Recently, in order to harmonise the use of food additives in foods in the community, 10 Over the last few years, after a period where 100% juices seemed to be very popular 23 and widespread, the market showed a slowdown in sales of these products and a significant 24 increase in sales of fruit juice-based drinks [START_REF] Rossi | Successo alla frutta[END_REF][START_REF] Muraca | Il succo della competizione[END_REF]. These beverages 25 allowed both a product innovation and, overall, a significant reduction of fruit and, as a 1 consequence, a reduction in costs [START_REF] Rossi | Successo alla frutta[END_REF]. The beverages sector is one of the most 2 important food areas where Allura red is widely used, mainly in fruit-based drinks and soft 3 drinks based on red fruit juices The success of these beverages comes from the increasing 4 consumer demand for foods containing anthocyanins due to their health properties (Mazza, 5 2007;[START_REF] Bitsch | Bioavailability and biokinetics of 4 anthocyanins from red grape juice and red wine[END_REF][START_REF] Galvano | Cyanidins: metabolism and biological properties[END_REF][START_REF] Bonina | Oxidative stress in handball players: effect of 7 supplementation with a red orange extract[END_REF]. Many foods are important 6 sources of anthocyanins (Wu et al., 2006), but in Italy these are immediately associated with 7 red (blood) oranges [START_REF] Maccarone | Antocyanins of the Moro orange juice[END_REF][START_REF] Maccarone | Acylated anthocyanins from oranges[END_REF]. The chemical composition (Fallico et al., 8 1996;[START_REF] Arena | Fatty acids of Italian blood orange 5 juices[END_REF][START_REF] Maccarone | Flavor components of 5 Italian orange juices[END_REF][START_REF] Rapisarda | Hydroxycinnamic 22 acids as markers of Italian blood orange juices[END_REF][START_REF] Arena | Comparison of odour active 15 compounds detected by gas-chromatography olfactometry between hand-squeezed 16 juices from different orange varieties[END_REF], 9 the influence of processing [START_REF] Arena | Influence of carotenoids and pulps on the color 7 modification of blood orange juice[END_REF]Arena et al., 2001a;[START_REF] Arena | Evaluation of antioxidant capacity of blood 12 orange juices as influenced by constituents, concentration process and storage[END_REF], the 10 recovery of valuable products (Di [START_REF] Mauro | Waste water from citrus 15 processing as source of hesperidin by concentration on styrene-divinylbenzene resin[END_REF][START_REF] Mauro | Recovery of 18 anthocyanins from pulp wash of pigmented oranges by concentration on resins[END_REF] and the safety 11 aspects [START_REF] Fallico | Exposure to pesticides residues from 12 consumption of Italian blood oranges[END_REF] of red oranges have been thoroughly investigated. 12 Risk assessment is based on a deterministic approach, when the calculated risk is 13 based on a point estimate, or on a probabilistic approach, where both the variability and 14 uncertainty of input variables are taken into consideration by using probability distributions to 15 represent the input variables instead of point estimates [START_REF] Vose | Risk Analysis. A quantitative guide[END_REF]Spanjersberg et al., 16 2007). The probabilistic approach was used by [START_REF] Slob | A probabilistic approach for deriving acceptable human intake 19 limits and human health risks from toxicological studies: general framework[END_REF] intake and exposure of the Italian population to Allura red from these beverages was 1 estimated. This assessment was carried out using different modelling intakes. 2

Materials and methods 3

Sampling and chemical analysis 4

HPLC analyses of colourants 5

Sample (10 ml) was appropriately diluted with water and filtered through a 0.45µm (Albet). 6 HPLC analyses were conducted on a Shimadzu ClassVP LC-10ADvp equipped with a SPD-7

M10Avp diode array detector. The column was a C18 Omnispher (Varian) (150 mm × 4.6 8 mm, 5µm) and the chromatographic conditions were in agreement with those of Minioti et al. 9 (2007). Each colourant was identified by comparing the retention time, splitting the peak of 10 colours detected in the sample with the colour standard, and comparing the UV-vis spectra of 11 the colour standards with those of the samples. The amount of each colour was determined 12 using an external calibration curve, measuring the signal at 504 nm for E129, 485 nm for 13 E110 and 516 nm for E122. The purity of the colours was evaluated according to Minioti et 14 al. (2007) and Dir CE 2008/128: they were 88.1% for E129, 88.7% for E110 and 86.9% for 15 E122. Details (uncertainty, limits of detection and precision) of the HPLC method are 16 reported in [START_REF] Fallico | Evaluation of color contribution and 15 label conformity of pasteurized red juices and related drinks[END_REF]. 17

Estimation of the consumption of red beverages 18

For the first two scenarios the consumers characteristics (age, weight), the food categories and 19 the daily consumption rates of juice-based and soft drinks were those reported in INRAN-20 SCAI 2005-06 survey [START_REF] Leclercq | The Italian 23 National Food Consumption Survay INRAN-SCAI 2005-06: main results in terms of 24 food consumption[END_REF]). For all others the estimates of yearly and daily 21 consumption, as well as the number of consumers, of total red beverages, red fruit juice-based 22 and red soft drinks, were made using yearly sales data of the 27 selected shops. Personal 23

Risk assessment 3

The estimated daily intake (EDI) of Allura red was made by following the methods described 4 in [START_REF] Petersen | Assessing the intake of contaminants and nutrients: an 17 overview of methods[END_REF] and [START_REF] Douglass | Estimation of dietary intake of food chemicals[END_REF]. A tired approach was used to 5 estimate the exposure with different scenarios. Details of each scenario are reported in table 1. 6 First, a MPL (Maximum Permitted Level) (EFSA, 2009) than a SPM (Single Point Method) 7 [START_REF] Petersen | Assessing the intake of contaminants and nutrients: an 17 overview of methods[END_REF], were hypothised. After, as to our knowledge no specific data, 8 both for consumption and for colorant levels, in Italian red fruit based beverages were 9 available, more refined scenarios were attempt. This part of the study was carried out with the 10 collaboration of the Roberto Abate spa company, which owns a chain of more than 40 shops, 11 each having from 200 to 6000 m 2 of selling area. It represents about the 10% of food retailers 12 in the metropolitan area of Catania (Italy) (∼1.0-1.5 million people). One year selling data 13 from 27 shops, including all of the largest ones, were used for this study. Moreover, from 14 these shops, all of the red fruit juice-based and red soft drinks were sampled in triplicate, 15 excluding 100% juices. A total of seventeen different brands were collected. For these 16 scenarios (3A, 3B and 4) a probabilistic approach was used. The distribution of the colour 17 concentration (mg/kg) in non-alcoholic beverages was multiplied with the distribution of 18 beverages. In order to carry out a safety evaluation of colourant E129 exposure, a risk 19 assessment model with a probabilistic approach from @Risk software ver.5.5 was used 20 (Palisade Inc., Newfield, NY, USA). In this kind of analysis the single values are replaced 21 from distributions of probability; a value selected at random from the food consumption 22 distribution curve was multiplied by a value drawn at random from the colour concentration 23 distribution curve; this process was repeated thousands of times until the pre-planned number 24 Simulations were run in order to obtain estimations of the following parameters: the number 3 of customers (total, red beverages, red soft drinks and red juice-based drinks (Table 4); the 4 distribution of red beverage consumption for consumers only (Table 5); yearly and daily 5 intakes (mg) of E129 for each beverage (Tables 678). The conditions of each simulation were 6 as follows: number of iterations: 10,000; sampling type: Latin hypercube. For each simulation 7 output the probability distribution between the 5 th and 95 th percentiles, the mean, the standard 8 deviation and the uncertainty parameters were recorded. Moreover the best fitting 9 distribution, according to the K-S value, was extrapolated. 10

Results and discussion 11

The estimation of daily intake of a substance from a food source needs data concerning the 12 amount of food eaten and the level at which the compound is present in that food. Many 13 methods can be used to obtain these data; the most common refer to the literature and specific 14 surveys of food intake [START_REF] Douglass | Estimation of dietary intake of food chemicals[END_REF]. Data concerning the consumption of 15 juices, nectars and soft drinks in Italy in 2009 indicates a consumption of 66 litres per year per 16 capita for carbonated, still and sports/energy drinks and of 15 litres per year per capita for 17 pure juices, nectars and juice-based drinks (Bevitalia, 2010 a,b). However, to the best of our 18 knowledge, specific data concerning the red beverages sector do not exist. [START_REF] Weir | JECFA 1980. Acute oral administration -dogs, five experimental non-20 toxic red colors[END_REF] The sales data of the selected 27 shops over a one-year period, covering a total area 20 with 1-1.5 million people, are reported in Table 2. In one year 5,100,630 receipts were given 21 out and 49,856 red beverages were sold with a total of 62,682.8 litres. In particular, the red 22 juice-based drinks accounted for 32,640.8 litres and the red soft drinks for 30,042 litres. 23 Furthermore, from the collected data it was possible to establish that red juice-based drinks 1 represented about 5.7% of all juice-based drinks, while red soft drinks represented 1.5% of all 2 soft drinks sold during this period. 3

Table 3 shows the results concerning the presence of colourants, the percentages of 4 colourants and the amounts in litres of the 17 studied beverages. Almost 48% of the red 5 beverages was made up by the red soft drinks, with one alone representing the 35.2% of the 6 market. Concerning the 15 red juice-based drinks, they ranged from a minimum of 0.17% to a 7 maximum of 11.96% of the market. Most of these were referred to as red orange juice (Fallico 8 et al., 2010), and, concerning their colour, in 7 this was due to the addition of anthocyanin 9 extracts, but in 8 it was due to the presence of Allura red. The colour of the two red soft 10 drinks was due to colourants: one contained a mixture of E122 and E110 and the other was 11 coloured with E129. The amount of colourants ranged from 10.86 mg/l in the first soft drink 12 (the sum of E122 and E110) to 55.91 mg/l in a juice-based drink (no. 14). It is interesting to 13 note that all but the soft drinks, which are usually perceived negatively, were very similar to 14 fruit juices. The contribution of actual fruit juices in these drinks to their colour is very small 15 or even sometimes negligible [START_REF] Fallico | Evaluation of color contribution and 15 label conformity of pasteurized red juices and related drinks[END_REF]. Instead, they have significant amounts of 16 colourants added, which are often quite higher than the amounts in soft drinks. The levels of 17 added colourants were very high in beverage nos. 5, 11 and 14, with 42.83, 48.51 and 55. 91 18 mg/l of E129, respectively. The first one claimed to have red orange juice properties whilst 19 the other two claimed to have strong antioxidant activity and berries. It is important to note 20 that consumption of the colourant depended on both its concentration in the beverages and on 21 beverage consumption rates. For instance, sample no. 17 represented the 35.2% of the market, 22 but it contributed 50.6% of Allura red consumption. Beverage nos. 11 and 14 each 23 represented each less than 3% of the market, but contributed to the 5.8% and 7.5% of Allura 24 red consumption, respectively. 25 The average level of colourant in the red juice-based drinks was 29.6 mg/l and the 1 levels were lognormally distributed, while in the two soft drinks the average value was 20.5 2 mg/l and the levels were normally distributed (Table 3). 3

In order to build a more realistic scenario to assess the daily intake of Red Allura from 4 red beverages, estimation of the number of consumers and a survey of these beverages was a 5 primary goal of this research. Literature data and the number of receipts were used to estimate 6 the total number of customers of the shopping centres. In turn, estimation for red drink 7 products consumers in one year was attempt. 8

Studies aimed to understand buyers' habits in Italy [START_REF] Audipress | Chi sono gli attuali "Responsabili Aquisti[END_REF]; TNS, 2010) 9 highlighted that the majority of consumers, about the 75%, prefer shopping chain. 10 Considering these as our population, their percentages as well as the frequency of purchases 11 are reported in Table 4. First, the total number of customers was divided in four categories, 12 considering the frequency of purchases. Than, considering that the number of receipts for 13 each category, is given by the number of customers multiplying the number of purchases, the 14 inverse formula (n. of receipts/n. of purchases) was used to estimate the number of customers 15 for each category. The number of receipts for each category was the result of a binomial 16 distribution of the total number of receipts and the related percentage. The total number of 17 customers was given by adding the customers of all categories. The resulting number was 18 137370 as average, with a minimum value of 123648 and maximum of 153921 total 19 customers. The best-fit distribution is a β-General one (Table 4). 20

From this, taking into account the tendency of customers to buy red beverages, the 21 incidence of red products on the category and the number of consumers per buyer, were 22 estimated the number of consumers for red beverages for the two scenarios 3A and 3B, 23 respectively (Table 4). The two scenarios differ each other for the number of consumers per 24 buyer (or item). In fact, the scenario 3A hypotheses that each buyer is the consumer of the 25 item, while the scenario 3B differs between the buyer and the numebr of consumers. In order 1 to build scenario 3B during two consecutive days in the largest of the 27 shopping centres, 30 2 buyers for each category of red beverages, juice based and soft drinks, were asked to say for 3 who and for how many people they were going to buy the red beverage. Regarding the red 4 juice based drinks they readily answered "for children" and the most common number of 5 consumers were one or two, up to three people per item. For this reason was chosen a 6 Triangular distribution (1; 1.37; 3) centred on 1.37 (the average number of children per 7 female in Italy (ISTAT, 2010)) ranging between one and three. For soft drinks the most 8 common number of consumers declared per item was 3, ranging between 1 and 4. So, was 9 chosen a triangular distribution (1; 3; 4). 10

For scenario 3A the average number of customers buying red soft drinks was 11 estimated to be 585, Log-normal distributed, ranging between 426 and 768 consumers (Table 12 4). While the average number of customers of red juice based drinks was 3571, Log-normal 13 distributed, ranging between 2586 and 4705. For scenario 3B it was estimated 1560 customers 14 for soft drinks, β-general distributed, ranging between 836 and 2360 customers. The estimated 15 number of juice based drinks consumers was 5009, Log-normal distributed, ranging between 16 3290 and 7210 customers. The sensitivity analysis highlighted that, in scenario 3A, the 17 uncertainty on the estimation of the number of customers was related to the tendency to buy 18 and to the incidence of the red products; while in scenario 3B it was first related to the 19 number of consumers per buyer, followed by the two input described above (Table 4). 20

The yearly (litres) and daily (ml) consumptions of both red soft drinks and red juice-21 based drinks were estimated (10000 iterations each) by dividing the litres sold for each 22 category by the appropriate customer distribution. These results are reported in Table 5. Both 23 consumption distributions were positively skewed, as found for other foods (Zhang et al., 24 2009). In fact, for both red soft drinks and red juice-based drinks the best fitting distribution 25 was a lognormal one. Data suggest that, in scenario 3A, on average, a consumer drinks 53.1 1 litres of a red soft beverage per year. However, none of the values between 39.1 and 70.7 2 litres per year can be excluded (Table 5). This would mean a daily consumption of 145.4 ml 3 of red soft drinks, ranging between 107.1 and 193.8 ml. These data fully agree with literature, 4 both with the industrial and food consumption surveys. In fact, of the 65 litres of yearly 5 consumption per capita, 79% (51.4 litres) must be ascribed to carbonated drinks (Bevitalia, 6 2010a), while the Italian food consumption survey reports a mean value of 130.9 ml up to 330 7 ml (95 th precentile) as daily consumption [START_REF] Leclercq | The Italian 23 National Food Consumption Survay INRAN-SCAI 2005-06: main results in terms of 24 food consumption[END_REF]. Thus, the approach used here 8 to estimate the number of consumers of red beverages seemed to be appropriate. Moreover, 9 the consumption of red soft drinks only concerned a small fraction of the population; 10 however, this was at the same level as all of the other soft drinks. It seems that the 11 consumption of red soft drinks was not added to that of the other soft drinks, but rather it was 12 treated as an alternative to them. The yearly estimated consumption of red juice-based drinks, 13 for the same scenario, was 9.5 l (25.6 ml/day), ranging between a minimum of 7.0 l (19.0 14 ml/day) and a maximum of 12.5 l (34.3 ml/day). The values reported here are similar to those 15 in Bevitalia (2010 b) (12.5 litres/year (34.6 ml/day)) for the category of nectars and juice 16 drinks; they are lower than those, reported as fruit and vegetable juices, found in the Italian 17 food consumption survey (64.4 ml up to 250 ml at the 95 th percentile, [START_REF] Leclercq | The Italian 23 National Food Consumption Survay INRAN-SCAI 2005-06: main results in terms of 24 food consumption[END_REF]. 18 Lower values were estimated for scenario 3B (Table 5). In fact, the yearly estimated 19 consumption of red soft drinks ranged between 12.7 and 35.9 litres, with a most probable 20 value of 21.3 litres. It means a daily consumption of 58.3 ml ranging between 34.8 and 98.4 21 ml/day. As concerns the estimation of consumption of red juice based products in scenario 3B 22 the results are the following: 6.9 litres/year (18.9 ml/day) ranging between 12.4 and 27.2 23 litres/year. The sensitivity analysis, analogously to the number of consumers estimation, have 24 shown that in scenario 3A the main uncertainty factors are the tendency to buy and the 25 Combining the daily beverages consumption value with the concentration of the 3 colourant (Scenarios 1 and 2) or the distributions of E129 concentrations with those of 4 beverage consumption (Scenarios 3-4), the per capita estimation of yearly and daily intakes of 5 E129 were obtained (Tables 678). In the worst case (Scenario 1), considering a consumption 6 per capita of 64.4 and 130.9 ml/d on the average, 250 and 330 ml/d at the 95th percentile, for 7 juice based and soft drinks [START_REF] Leclercq | The Italian 23 National Food Consumption Survay INRAN-SCAI 2005-06: main results in terms of 24 food consumption[END_REF], the average colorant intake was 6.5 mg/day 8 and 13.0 mg/day for juice-based and soft drinks, respectively. The daily intakes, for 9 consumers, at the 95 th percentile were 25.0 and 33 mg/day for juice-based drinks and soft 10 drinks, respectively (Table 6). The second Scenario took in account the same consumption 11 rates of Scenario 1, but instead of the Maximum Permitted Level of the colorant (100 mg/l), 12 were used the average values found in these beverages (Table 3). Under these hyphoteses the 13 intake of colorant was 1.9 and 2.7 mg/day from juice based and soft drinks, respectively. At 14 the 95 th percentile the intake was 7.4 and 6.8 mg/day, respectively (Table 6). 15

These intakes, both for Scenario 1 and 2, according to categories of consumers 16 reported in [START_REF] Leclercq | The Italian 23 National Food Consumption Survay INRAN-SCAI 2005-06: main results in terms of 24 food consumption[END_REF] (children (3-9.9 years), teenagers (10-17.9 years) and adults 17 (18-64.9 years)), were converted in µg kgbw -1 day -1 (Table 6). These data, analogusly to 18 literature [START_REF] Tennant | Screening potential intakes of colour additives used in non-alcoholic 4 beverages[END_REF], EFSA 2009[START_REF] Expochi | Long-term dietary exposure to different food colours in young children 6 living in different European contries[END_REF], highlighted that the most exposed 19 were children because of their high consume/body weight ratio. In fact, for Scenario 1, the 20 mean exposure values for red juice based and red soft drinks were 247.5 and 498.1 µg kgbw - 21 1 day -1 , respectively. At the 95 th percentile the childrens' exposure was 957.9 and 1264.4 µg 22 kgbw -1 day -1 , respectively. The above data represent the 3.5% and the 7.1% on the mean, up to 23 13.7% and 18.1% at the 95 th percentile of the A.D.I. value, for juice based and soft drinks, 24 respectively. Moreover, comparing the above data to those reported in the EXPOCHI (2010) 25 6635.4 µg kgbw -1 day -1 as mean value and at the 95 th percentile, respectively. They represent, 2 on the average the 32% up to the 64.4% of total intake for juice-based and soft drinks, 3 respectively. Lower values, 14.4% and 19.1% at the 95 th percentile. 4

Using the average levels of the colorant (Scenario 2) took to a much lower intake 5 (Table 6). In fact, under this scenario the daily intake per kilogram of body weight for Italian 6 children was 73.2 and 102.3 µg for juice-based and soft drinks, respectively. It could achieve 7

283.5 and 260.5µg at the 95 th percentile. Representing the 1.0 and 4.1% of the A.D.I. at mean 8 and the 95 th percentile for juice-based drinks and 1.5 and 3.7% for soft drinks, respectively. 9

The comparison with results of the "analytical scenario" reported in EXPOCHI (2010) study 10 (524.3 and 1257.5 µg kgbw -1 day -1 as mean value and at 95 th percentile, respectively) 11 highlights that the consumption of these beverages provides the 14.0% of colorant total daily 12 intake (up to the 22.5%) from juice-based drinks or the 19.5% (up to 20.7%) for soft drinks. [START_REF] Scf | Reports of the Scientific Committee for Food (21 th series)[END_REF] The above scenarios took in account results of surveys not specific for red beverages 14 and, scenario 1, using the maximum permitted level of the colorant. Moreover, these did not 15 consider properly the amount of knowledge contained in the Input parameters in terms of 16 uncertainty and the associated probability. Here follow the results of the E129 daily intake, 17 using a probabilistic approach, in always more realistic scenarios (3A, 3B and 4) aimed to 18 manage the uncertainty and variability about the number of consumers as well as the colorant 19 level in the beverages. 20

Under the hypotheses of scenarios 3A and 3B, using the distributions of: colorant 21 levels, the year beverages intake and the appropriate number of consumers, dividing for 365, 22 was possible to get an estimation of the Red Allura daily intake (Table 6). Under scenario 3A 23 it was 0.8 mg/d, ranging between 0.2 and 1.7 mg/day for juice-based drinks. The intake of 24 E129 from red soft drinks was estimated to be 3.0 mg/day, ranging between 0.4 and 5.9 25 Under scenario 3B the daily intake from juice-based drinks was 0.6 mg (range 0.2 -1.3 2 mg/day); it was 1.2 mg/day from red soft drinks, ranging between 0.1 and 2.6 mg/day. 3

The above data suggest that the intake (µg) per children' kilogram of body weight 4 (Table 6) is 35.5 µg, ranging between 9.3 and 87.4 µg kgbw -1 d -1, , from juice-based drinks 5

under Scenario 3A and 29 µg, ranging between 6.6 and 67.7 µg kgbw -1 d -1 , under Scenario 3B, 6 respectively. Representing, under the two scenarios, the 0.5% (0.1-1.3%) and the 0.4% (0.1-7

1.0%) of the A.D.I. value, respectively. 8

The daily intake of E129 from red soft drinks was 3.0 mg, ranging between 0.4 and 5.9 9 mg/day, under Scenario 3A, and 1.2 mg, ranging between 0.1 and 2.6 mg/day, under Scenario 10 3B (Table 6). These mean 130.5 µg kgbw -1 d -1 (38.3-310.7 µg kgbw -1 d -1 ) under Scenario 3A 11 and 54.0 µg kgbw -1 d -1 (12.9-137.8 µg kgbw -1 d -1 ) under Scenario 3B. Representing the 1.9% 12 (0.6-4.4%) and the 0.8% (0.2-2.0%) of the A.D.I. value, respectively. 13

The sensitivity analysis highlighted that most of the uncertainty in the estimation of 14 E129 daily intake in both scenarios was due to the variability of E129 level in red beverages, 15 followed by the tendency to buy and the market incidence for scenario 3A and the number of 16 consumers per buyer, the tendency to buy and the market incidence in scenario 3B, 17 respectively. The number of consumers per buyer becomes very meaningful in the estimation 18 of daily intake from soft drinks under scenario 3B (Table 6). [START_REF] Weir | JECFA 1980. Acute oral administration -dogs, five experimental non-20 toxic red colors[END_REF] Although the intakes shown above can be considered as realistic since they were based 20 on both the true E129 concentration distribution and on red beverages consumption, they do 21 not provide information about brand loyalty behaviour. Moreover, this approach did not take 22 into account the contribution of each beverage to the E129 intake. For these reasons, an 23 estimation of E129 intake was made for each beverage and a weighted average were taken 24 (Scenario 4). Similar to other foods [START_REF] Leclercq | Stochastic 18 modelling of human exposure to food chemicals and nutrients within the "Montecarlo" 19 project. An exploration of the influence of brand loyalty and market share on intake 20 estimates of intense sweeteners from sugar-free soft drinks[END_REF]Zhang et al., 2009), the market 25 3), and the number of consumers 1 found in scenario 3B, was used to estimate the per capita consumption of each beverage. The 2 E129 intake values were obtained for each beverage by dividing the mg/year of colourant 3 (Table 3) by the consumer distribution (Table 4), from which, in turn, the estimated daily 4 intake was obtained (Table 7). 5

A new average intake (Table 8), both for red juice-based and red soft drinks, was 6 estimated using the following equation: EDI = ∑(EDI i * L i /L T ). Where EDI i is the estimated 7 daily intake for a single beverage, L i is the litres of the beverage and L T are the total litres for 8 each category of juice-based and soft drinks. For the juice-based drinks (Table 7): three 9 beverages (11, 14 and 15) showed a daily intake higher or equal to 1 mg/day. For beverage 10 14, under the brand loyalty scenario, the intake of E129 achieved very high levels: 2.6 mg/day 11 (3.6 mg/day at the 95 th percentile). All other juice-based drinks containing E129 (1, 2, 4, 5, 12 10) showed a daily intake equal or lower than 0.7 mg/day. As mentioned above, beverage no. [START_REF] Scf | Reports of the Scientific Committee for Food (21 th series)[END_REF] 5 claims to have red orange juice properties, whereas the other two, 11 and 14, claim to have 14 very high antioxidant levels and healthy properties of berries. The EDI values for the two soft 15 drinks (Table 7) were 0.2 and 0.5 mg/day with the highest values of 0.3 and 0.8 mg/day, 16 respectively. The weighted average daily intake for Allura red showed (Table 8) a lognormal 17 distribution centred on 0.3 mg/day for red juice-based drinks and 0.4 mg/day for red soft 18 drinks. The highest values (95% percentile) were 0.5 and 0.6 mg/day, respectively. These data 19 suggest a children intake of 14.7 µg kg bw -1 d -1 (6.9-28.2 µg kg bw -1 d -1 ) from juice-based 20 drinks and 19.8 µg kg bw -1 d -1 (9.1-37.6 µg kg bw -1 d -1 ) from soft drinks, respectively. 21 Representing the 0.2% (0.1-0.4%) of the A.D.I. value for juice-based drinks and the 0.3% 22 (0.1-0.5%) of the A.D.I. value for soft drinks, respectively. Under this scenario the 23 uncertainty was due exclusively to the number of consumers. 24 The above data show that the estimation of the E129 daily intake changes significantly 25 with the hypotheses under each scenario. There is a deep difference between the first scenario 1 and all others. In fact, under scenario 1, the daily intake at the 95 th percentile of the colourant 2 from red soft drinks can achieve the 64% of the A.D.I. value for children. The more the 3 scenario becomes realistic the lower is the anticipated intake of E129, at least from red 4 beverages. In fact the anticipated daily intake from these beverages is always far from the 5 A.D.I. value. 6

The more refined becomes the scenario the lower is difference of the colourant intake 7 from juice-based and soft drinks, respectively: the colourant level in the beverages is the most 8 important factor affecting its daily intake. The loyalty to a single red beverage, if it has high 9 E129 levels, can significantly increase the daily intake of the colourant. 10

All of the beverages, with colourant concentrations below 30 mg/l, did not show any 11 potential risk. Considering the findings reported in risk evaluation processes (EFSA, 2009), 12 these data clearly suggest that the main risk from the colourants in beverages may be from the 13 overall levels used. Although the risk assessment process did not call for a modification of the 14 allowed amounts of E129, the obligation of art. 24 of REG 1333/08 to include the additional 15 information "may have an adverse effect on activity and attention of children", can have, in 16 practice, the same effect as a ban on these products. A significant reduction in the amount of 17 E129 allowed in non-alcoholic beverages would help to protect young consumers. Moreover, 18 the data above highlight that if a risk does exist, it often comes from beverages claiming to 19 have healthy properties on their labels. Major restrictions from this point of view would be 20 very useful. 21

F

  concerning the role of synthetic colourants on children's behaviour (attention deficit 1 disorder (ADD) and/or attention deficit hyperactivity disorder (ADHD)), their use has been 2

  the European Parliament and the Council of the European Union adopted Regulation (EC) 11 No. 128/2008 of 22th December 2008 on food additives. This regulation defines 'colours' as 12 substances that add or restore colour in a food, and include natural constituents of foods and 13 natural sources which are normally not consumed as foods as such and not normally used as 14 characteristic ingredients of food. Preparations obtained from foods and other edible natural 15 source materials obtained by physical and/or chemical extraction resulting in a selective 16 extraction of the pigments relative to the nutritive or aromatic constituents are colours within 17 the meaning of the Regulation'. 18 Despite this scientific advice, since July 2010, Annex V of Regulation (EC) No. 19 1333/2008 provides a list of food colours (E 110, E 104, E 122, E 129, E 102, E 124) which 20 must be included on food labels with the following information: 'may have an adverse effect 21 on activity and attention in children'. 22

  managers of the shopping chain was essential in order to obtain both 1 sets of data. 2

  red beverages on the category, while in scenario 3B these factors are 1 overcome by the number of consumers/buyer. 2

  .P.L. scenario) concerning the total exposure to E129 of Italian Children, 773.7 and 1

  . The best fitting distribution for the daily intake was, in both cases, a lognormal one. 1

  using the percentage of litres (Table

Food

  4); K-S: 0.0082 1) n. of consumers: -0.79 2) tendency to buy: -0.38 3) Incidence %: -0.30 LogNormal (18.9; 4.5); K-S: 0.0071 1) n. of consumers: -0.61 2) tendency to buy: -0.58 3) Incidence %: -0.43 Page 30 of 32 http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk

  each Member State to monitor the consumption and usage of food additives (Report EU, 1 2001). The European Food Safety Authority (EFSA), specifically the Panel onFood 2 Additives, Flavourings, Processing Aids and Food Contact materials, following a request 3 from the European Commission, was asked to assess the results of the McCann study and it 4

		F o r
		P
		e
		e r
		R
		e
		v i e
		w
		O n l
		y
	24	).
	25	European Parliament and Council Directives 94/35/EC, 94/36/EC and 95/2/EC require

Table 2 -

 2 Data from the shooping centers.

	o r
	P
	e
	e r
	R
	e
	v i e
	w
	O n l
	y

Table 3 -

 3 Market incidence and colorant data of each beverage.

	Litres per year

LogNormal (29.6; 18.1)

		Food Additives and Contaminants
	No. Of shopping centers	27 27 27 27
	No. Of receipts	5100630 5100630 5100630 5100630
	No. Of items per year (red beverages)	49856 49856 49856 49856
	Litres per year (red beverages)	62682.8 62682.8 62682.8 62682.8
	Litres per year (red juice based)	32640.8 32640.8 32640.8 32640.8
	Litres per year (red soft drinks)	30042 30042 30042 30042
	16 17 Incidence of red soft drink Soft Incidence of red juice based dirnks F o r P e e r R e v i e w 7977 12.73 27.00 E122 + E110 10.86 (1.80) 22065 35.20 73.00 E 129 30.22 (1.49) Normal (20.5; 10.9) 1.5% 1.5% 1.5% 1.5% 5.7 5.7 5.7 5.7% % % % O n l y 86630.2 (4360) 666804.3 (32876)	6.6 50.6	11.00 89.00
				28

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Table 4 -

 4 Estimated number of red beverages per year.

				Frequency of purchases	%	Purchases per year	Number of customers
	Total customers of the	shopping centres	Input Output	Never 1 per month 2-3 per month 4-8 per month > 21 per month (daily) Total customers	25 13.1 23.6 52.8 10.5	-12 Uniform (24; 36) Uniform (48; 96) 252	Binomial (5100630; 0.131)/12 Binomial (5100630; 0.236)/Uniform (24; 36) Binomial (5100630; 0.528)/Uniform (48; 96) Binomial (5100630; 0.105)/252 Mean: 137370; st.dev.:9212; min (5%): 123648; Max (95%): 153921 β β β β-General (1.99; 3.2; 119333; 166273); K-S: 0.01
				Total Customers			β-General (1.99; 3.2; 119333; 166273)
	Scenario 3A	Input Output	Tendency to buy Incidence on category Number of consumers per buyer Number of consumers		Red Soft Uniform (0.218; 0.35) Normal (0.015; 0.0015) 1	Red Juice based Uniform (0.35; 0.562) Normal (0.057; 0.0057) 1

(Uncertainty (

rank and regr.)) Mean: 585; st. dev.: 106; min (5%): 426; Max (95%): 768 LogNormal (585; 106); K-S: 0.0173 (1. tendency to buy: 0.74; 2. Incidence %: 0.55). Mean: 3571; st. dev.: 654; min (5%): 2586; Max (95%): 4705 LogNormal (3571; 654); K-S: 0.013 (1. tendency to buy: 0.74; 2. Incidence %: 0.55).

			Total Customers	β-General (1.99; 3.2; 119333; 166273)
	Scenario 3B	Input Output	Tendency to buy Incidence on category Number of consumers per buyer Number of consumers	Red Soft Uniform (0.218; 0.35) Normal (0.015; 0.0015) Triang (1; 3; 4)	Red Juice based Uniform (0.35; 0.562) Normal (0.057; 0.0057) Triang (1; 1.21; 2)

(Uncertainty (rank and regr.)) Mean: 1560; st. dev.: 465; min (5%): 836; Max (95%): 2360

	β β β β-General (

4.9; 9.4; 252; 4080); K-S: 0.0074 (1. N. of consumers: 0.78; 2. tendency to buy: 0.45; 3. Incidence %: 0.34). Mean: 5009; st. dev.: 1194; min (5%): 3290; Max (95%): 7210 LogNormal (5009; 1194); K-S: 0.0075 (1. N. of consumers: 0.64; 2. tendency to buy: 0.57; 3. Incidence %: 0.42). Page 29 of 32 http

	Food Additives and Contaminants
	F o r P e e r R e v i e w	O n l y
		29

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Table 5 -

 5 Estimation of yearly and daily consumption of red beverages.

			Red soft drinks	Uncertainty (rank and regr.)	Red juice based	Uncertainty (rank and regr.)
			Yearly (litres)		Yearly (litres)
		Mean (St. dev.):	53.1 (9.8)		9.5 (1.8)
	Scenario 3A	Min (5%): Max (95%): Best fit: Mean (St. dev.): Min (5%):	39.1 70.7 LogNormal (53.1; 9.8); K-S: 0.0154 Daily (milliliters) 145.4 (27) 107.1	1) tendency to buy: -0.73 2) Incidence %: -0.55	7.0 12.5 LogNormal (9.5; 1.8); K-S: 0.0185 Daily (milliliters) 25.6 (4.8) 19.0	1) tendency to buy: -0.73 2) Incidence %: -0.55
		Max (95%):	193.8		34.3
		Best fit:	LogNormal (145.4; 27); K-S: 0.0154		LogNormal (25.6; 4.8); K-S: 0.0185
			Yearly (litres)		Yearly (litres)
		Mean (St. dev.):	21.3 (7.4)		6.9 (1.6)
	Scenario 3B	Min (5%): Max (95%): Best fit: Mean (St. dev.): Min (5%):	12.7 35.9 LogNormal (21.3; 7.4); K-S: 0.0082 Daily (milliliters) 58.3 (20.4) 34.8		3.2 17.7 LogNormal (6.9; 1.6); K-S: 0.0071 Daily (milliliters) 18.9 (4.5) 12.4
		Max (95%):	98.4		27.2
		Best fit: LogNormal (58.3; 20.

Table 6 -

 6 Estimation of E129 Daily Intake from red beverages.

	Intake (mg/d)

Table 7 -

 7 Estimation of E129 daily intake from single red beverage.

		% Litres	% Colourants	Consumers	Daily intake (mg)
	1
	Min. (5%)			484	0.36
	Max. (95%)			1050	0.79
	Mean (St. dev.)	14.6	24.8	733 (175)	0.55 (0.13)
	Best fit			LogN (484; 175)	LogN (0.55; 0.13)
	2
	Min. (5%)			106	0.29
	Max. (95%)			229	0.65
	Mean (St. dev.)	3.2	4.4	160 (38)	0.45 (0.11)
	Best fit			LogN (160; 38)	LogN (0.45; 0.11)
	4
	Min. (5%)			759	0.21
	Max. (95%)			1649	0.45
	Mean (St. dev.)	23.0	22.1	1150 (275)	0.32 (0.08)
	Best fit			LogN (1150; 275)	LogN (0.32; 0.08)
	5
	Min. (5%)			87	0.53
	Max. (95%)			190	1.03
	Mean (St. dev.)	2.6	6.6	132 (32)	0.74 (0.20)
	Best fit			LogN (132; 32)	LogN (0.74; 0.20)
	10
	Min. (5%)			167	0.20
	Max. (95%)			362	0.43
	Mean (St. dev.)	5.1	4.7	253 (60)	0.30 (0.07)
	Best fit			LogN (253; 60)	LogN (0.30; 0.07)
	11
	Min. (5%)			161	0.60
	Max. (95%)			349	1.32
	Mean (St. dev.)	4.9	13.7	243 (58)	0.92 (0.22)
	Best fit			LogN (243; 58)	LogN (0.92; 0.22)
	14
	Min. (5%)			178	1.85
	Max. (95%)			387	3.59
	Mean

Uncertainty: N. of consumers (regr.> -0.90)

(St. dev.) 5.4 17.5 270 (65) 2.58 (0.55) Best fit

		F o r
			P
			e
			e r
				R
				e
				v i e
				LogN (270; 65)	LogN (1.55, 0.55; shift 1.02) w
	15 Min. (5%) Max. (95%) Mean (St. dev.) Best fit	6.0	6.2	83 161 121 (24) LogN (121; 24)	0.70 1.52 1.06 (0.25) LogN (1.06; 0.25) O n l
	16				y
	Min. (5%)			878	0.11
	Max. (95%)			1906	0.29
	Mean (St. dev.)	26.6	11.5	1330 (318)	0.19 (0.06)
	Best fit			LogN (1330; 318)	LogN (0.19; 0.06)
	17
	Min. (5%)			2428	0.34
	Max. (95%)			5272	0.76
	Mean (St. dev.)	73.4	88.5	3679 (879)	0.52 (0.13)
	Best fit			LogN (3679; 879)	LogN (0.52; 0.13)
					32

Table 8 -

 8 Weighted estimation of E129 daily intake from red beverages (scenario 4). N. of consumers (regr.> -0.9).

	Intake (mg/d)	3-9	Intake (µg/d/kg bw)

Uncertainty:

.9 years 10-17.9 years 18-64.9 years Red juices based drinks

	Mean (St. dev.)	0.34 (0.09)	14.7 (34.9)	6.7 (2.8)	4.9 (1.7)
	Min. (5%)	0.22	6.9	3.7	2.8
	Max. (95%)	0.50	28.2	11.7	8.0
	Best fit	LogNormal (0.34; 0.09) K-S: 0.007
		Red soft drinks
	Mean (St. dev.)	0.44 (0.11)	19.8 (53.3)	9.0 (5.2)	6.6 (2.3)
	Min. (5%)	0.28	9.1	4.8	3.8
	Max. (95%)	0.63	37.6	15.3	10.7
	Best fit	LogNormal (0.44; 0.11); K-S: 0.003

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Food Additives and Contaminants

F o r P e e r R e v i e w O n l y

26 Table 1 -Description of Input used for the models.

Method

Input References 1 Maximum Permitted Level (M.P.L.).

• Consumption of fruit juices, ml/day: 130.9; 330 (mean; 95th).

• Consumption of Soft drinks, ml/day: 64.4; 250 (mean; 95th).

• Maximum Concentration allowed of E129 in beverages: 100 mg/l.

3B Probabilistic

Red juice based drinks • Consumption of beverages in the selected shops (liter/year): Table 2 • Distribution of tendency to drink: Uniform (0.35; 0.56).

• Distribution of number of buyers:∑ ∑ ∑ ∑(Binomiali (number of receipt; Pi)/Purchases per yeari).

• Distribution of market share in the selected shops: Table 2.

• Distribution of number of consumers: Triang (1, 1.21, 2).

Soft Drinks • Consumption of Soft drinks in the selected shops (liter/year): Table 2 • Distribution of tendency to drink: Uniform (0.218; 0.35).

• Distribution of number of buyers:∑ ∑ ∑ ∑(Binomiali (number of receipt; Pi)/Purchases per yeari).

• Distribution of market share in the selected shops: