Ignacio Alvarez-Hamelin
email: ignacio.alvarez-hamelin@cnet.fi.uba.ar

Juan Ignacio Giribet

Pablo Jacovkis

Mario Valencia-Pabon

Ariel Futoransky

IS IT POSSIBLE TO FIND THE MAXIMUM CLIQUE IN GENERAL GRAPHS?

Keywords: algorithms, graphs, complexity

Finding the maximum clique is a known NP-Complete problem and it is also hard to approximate. This work proposes two efficient algorithms to obtain it. Nevertheless, the first one is able to fins the maximum for some special cases, while the second one has its execution time bounded by the number of cliques that each vertex belongs to.

Introduction

Finding cliques in graphs is a well known problem, mainly the maximum clique was found to be a NP-Complete problem [Karp, 1972]. Indeed, from any vertex, to discover the maximum clique to which it belongs, we should take all combinations of k neighbors and verify whenever they are mutually adjacent, which yields an exponential time as a function of the vertex degree. Moreover, [Johnson, 1973] shows that there exists no sublinear approximation algorithm. This problem was largely treated and an extensive survey can be found in [START_REF] Bomze | The maximum clique problem[END_REF]. In this work, we present two algorithms to find maximum cliques. We decompose the graph vertex by vertex until no vertices remain; then, we re-build the graph restoring each of the vertices, one by one, in an inverted order computing maximal cliques at each step. Next section is devoted to present the algorithms and the theorems showing their correctness. A section showing the algorithms applied to real graphs is presented to illustrate how they works. The paper is concluded with a discussion about the complexity of the problem.

Algorithms

Let G = (V, E) be a simple undirected graph with n = |V | vertices and m = |E| ≤ |V | × |V | edges. The neighborhood of a vertex is the set composed by vertices directly connected to it, i.e., w ∈ N (v) such as {v, w} ∈ E. Then, the degree of vertex v is denoted as

d(v) = |N (v)|. Let us call H = (V, E, A) the annotated graph G, where |A| = |V | such that if v ∈ V then a v is a list of attributes of vertex v.
Attributes a v are list of sets, each of one a clique, and they are computed by the proposed algorithms. For Algorithm 2, we denote an element of the list as a set L ∈ a x v , its initialization with set L as a x v ← {L}, and the append of set L as a v ← {a v , L} and the elimination of a set L as a v ← {a v \ L}. The elements of this list can also interpreted as a i v for i ∈ N (v) (see Algorithm 1). A list of maximal cliques is stated by Definition 1.

Definition 1. Given a vertex v and the list l v where its elements are sets S i having v, i.e., S i ⊃ v. These sets S i ∈ l v are maximal if for all i = j they verify the following properties:

(i) v ⊆ S i ∩ S j (ii) S i S j (iii) S i S j
Therefore, lists a v have the properties presented in the following Definition 2.

Definition 2. Let a vertex v ∈ V the a v be a list composed by sets of vertices A i , such that the following properties hold:

(i) each set A i ∈ a v denotes a maximal clique having v (see Definition 1); (ii) the maximum clique K max (G) of the graph G is found as (1) K max (G) = A if A = max |A i | : A i ∈ a v i ∈ [1, |a v |] ,
where |a v | refers to the length of a v list.

Notice, firstly, each set A i is maximal in the sense that there not exist other set B ⊇ A i ; secondly sets A i ∈ a v are maximal cliques at the time that they are computed (see Algorithm 2), but at any later time can exist other clique maximal containing the A i . As we demonstrate, computing Equation 1 when Algorithm 2 finished leads to obtain the maximum clique. We analyze it cost later.

We introduce first an algorithm of low complexity that could find the maximum clique in certain cases, this is the Algorithm 1. Theorem 1. Algorithm 1 ends if G = (V, E) has finite size, computing the attributes a x v for all vertices v ∈ V and their corresponding neighbors, according to the property (i) in Definition 2.

Proof. Let us start with the winding phase of the recursion, that is, steps 1.2, 1.4 and 1.8 are executed until we reach an empty graph G ′ (see 1.7). At this step, the end of recursion (step 1.7) is found because each call to find cliques(G ′ ) function is done with a reduced set of vertices |V ′ | = |V -1| (and its induced graph), and G has finite size; i.e., the recursion is done n times.

From there, we analyze the unwinding phase. Let's start when steps 1.19 and 1.20 are executed for the first time: the return of the function will carry a graph with just the vertex v, no edges and a v = ∅ (step 1.5), that is H = ({v}, ∅, a v ). Then, the following instance(s) can add vertices of degree zero until one instance begins to add the first edges (edge), getting a star with leaves (one leaf), because the degree is an increasing function (the winding phase was carried out taking the maximum degree at step 1.2, so the unwinding one reconnects vertices with the same degree or greater one). At this instance, steps 1.9 and 1.10 are executed and step 1.10 yields L = {v, x} because a x is empty (the vertex x has no registered neighbors until now). The following conditional sentence is true (step 1.5 assures a x v = ∅) setting each neighbor as a clique, on both sides, the neighbor vertex a v x = {v, x} and the local one a x v = {v, x} (see 1.12, 1.13 and 1.14, we consider objects a w as mutable).

From this point of the algorithm execution any one of the next vertices could build a K s /s ∈ N (e.g., s = 3) because a new vertex joining former vertices constituting a K s-1 could appear. It is worth remarking that it is not possible to build

Algorithm 1: Function H ←find cliques(G) Input: a graph G = (V, E) Output: a graph H = (V, E, A) begin 1.1 find a vertex v such that d(v) is maximum ; 1.2 set M = N (v); 1.3 G ′ ← (V ′ , E ′ ) : V ′ = V \ v, E ′ = E \ {v × N (v)} ; 1.4 a x v ← ∅ for all x ∈ M , or a v ← ∅ if M = ∅ ; 1.5 set H ′ ← {∅, ∅, ∅} ; 1.6 if G ′ = ∅ then 1.7 set H ′ ← find cliques(G ′ ) ; 1.8
for each x ∈ M do 1.9

for each set a i x , or

a x = ∅ do 1.10 if (a x = ∅), then L ← N (v) ∩ a i x ∪ v ∪ x, else L ← v ∪ x ; 1.11 if (|L| ≥ |a x v | or a x = ∅) then 1.12 a v x ← L ; 1.13 a x v ← L ; 1.14 end 1.15 end 1.16 end 1.17 end 1.18 set H ← H ′ ∪ (v, v × M, a v ) ; 1.19 return H ; 1.20 end 1.21
a K s+1 at this stage (e.g., s + 1 = 4). The reason why it is not possible is that there are only K s-1 and a new vertex just adds edges between this new vertex and the present vertices, although this new vertex will never add an edge between the present vertices. In this way, the size of new cliques is an increasing function (either the maximum clique remains at same size or it is increased by one vertex). Now, we will show the a x v is always a clique. We have also shown that the first elements in a x v constitute a clique of two vertices: v and its neighbor x. Considering, at any instance in the unwinding phase of the Algorithm 1, a vertex v has a clique stored for each one of its neighbors x in a x v . Let's consider, without loss of generality, a new neighbor of v, called w, having as neighbors B ⊆ a

x v = K t , that is N (w) ⊇ B ∪ v. When step 1.12 is executed, either a v w = ∅ or a v w = {w, v} (because it is possible that a x v ∩ N (w) is empty in 1.10), and then a v w ← B ∪ w, which is also a clique because B ⊆ a x
v is a clique and w is a neighbor of all vertices in B by hypothesis.

To conclude the proof is enough to determine if the property (i) in Definition 2 is obtained by Algorithm 1. The initial case was already shown in the second paragraph of this proof. Then, considering a case where a maximal clique of vertices w and y is K s , and there exists another clique K t such that w, y ∈ K t and t ≤ s. As seen in a previous paragraph, vertices can only build cliques that increase the previous one by just one vertex. Let's consider that the next vertex z is connected with all vertices in K t and z is at most connected with t -1 vertices in K s . The minimum difference that is needed to distinguish between two cliques is one vertex. Taking into account, without loss of generality, that the maximum clique for z is K s ∪ z, steps 1.10 and 1.12 will select the clique K s ∪ z because at least one neighbor of z, let's call it u, has a i u = K s /i ∈ N (u). At this point, the values a w z , a y z , a z w , and a z y will be updated because their size is greater or equal to the size of a clique found before (see 1.12, 1.13 and 1.14). Thus, if t + 1 < s then K s remains a maximal clique for w and y; or else t + 1 ≥ s and the set of t vertices in K t plus z is a maximal clique for w and y. It is worth remarking that it is possible that a vertex v has several cliques with a neighbor x, and the condition in 1.12 assures that the maximum clique, among the known cliques, is taken. Notice that a y w and a w y have the other clique K s stored, but still K t+1 = a x w max(|a x w |, ∀x ∈ N (v)) is the maximum clique, among the known cliques, for w because t + 1 ≥ s and the previous maximum clique was K s ; the same occurs to y.

Before to analyze its complexity, we remark that Algorithm 1 not guarantee that the operation

A = max |A i | : A i ∈ a x v , v ∈ V, x ∈ N (v)
gives the maximum clique K max (G). In fact, Figure 1 presents a counterexample. Imagine that the unwinding phase takes vertices in the order 1, 2, 3,4,5,6,10,11,12,13,14. Regarding that list a 12 has not the clique {10, 11, 12} because a 10 11 = {5, 6, 10, 11}, a 11 12 = {3, 4, 11, 12} and a 10 12 = {1, 2, 10, 12}; therefore, when vertex 13 is added it never find the clique {10, 11, 12, 13}, so the maximum clique is not found. Nevertheless, the maximum clique is often found when very few vertices have degree close to d max and the graph is sparse, as it is the case for the so called scale free networks1 . Time complexity of Algorithm 1. Step 1.10 of Algorithm 1 is an intersection of two sets of size d max (maximum degree), if both are ordered2 taking O(d max ). Next, we consider loop 1.10, taking d max times, what determines O(d 2 max ). Loop 1.9 on vertex v neighbors 1.9 takes an extra d max , giving O(d 3 max ).

Step 1.2 has a complexity of O(n) if vertices are not ordered, but there is an additive cost expected smaller than O(d3 max ). Finally, the recursion is done for each vertex of the graph, producing a total time complexity of O(n • d 3 max ). In the case when neighbors are not ordered, we get O(n • d 4 max ). Considering connected graph, we can express the n recursions and the visit to all neighbors in loop 1.9 as visiting all the edges, yielding a time complexity of O(m • d 2 max ). For graphs in general, where d max could be bound by n, time complexity is O(n 4 ). However, for graphs having a heavy tailed degree distribution which can be bound by a power law 3 : P (d) ∝ d -β , it is possible to find a lower bound. Indeed, these graphs have n Now, we present another Algorithm 2 capable to find the maximum clique in any graph.

The following theorem proof the correctness of Algorithm 2, verifying how this algorithm accords with Definition 2. Theorem 2. Algorithm 1 ends if G = (V, E) has finite size, computing correctly attributes in the list a v for all vertices v ∈ V , according to Definition 2.

Proof. Let us start with the winding phase of the recursion, that is, steps 2.2, 2.4 and 2.8 are executed until we reach an empty graph G ′ (see 2.7). At this step, the end of recursion (step 2.7) is found because each call to find cliques(G ′ ) function is done with a reduced set of vertices |V ′ | = |V -1| (and its induced graph), and G has finite size; i.e., the recursion is done n times.

Next, we analyze if steps 2.13 to 2.26 maintains the list a v according to Definition 1. Firstly, suppose that a certain set L in a subset of one or more sets in a v list, that is L ⊂ S j ∈ a v . In this case, for every S j ∈ a v , the conditional step 2.14 is false because the size of L is necessarily smaller than S j because L ⊂ S j ; then the 'else' clause is executed. The condition in step 2.20 is also 'false' by hypothesis L ⊂ S j , and again the 'else' clause is selected, setting the old variable as T RU E (notice this variable was initialized as F ALSE in step 2.11). Therefore, the conditional sentence in step 2.27 will be 'false' because the value of old is not changed until step 2.11 is executed again, and L will not included in a v . Secondly, suppose that a certain L in not a subset of any A i ∈ a v , and certain A j are subsets of L. Considering when condition in 2.14 is true, then new will be set 'true' only if sets A j are been considered, and also the set A j is eliminated form list a v in step 2.16 (notice that the case a v = ∅ conditions 2.14 and 2.15 are true because B size is zero and B ∩ L = B when B = ∅). Finally, for the case in which L is different to all sets A i ∈ a v , new is set to T RU E in step 2.18 or step 2.21 because, either |A i | < |L| and A i L, or A i L, respectively. Consequently, it is shown that the algorithm maintains list a v verifying Definition 1.

Algorithm 2: Function H ←find cliques(G)

Input:

a graph G = (V, E) Output: a graph H = (V, E, A) begin 2.1 find a vertex v ∈ V ; 2.2 set M = N (v); 2.3 G ′ ← (V ′ , E ′ ) : V ′ = V \ v, E ′ = E \ {v × N (v)} ; 2.4 a v ← ∅ ; 2.5 set H ′ ← {∅, ∅, ∅} ; 2.6 if G ′ = ∅ then 2.7 set H ′ ← find cliques(G ′ ) ; 2.8
for each x ∈ M do 2.9

for each set A ∈ a x , or a x = ∅ do 2.10

set new ← F ALSE, and old ← F ALSE ; We continue with the algorithm from the end of the winding phase. Let's start with the unwinding phase when steps 2.31 and 2.32 are executed for the first time: the return of the function will carry a graph with just the vertex v, no edges and a v = ∅ (step 2.5), that is H = ({v}, ∅, a v ). Then, the following instance(s) can add vertices of degree zero until one instance begins to add the first edges (edge), getting a star with leaves (one leaf). At this instance, steps 2.9 and 2.10 are executed and step 2.12 yields L = {v, x} because a x is empty (the vertex x has no registered neighbors until now). Steps 2.13 to 2.26 are executed yielding as result new = T RU E and old = F ALSE because a v = ∅, and getting finally a v = {{v, x}} in step 2.27. Until now, we shown the initial phase, where the first cliques of size 2 are stored.

2.11 if a x = ∅ then L ← (N (v) ∩ A) ∪ v ∪ x else L ← v ∪ x ; 2.12 for each set B ∈ a v , or a v = ∅ do 2.
We continue to treat the case at any instance in the unwinding phase of the Algorithm 1.

To verify property (i) of Definition 2, we need to show that each set A i ∈ a v is a clique. As we shown, the initialization phase let always sets composed by a vertex w and its neighbors z ∈ N (w), which are cliques of size 2. A new joining vertex v neighbor of w will compute the step 2.12 with

a w = ∅ as L ← N (v) ∩ A) ∪ v ∪ w,
where can be A = {w, z}. Thus, if z is also a neighbor of v, the result will be L = {v, w, z}, which is also a clique. In general, if the set A is a clique, then the result of step 2.12 is also a clique because we find the intersection of A with the neighbors of the actual connected vertex v. Then, all the sets A i ∈ a v are always cliques; and as we already shown that they verify the Definition 1, they are also maximal.

To conclude the proof is enough to determine if the property (ii) of Definition 2 is obtained by Algorithm 1. The initial phase was already shown some paragraphs before. steps 2.13 to 2.26 assure that a new clique L that the joining vertex v is stored when it is different to all the previous ones, or any of the previous A i ∈ a v are a subset of L. Then, when the vertex v is reconnected to the graph, all the maximal cliques are computed and stored, and among them the maximum clique that this vertex v belongs to at this time of the algorithm; we call it K max,j (v). Moreover, it is possible that in a later time of the execution of the algorithm, other vertex w found a greater clique containing the last seen K max,j (v), but this clique will be always found because it will stored in the last reconnected vertex belonging to this clique. Therefore, reading all the A i ∈ a v for all vertices the maximum clique is obtained. Time complexity of Algorithm 2. We firstly analyze the cost of the central loop in steps 2.13 to 2.26. Considering that the size of list a v can be bound by the function S(d max ), and the set operations in steps 2.15 and 2.20 are bound by the maximum clique size in v, that is O(d max ), therefore this loop is done in

O(d max • S(d max )).
Then, step 2.12 of Algorithm 1 is an intersection of two sets of size d max (maximum degree), if both are ordered 4 ). Considering graphs with m ≫ n, we can express the n recursions and the visit to all neighbors in loop 2.9 as visiting all the edges, yielding a time complexity of O(m

• d max • S 2 (d max )).
For graphs in general, where d max could be bound by n, time complexity is O(n 3 • S 2 (n)). Now, the main problem is to bound S(d max ) function. In general, this function can be exponential but for some family of graphs it can be polynomial. This last case is observed in most of the graphs issues form the Complex System field, whose have a heavy tailed degree distribution which can be bound by a power law. These last have also the property that m ≪ n 2 , that is they are sparse. As the number of cliques is highly related to the number of triangles in the graph, and this is low because the graph is sparse, the number of cliques per vertex is also low. For these cases, we can model this function as S(d max ) = d α max with α ∈ N. Then, knowing that n

Applications

In this section we illustrate Algorithm 1 and 2 through an implementation developed in python programming language [Alvarez-Hamelin, 2011], and its application to some graphs showing their maximum clique.

Firstly, we apply our algorithm to find cliques to some random graphs defined by [START_REF] Erdös | On random graphs I[END_REF]. It is shown in [Bollobás, 2001, Bollobás and[START_REF] Bollobás | [END_REF]] that an ER random graph has a high probability to contain a clique of size,

(2) r = 2 • log n log 1/p ,
where n is the number of vertices and p is the probability that an edge exist between any pair of vertices. Table 1 show the results, where the columns are: the size of the graph, the probability p, the average degree d, the computed r according to Equation 2, the size found by Equation 1, the number of different cliques (i.e., at least one vertex is different), and if an induced clique of size r + 1 were found. The last column is obtained adding new edges to build a greater clique than the maximum, it shows 'yes' when this clique is found and 'not' if this is not found. Moreover, the 'yes' answer also means that we find just one clique of that size (see the number of clique r find in the original graph). We tested the algorithm on several graphs of each kind, obtaining the same results (excluding |K| which changed some times). We display just one result of each kind.

The result is evident, we always find the predicted maximum clique, even when an artificial one is introduced.

Secondly, we applied our algorithm to a AS Internet graph. This graph has, as main properties, a power law degree distribution and most vertices of low degree are connected to the high degree ones. We used an exploration of [CAIDA, 1998] performed in September 2011. Figure 2 shows a visualization of this map obtained by LaNet-vi [START_REF] Beiró | A low complexity visualization tool that helps to perform complex systems analysis[END_REF]. This visualization is based on k-core decomposition. A k-core is a the maximum induced subgraph such that all vertices have at least k degree [Seidman, 1983, Bollobás, 1984]. LaNet-vi paints each vertex with the rain-bow colors according to its shell index, i.e., the maximum core that a vertex belongs to. It also makes a greedy clique decomposition of the top core, i.e., the core with maximum k, placing each clique in circular sector according to its size.

In this graph Algorithm 2 found a K 29 while LaNet-vi found a K 24 ; this is displayed as the largest circular sector of red vertices in Figure 2. Moreover, this figure shows vertices of the K 29 as those enumerated from 01 to 29. It is possible to appreciate that, even if all vertices are in the top core, the heuristic of LaNet-vi do not find this clique. For cases where some vertex is not at the top core LaNetvi never find the maximum clique. Algorithm 1 runs several times faster than Algorithm 2, but it not always find the maximum clique. For instance, for some starting vertices, Algorithm 1 found a K 27 instead of K 29 .

Comparing the execution time of this graph and a ER graph of the same size, e.g., the same number of edges, we find that AS graph ends quicker than ER graph, since that its degree distribution follows a power law with β ≃ 2.2.

Discussion

As we have already remarked this problem is NP-Complete, and this can be see from the complexity of Algorithm 2, which depends on the number of cliques that a vertex belongs to, or the Algorithm 1 do not find always the maximum clique. However, this papers aims to introduce a new approach to find cliques, that seems to be faster that the classical algorithms.

Figure 1 .

 1 Figure 1. Counterexample for Algorithm 1, which gives one of size 4 instead of 5 when vertices are treated an increasing order.

1β

  as a bound of d max , therefore the complexity yields O(n 1+ 3 β ) for β ≤ 3; and for β > 3 either the search in 1.2 or the elimination in 1.4 dominates, reaching the bound O(n 2 ) or O(n • d max • log d max ) respectively. Storage complexity. It can be computed as the space to storage graph G, which is O(n • d max ), and the space occupied by all the a x v sets. This last quantity can be computed as the length of each set a x v , which is bound by d max and the number of them per vertex, which is also bound by d max , yielding O(d 2 max ) per vertex. Thus, the total storage complexity is O(n • d 2 max ).

1β

  is a bound of d max , therefore the complexity yields O(n 1+ 2+α β ) for β ≤ 2 + α; and for β > 2 + α the elimination in 1.4 can dominate, reaching the bound O(n 2 ) or O(n • d max • log d max ). Storage complexity. It can be computed as the space to storage graph G, which is O(n•d max ), and the space occupied by all the a v list of sets. This last quantity can be computed as the length of each set in a v , which is bound by S(d max ). Thus, counting d max neighbors, the total storage complexity is O(n • d max • max(d max , S(d max ))).

Figure 2 .

 2 Figure 2. Visualization of AS Internet map by LaNet-vi. Vertices in the maximum clique are labeled with numbers.

  taking O(d max ). Next, we consider loop 2.10, taking S(d max ) times because |a x | ≤ S(d max ), giving O(d max • S 2 (d max )). Loop 2.9 on vertex v neighbors takes an extra d max times, giving O(d 2 max •S 2 (d max )). Step 2.2 has a complexity of O(1). Finally, the recursion is done for each vertex of the graph, producing a total time complexity of O(n•d 2 max •S 2 (d max )

Table 1 .

 1 Maximum cliques in Erdös Renyi graphs.

	n	p	d	r |Kmax| |K| : K = {K ∈ Kmax} induced r + 1
	100	0.01	1	2	2	60	yes
	1000	0.01	10	3	3	159	yes
	10000	0.01	100	4	4	372	yes
	10000 0.04642 464.2 6	6	5	yes

Graphs having a heavy tailed degree distribution which can be bound by a power law.

This can be done at the beginning for all vertices in graph G, taking O(n • dmax • log(dmax)).

Most of the real problems in Complex Systems field has 2 ≤ β ≤ 3.

This can be done at the beginning for all vertices in graph G, taking O(n • dmax • log(dmax)).


Anna S Roettger
email: anna.roettger@fli.bund.de

Ingrid Halle

Hubertus Wagner

Gerhard Breves

Gerhard Flachowsky

The effect of various iodine supplementations and two different sources on performance and iodine concentrations in different tissues of broilers

Keywords: Broilers, Minerals, Nutrition

come

The effect of various iodine supplementations and two different iodine sources on performance and iodine concentrations in different tissues of broilers

INTRODUCTION 18

Iodine (I) deficiency is still a widely prevalent problem in human nutrition around the world, 19 approximately 57 % of the European population and 35 % of the world's population having 20 insufficient iodine intake (urinary iodine < 100 µg/l; de [START_REF] De Benoist | Iodine status worldwide[END_REF]. I is an essential 21 trace element for humans and animals, being required for the synthesis of the thyroid 22 hormones triiodothyronine (T 3 ) and tetraiodothyronine (T 4 ). The appropriate I provision is 23 important for a normal thyroid function and the associated physiological mechanisms. For an 24 adult person, the D-A-CH (German-, Austrian-and Swiss Nutrition Society) recommends a 25 -26 CH, 2008). I is assigned to the 'high risk category' and also to 'supply category 1'. This means 27 there is only a narrow range between recommended daily intake and UL (≈ 1:3) (Gaßmann, 28 2006). Due to adverse health effects, deficiency as well as excessive intake should be avoided. 29

A lack in I supply impairs thyroid hormone synthesis, which results in hypothyroidism and 30 leads to various developmental and functional disorders, summarised as 'Iodine Deficiency 31 Disorders' (IDD) [START_REF] Hetzel | Iodine deficiency disorders (IDD) and their eradication[END_REF][START_REF] De Benoist | Iodine status worldwide[END_REF]. IDD can affect the organism in all 32 stages of life: foetus (e.g., abortions, endemic cretinism), neonate (e.g., endemic mental 33 retardation), child and adolescent (e.g., goitre, retarded physical development) and adult (e.g., 34 hypothyroidism, spontaneous hyperthyroidism in the elderly) [START_REF] Hetzel | Iodine deficiency disorders (IDD) and their eradication[END_REF]Stanbury et al., 35 1998;[START_REF] Laurberg | Thyroid disorders in mild iodine deficiency[END_REF]. 36

To counteract I deficiency, salt iodination was implemented, but this did not solve the 37 problem to a satisfactory extent, so further I sources were sought. Products of animal origin, 38 such as milk or eggs, can, for example, be enriched by feed iodination. Feed iodination is 39 limited by EU regulations to avoid I excess, the maximum allowance of feed iodination for 40 broiler chickens being 10 ppm (EU Commission, 2005). Due to high carry-over into milk and 41 eggs, the UL for laying hens and dairy cows was reduced from 10 ppm to 5 ppm (EU 42 Commission, 2005). The I requirements of animals depend on species, age and physiological 43 stage. The maximum allowance for broiler feed of 10 mg I/kg compares with a requirement of 44 0.35 mg I/kg feed for broilers (NRC, 1994). Thus health and performance of the animals have 45 to be considered at the intention of feed iodination and possible elevation of the legal feed 46

UL. 47

The present study encompassed two feeding experiments with broilers. A range of I 48 supplementations and two different I sources were tested to investigate the influence on I 49 concentrations of different tissues and the impact on growth and slaughter performance. 50 Souci et 51 al. (2008); this shows the necessity of determining these data. The motivation for this study 52 was a document of the European Food Safety Authority (2005) (EFSA) about the use of I in 53 feeding stuffs, which concluded that more dose-response studies were necessary to assess the 54 resulting I content in food of animal origin. The experiments were conducted to investigate 55 the impact of broiler feed iodination on the I concentrations of tissues, organs and blood 56 serum. The main interest of the study was to determine to what extent chicken meat can be 57 enriched with I when broilers ingest feed supplied with I according to EU guidelines. As 58 different I sources are permitted as feed additives in the EU (EU Commission, 2005), two of 59 them were tested -potassium iodide (KI) and calcium iodate (Ca(IO 3 ) 2 ). Growth and 60 slaughter performance were determined in these feeding experiments to evaluate the tolerance 61 of the broilers towards different I concentrations. 62

MATERIALS AND METHODS 63

Animals, diets and sampling 64

Two experimental approaches were conducted to test the two different I sources -iodide (KI) 65 and iodate (Ca(IO 3 ) 2 ). Both experiments were conducted at the same experimental station 66 (Friedrich-Loeffler-Institute, Institute of Animal Welfare and Animal Husbandry, Celle, 67 Germany) under identical experimental conditions. The experimental approach testing KI was 68 conducted in April/May 2007; the approach testing Ca(IO 3 ) 2 was carried out in March/April 69

70

Each of the two experimental trials was performed as follows. Ten Ross 308 broiler 71 breeder hatching eggs which had been produced under standard management conditions were 72 used to determine the initial I concentration. On the day of hatch, 288 male chicks were 73 divided into 4 groups (72 per group) and a further 10 chicks killed for I determination. The 74 individually at 14, 21 and 35 d, and the data used to calculate the feed conversion ratio (FCR). 79 KI and Ca(IO 3 ) 2 were tested as different I sources in a range of concentrations (0 80 (control), 1, 2.5 and 5 mg I/kg feed). The feed for both experiments was mixed at the same 81

time. An I-free mineral premix was used for the composition of the feed mixtures. Pre-82 mixtures were produced to achieve a homologous dispersion of I in the feed. In a first step, 83 the calculated I amounts were applied to 70 g ground wheat, then 70 g were mixed into 2 kg 84 ground wheat, and finally this pre-mixture was mixed into the 300-kg feed mixture. The 85 control group were given the same basic diet as the test groups but without additional I; these 86 birds only received naturally occurring I from the feed ingredients. Only I concentrations of 87 up to 5 mg I/kg were tested, despite 10 mg I/kg being allowed for growing birds, because 88 other experiments conducted with laying hens used 5 mg I/kg. 89

On d 35, one bird from each cage with a BW close to the mean of the respective cage-90 group was slaughtered by electrical stunning and severance of the carotid artery and jugular 91 vein according to welfare regulations [START_REF] Eu Commission | Proposal for a council regulation on the protection of animals at 344 the time of killing[END_REF] Feed analyses were performed of the feed mixtures from each experimental group. Dry 99 matter, crude fibre, crude ash, crude protein (CP), crude fat (EE), starch (ST), sucrose (S), 100 NDF, ADF and Ca were analysed according to the guidelines of "Verband Deutscher 101 Landwirtschaftlicher Untersuchungs-und Forschungsanstalten" (VDLUFA, 1976). 102

I analyses 103

The I contents were measured using Inductively Coupled Plasma-Mass Spectrometry (ICP-104 MS) [Agilent 7500C]. The measuring solutions were prepared in an alkaline medium in order 105 to avoid vaporisation of I (Wagner, 2007). Feed samples (5 g) were boiled for 30 min with 106 ammonia solution (0.59 mol/l). After cooling, samples were filled with ammonia solution up 107 to 1000 ml, and then filtered with a fluted filter. Prior to use, filters were flooded twice with 108 boiling distilled deionised water and then dried in an oven to remove possible I and avoid 109 contamination. After filtration with a syringe filter (Minisart 0.45µm), 10 ml of the filtrate 110 were taken for measurements. Blood serum was diluted 1:10 with 0.11 mol/l 111 Tetramethylammonium hydroxide solution [TMAH ,Alfa Aesar GmbH & Co KG Karlsruhe,112 25 % w/w aq.soln., Electronic Grade, 99.9 %] before measuring. Meat, liver, 1-d-old chicks 113 and egg samples were each homogenised with a mixer [ESGE-ZauberStab®] prior to 114 digestion, with the exception of the thyroid gland, which was taken in whole due to its small 115 size. Therefore, the thyroid weight was included into the calculation for its I concentration. 116

After mechanical homogenisation, meat, liver, eggs and 1-d-old chicks were digested 117 chemically with TMAH [START_REF] Fecher | Determination of iodine in 346 food samples by inductively coupled plasma mass spectrometry after alkaline extraction[END_REF] and a 1-g sample heated for 3 h at 90°C in a gas-118 tight Erlenmeyer flask, with 5 ml distilled deionised water and 1 ml TMAH. After cooling, 119 the solution was filled up to 25 ml with distilled deionised water. 10 ml of the solution were 120 taken for measurement using ICP-MS, 0.5 ml tellurium oxide solution [100 µg/l; tellurium 121 (IV) oxide, Alfa Aesar GmbH & Co KG Karlsruhe, 99.9 %] as the internal standard. The 122

Statistical analyses and calculations 125

The carryover was calculated as the percentage of I content of the sample from the total 126 amount of I which was ingested during the whole experiment (35 d). Nitrogen-corrected 127 apparent metabolisable energy, was calculated with the energy estimation formula: 128 AME N [MJ/kg] = (15.51 g CP/kg + 34.31 g EE/kg + 16.69 g ST/kg + 13.01 g S/kg)/1000 129 (WPSA, 1984[START_REF] Gfe | Empfehlungen zur Energie-und Nährstoffversorgung der Legehennen und 354 Masthühner[END_REF]. 130

The statistical analyses were carried out with SAS 9.1 (© by SAS Institute Inc., Cary, 131 NC, USA) and STATISTICA 8.0 (StatSoft, Inc. 2007). Differences were recognised as 132 significant at P < 0.05. Since the two experiments were carried out at two different points in 133 time, the effects of iodate and iodide were not compared with each other. Outliers were 134 detected with the ROBUSTREG procedure and then excluded from subsequent analysis. 135 Afterwards, data were tested for normality with the procedure UNIVARIATE, using the 136 Kolmogorov-Smirnov test (P = 0.01). Performance and slaughter data were normally 137 distributed and thus analysed parametrically with the Tukey test using the GLM procedure. 138

The data of I concentrations showed significant differences from the normal distribution. 139

These not normally distributed data were analysed non-parametrically with the Kruskal-140 Wallis test using STATISTICA. 141 RESULTS 142

Feed I concentrations 143

The naturally occurring I concentration of the feed mixtures was 0.5 mg I/kg in the control 144 diet of both experiments. The I concentrations of the groups supplemented with 1, 2.5 and 145 5 mg I/kg approximated to the desired concentrations of 0.6, 2.4 and 5.5 mg I/kg respectively 146 in KI groups and 0.9, 2.7 and 4.7 mg I/kg in Ca(IO 3 ) 2 groups. 147

Rearing performance 148

The I supplementation had no significant effect on FI and BW gain (BWG) (Tables 2 and3). 149

The mean FI of all groups and total experimental period was 98.2±3.3 g/d in KI groups and 150 97.2±3.6 g/d in Ca(IO 3 ) 2 groups. The mean BWG was 65.1±2.2 g/d in KI groups and 151 67.3±2.4 g/d in Ca(IO 3 ) 2 groups. 152

The FCR of KI-birds was significantly improved between 21 and 35 d, where the 153 5 mg I/kg supplemented group had a significantly superior FCR to the control group 154

(1.61 kg/kg vs. 1.68 kg/kg). There was no significant effect on FCR during the other phases 155 (Table 2). When testing FCR through to 35 d, the FCR of the 5 mg I/kg (1.49 kg/kg) was also 156 significantly higher than in the control (1.53 kg/kg; P = 0.0071). In the Ca(IO 3 ) 2 broilers no 157 significant differences were found between the different groups, the average FCR of all 158 Ca(IO 3 ) 2 groups was 1.45±0.04 kg/kg. In contrast to KI groups the highest supplemented 159 group had in tendency a lower FCR than the control group. The significant positive effect in 160 FCR, found in the 5 mg I/kg KI group was caused by negligiblly low standard deviation and 161

was not considered to have biological relevance. 162

Slaughtering performance 163

The data retrieved at slaughtering are listed in Tables 4 and5. The final BW was not 164 significantly affected by the various I concentrations, neither with KI supplementation 165 (2230±87 g) nor with Ca(IO 3 ) 2 supplementation (2303±58 g) birds. 166 No significant differences were found in the mass of the different organs and fractions 167 or in their proportions of final BW as influenced by I supplementation. Considering the 168 masses of samples gained from KI-and Ca(IO 3 ) 2 -supplemented birds, the mean weight of 169 pectoral meat was 340.1 g and 371.1 g, of breast skin 26.2 g and 29.7 g and thigh mass with 170 bone and skin 440.5 g and 472.0 g respectively. In the other fractions and organs, mean 171 weights were: heart 10.6 g and 11.5 g, liver 47.0 g and 44.9 g, spleen 1.93 g and 2.19 g, 172 Ca(IO 3 ) 2 supplementation. The thyroid gland had a mean weight of 0.15±0.04 g in KI and 174 0.19±0.08 g in Ca(IO 3 ) 2 broilers. 175

I concentrations 176

The hatching eggs (n = 10) had I concentrations of 687±170 µg I/kg in the KI experiment and 177 484±82 µg I/kg in the Ca(IO 3 ) 2 experiment. The 1-d-old chicks had concentrations of 178 749±107 µg I/kg in the KI experiment and 565±148 µg I/kg in the Ca(IO 3 ) 2 experiment. I 179 content was less than 1 % of the final I concentration found in the slaughtered birds at the end 180 of the experiment, and so the initial I content was ignored in carry-over calculations. 181

Increasing I supplementation significantly raised the I concentration in all investigated 182 tissues and blood serum of the birds (Tables 6 and7). The order of I concentrations in the 183 different investigated samples was meat < liver < serum < thyroid gland. 184 I concentration of the thyroid was independent of feed I supplementation, the highest 185 of the various samples, and increased significantly with rising feed I concentration. In KI 186 groups, it ranged from 2317 to 5053 µg I/g and in Ca(IO 3 ) 2 groups from 2323 to 3652 µg I/g. 187 Calculated on the weight of the thyroid gland, the I content of one thyroid gland of the highest 188 KI supplemented group had a mean I content of 825 ± 373 µg and in the highest Ca(IO 3 ) 2 189 groups the I content of one thyroid gland was 601 ± 197 µg. The carry over of I into the 190 thyroid was higher than the other samples. Regarding both I sources, the carry-over decreased 191 with increasing feed I concentration, the median amount in the control group of KI birds was 192 18.0 % and 21.8 % in Ca(IO 3 ) 2 birds. In the highest KI group, the median carry over into the 193 thyroid was 4.2 % and, in the highest Ca(IO 3 ) 2 group, 3.5 %. 194

In comparison with liver and meat samples, the I concentration of blood serum was 195 relatively high. The I concentration in the control groups was 29 µg I/l (KI groups) and 196 The results show that the I concentration of liver was at a higher level than meat and 199 also increased significantly with increasing feed I concentrations. I concentrations of liver 200 ranged from 22.4 to 173.4 µg I/kg in KI groups and from 28.1 to 181.3 µg I/kg in Ca(IO 3 ) 2 201

groups. 202

The transfer into the meat (pectoral) was below 0.17 ± 0.04 %. Hence the total I 203 content of meat was low compared with the other tissues investigated. The I concentrations of 204 pectoral and thigh meat were similar and increased significantly with increasing I content of 205 the feed. In KI groups, the I concentrations of pectoral meat ranged from 5.8 µg I/kg for the 206 control to 58.0 µg I/kg for the highest group and from 5.9 µg I/kg to 63.1 µg I/kg respectively 207 in thigh meat. Breast meat of Ca(IO 3 ) 2 groups contained between 7.0 µg I/kg in the control 208 and 52.1 µg I/kg in the highest supplemented group, and correspondingly 12.2 µg I/kg to 209 67.8 µg I/kg in thigh meat. 210 DISCUSSION 211

Feed I concentrations 212

The control feed met the I requirements of the broiler chickens (NRC, 1994). Since an I-free 213 premix was used this relatively high naturally occurring concentration must have been due to 214 feed ingredients. 215

Growing performance 216

The results regarding growing performance agree with previously published studies, which 217 found no significant impact on FI, BWG or FCR when similar feed I concentrations were 218 supplied [START_REF] Perry | Iodine supplementation from 379 two sources and its effect on egg output[END_REF][START_REF] Kaufmann | Iodine supplementation in chicken, pig and cow 364 feed[END_REF]Maroufyan and Kermanshahi, 219 2006). These results show that the I concentrations used in the current study met the 220 requirements and did not exceed a critical level for the broilers.

Slaughtering performance 223

The results of the slaughtering performance are in accordance with previous studies, which 224 investigated the impacts of I supply at concentrations up to 10 mg I/kg and found no 225 significant effect [START_REF] Kaufmann | Iodine supplementation in chicken, pig and cow 364 feed[END_REF][START_REF] Maroufyan | Effect of different levels of rapeseed 371 meal supplemented with calcium iodate on performance, some carcass traits, and thyroid 372 hormones of broiler chickens[END_REF]. The 226 effect on thyroid weight observed in present study confirms the results of Travnicek et al. 227 (1999) who investigated the influence of dietary I concentrations between 0.3 (control) and 228 15 mg I/kg on histometric parameters and thyroid gland weight in layers over a 74-d period. 229

They found a decrease in the height of follicular cell-thyreocytes with increasing feed I 230 content, but no significant impact on thyroid weight. The present experiment showed that I 231 applications at moderate amounts below EU-regulations did not significantly affect the weight 232 of the thyroid gland. Further studies of the impact of I on thyroid glands of growing chickens, 233 histological investigations should be carried out to identify possible hyperplasia or 234 hypertrophy of the thyroidal follicle cells. 235

I concentrations 236

According to previous studies on egg I contents, the I content of the hatching eggs 237 corresponded with concentrations of eggs from laying hens provided with I concentrations of 238 approximately 1.6 mg I/kg [START_REF] Roettger | Carry over of 381 nutrient iodine in laying hens -a new possibility to improve human iodine supply? World[END_REF]. 239

The relatively high I concentrations in the blood serum can be explained by the intense 240 absorption of iodide in the small intestines, which occurs after ingestion and the reduction of 241 free I and iodate into iodide [START_REF] Lewis | Responses of domestic fowl to excess iodine: a review[END_REF]. Subsequently, iodide enters the extracellular fluid 242 which includes blood plasma and interstitial fluid. 243

From the extracellular compartment, the sodium/iodide-symporter (NIS) actively 244 transports I into the thyroid gland. The concentration gradient between plasma and thyroid 245 can be 1 to 100 or more. The high I concentration measured in thyroid samples is enabled by 246 the active NIS-transport and is necessary for the thyroid function as an I reservoir and 247 There are no data for I concentration in practical broiler diets. Grünewald et al. (2006) 250 investigated the actual I concentration of laying hen feed, which meets a mean I concentration 251 of 1.09 mg/kg (min/max: 0.5/ .16 mg I/kg); this corresponds with the I content supplied in 252 group 2 (1 mg I/kg) of the present study. Based on the data from this study, and at typical 253 dietary I inclusions, the I content of a thyroid gland would amount to 492 ± 127 µg with KI 254 supplementation and 562 ± 395 µg with Ca(IO 3 ) 2 supplementation. This exceeds the 255 recommended 200-mg daily I intake and agrees with the UL for an adult human suggested by 256 the D-A-CH. However, the risk for an oversupply through a consumption of the thyroid gland 257 remains minimal, because the thyroid is usually removed with oesophagus and trachea during 258 processing. 259

Thyroid hormones and I enter the liver via the venae portae. The liver is an important 260 location for deiodination of T 4 . If the outer ring is deiodinated, T 3 is generated, and 261 deiodination of the inner ring rT 3 is produced (Visser et al., 1988). Because of this, a greater 262 accumulation of I was assumed for that tissue compared with meat. [START_REF] Groppel | Iodanreicherung in Organen und 356 Geweben von Mastküken nach Iodsupplementation des Futters[END_REF] and 263 [START_REF] Kaufmann | Iodine supplementation in chicken, pig and cow 364 feed[END_REF] investigated the influence of KI and KIO 3 supplementation 264 on the I content in tissues of male broiler chickens. In liver samples of the 1 mg I/kg 265 supplemented group they found 71.0 µg I/kg dry matter, assuming a 70.3 % water content for 266 the liver [START_REF] Souci | Food Composition and Nutrition 388 Tables[END_REF], which amounts to 21.1 µg I/kg fresh matter. In the 0.9 mg I/kg 267 supplemented Ca(IO 3 ) 2 group of the present study, approximately double this concentration 268 (44.1 µg I/kg) was measured. Furthermore, in 10 mg I/kg-groups, Souci et al. found the same 269 or only the 1.5 fold amount of the concentration found in this study at 5 mg supplementation, 270 but this difference partly lay within the range of standard deviation. However, Stibilj and 271 Holcman (2002) detected 31.7 µg I/kg with a supplementation of 0.66 mg I/kg in laying-hen 272 pectoral), this meat would contribute with 0.5 µg I/d to the daily I uptake in consumers. If the 299 feed was enriched with 5 mg I/kg (median concentration of pectoral and thigh meat: 300 60.2 µg I/kg), the daily contribution would amount to 3.0 µg I/d. In comparison with the 301 recommended daily I intake of 200 µg for adults, it becomes obvious that meat of broiler 302 chickens fed on EU guidelines is not a considerable I source. However, the European 303 guidelines should not be elevated above 10 mg I/kg feed, because the meat I concentration 304

will not be remarkably elevated therewith, but high I doses could have possible adverse 305 effects on broiler health and performance. A reduction of feed UL is not recommended as 306 well because, at current levels, there is no risk for consumers due to the low carry-over into 307 the broiler meat, and furthermore the UL for feed iodisation is not exhausted in practice 308 [START_REF] Grünewald | Praxiserhebungen zum 359 Jodgehalt im Mischfutter[END_REF]. It can be positively reviewed that the consumption of chicken meat 309 would not hold the risk for an over-dosage, if thyroid tissue is excluded from comestible 310 production. 311

Conclusion.

The study showed that neither of the two I sources at the applied concentrations 312 had a significant impact on growing and slaughtering performance. The investigations show 313 that the carry-over and accumulation into meat is marginal; thus, the contribution to the I 314 supply of consumers is considered as minor. The thyroid gland should be removed at 315 slaughtering to prevent over-consumption. However, the European guidelines on UL for 316 broiler feed should be retained because feed I concentration does not greatly influence the 317 carryover into meat. An elevation could lead to possible adverse effects on health and 318 performance of the birds, but a reduction is also not necessary because in practice the UL for 319 feed iodisation is not exhausted. 320 Detailed studies concerning I metabolism and I distribution in different tissues in 321 chicken are still lacking. Therefore more holistic studies with regard to carry-over into eggs, 322 Arbeitstagung 395 Mengen-und Spurenelemente, Jena. 396 TRAVNICEK, J., KROUPOVA, V., KRATOCHVÍL, P. & KRABACOVA, I. (1999) The 397 effect of superfluous iodine intake and strumigenic factors on histometric parameters of 398 thyroid gland in layers. Veterinarni Medicina, 44: 177-182. 399 VDLUFA (1976)

  180-200 µg I and a 'Tolerable Upper Intake Level' (UL) of 500 µg I/d (D-A

  I content of poultry meat are absent from the nutrition table of

  for I and 125 for Te were used. During calculation of I concentrations, the 123 standard addition method was applied during calibration. 124

  7 g and 27.7 g and abdominal fat pad 33.5 g and 30.0 g respectively for KI and 173

  /l (Ca(IO 3 ) 2 groups) and increased to 382 µg I/l (KI groups) and 350 µg I/l (Ca(IO 3 ) 2 197 groups) in the highest supplemented groups. 198

  the synthesis of thyroid hormones. The lower carry-over in high 248 supplemented groups is caused by saturation, which occurs above a certain point of provision. 249

  agrees well with the data gained in this study; in comparable groups, KI birds 273 showed 31.0 and Ca(IO 3 ) 2 birds 28.1 µg I/kg. 274There are very few studies on the I concentration of fowl meat, in response to 275 moderate I supplementation. In studies of I concentration in the tissues of male broiler a median measured I concentration of 10.6 µg I/kg in broiler meat (thigh and 298

  , blood serum, organs (especially thyroid gland) and the loss via 323 excrements, as well as studies on long term effects of I supply, are necessary. 324 ACKNOWLEDGEMENT 325 The Federal Ministry of Food, Agriculture and Consumer Protection (BMELV), Germany 326 supported this work financially. The technical assistance of the co-workers of the 327 Max Rubner-Institute in Kulmbach and the Friedrich-Loeffler-Institute in Celle and 328 Braunschweig, in supporting the procedure of the experiments and analysis, . & HOLCMAN, A. (2002) Se and I content in eggs and tissues of hens from 394 farmyard rearing. Arbeitstagung

Table 1

 1 group were allocated to 6 cages (1.28 x 0.82 m) with 12 birds (11.4 chickens/m²) 75 per cage and reared to 35 d under standard management conditions. Feed and water were 76 provided ad libitum. The feed composition is detailed in Table1. Feed intake (FI) was 77 recorded by cage weekly and body weight (BW) measured on a cage basis at 7 d and 78

	near here

Table 1 .

 1 Composition of the diets for KI and Ca(IO 3 ) 2 supplemented groups, without I 407

	400	Die chemische Untersuchung von Futtermitteln, Methodenbuch, Vol. III
	401	(Darmstadt, VDLUFA-Verlag).
	402	VISSER, T. J., KAPTEIN, E., TERPSTRA, O. T. & KRENNING, E. P. (1988) Deiodination
	403	of thyroid hormone by human liver. Journal of Clinical Endocrinology and Metabolism, 67:
	404	17-24.
	405	WAGNER, H. (2007) Der Beitrag von Fleischprodukten zur Iodversorgung der Bevölkerung.
	406	Fleischwirtschaft, 7: 92-95.

Table 2 .

 2 Mean ± SD feed intake (FI), body weight gain(BWG) and feed conversion

	417
	418	ratio(FCR) of broilers fed diets supplemented with KI to give I concentrations of 0, 1, 2.5 or 5
	419		mg/kg (n = 6)
		Iodine
		supplementation	FI (g/d)	BWG (g/d)	FCR (kg/kg)
		(mg I /kg feed)
		0 -14 d
		0	39.3 ± 1.5 30.9 ± 1.7	1.25 ± 0.02
		1.0	37.8 ± 1.2 30.4 ± 1.6	1.24 ± 0.03
		2.5	39.3 ± 0.5 31.2 ± 0.7	1.26 ± 0.02
		5.0	38.1 ± 1.2 30.5 ± 1.6	1.25 ± 0.03
		14 -21 d
		0	99.0 ± 3.6 72.0 ± 2.2	1.37 ± 0.04
		1.0	97.5 ± 3.0 71.7 ± 2.1	1.36 ± 0.01
		2.5	101.0 ± 2.8 72.9 ± 3.0	1.39 ± 0.06
		5.0	97.9 ± 2.9 71.2 ± 2.2	1.38 ± 0.01
		21 -35 d
		0	156.9 ± 5.6 93.3 ± 3.9	1.68 a ± 0.03
		1.0	154.9 ± 5.6 94.3 ± 4.6 1.64 ab ± 0.04
		2.5	160.5 ± 7.4 98.8 ± 5.2 1.63 ab ± 0.04
		5.0	157.4 ± 6.0 97.7 ± 3.2	1.61 b ± 0.05
		0 -35 d
		0	98.3 ± 3.4 64.1 ± 2.3	1.53 a ± 0.02
		1.0	96.5 ± 2.6 64.2 ± 2.1 1.50 ab ± 0.01
		2.5	100.1 ± 3.5 66.6 ± 2.3 1.50 ab ± 0.02
		5.0	97.7 ± 3.2 65.5 ± 1.7	1.49 b ± 0.02
	420	a,b Within a column and time period, means without a common superscript are significantly
	421	different at P <0.05.
	422

Table 3 .

 3 Mean ± SD feed intake (FI), body weight gain(BWG) and feed conversion ratio

	423
	424	(FCR) of broilers fed diets supplemented with Ca(IO 3 ) 2 to give I concentrations of 0, 1, 2.5 or
	425		5 mg/kg (n=6)
		Iodine
		supplementation	FI (g/d)	BWG (g/d)	FCR (kg/kg)
		(mg I /kg feed)
		0 -14 d
		0	36.9 ± 1.0	29.8 ± 0.9 1.24 ± 0.01
		1.0	36.9 ± 1.5	30.0 ± 1.5 1.23 ± 0.03
		2.5	37.2 ± 1.7	30.3 ± 1.5 1.23 ± 0.02
		5.0	36.9 ± 0.8	30.1 ± 0.7 1.23 ± 0.01
		14 -21 d
		0	94.2 ± 4.0	70.6 ± 3.2 1.33 ± 0.04
		1.0	95.4 ± 4.3	70.2 ± 3.7 1.36 ± 0.02
		2.5	94.4 ± 5.0	69.7 ± 4.0 1.35 ± 0.03
		5.0	93.6 ± 2.1	67.9 ± 2.3 1.38 ± 0.02
		21 -35 d
		0	163.6 ± 6.2 107.2 ± 4.0 1.53 ± 0.04
		1.0	159.6 ± 7.3 102.8 ± 5.6 1.55 ± 0.06
		2.5	160.0 ± 5.5 102.7 ± 2.8 1.56 ± 0.05
		5.0	158.0 ± 5.9 100.6 ± 5.2 1.57 ± 0.04
		0 -35 d
		0	98.1 ± 4.7	68.9 ± 1.9 1.42 ± 0.04
		1.0	97.3 ± 3.8	67.2 ± 2.9 1.45 ± 0.04
		2.5	97.4 ± 3.6	67.1 ± 2.0 1.45 ± 0.03
		5.0	95.9 ± 2.8	65.8 ± 2.1 1.46 ± 0.02
	426

Table 4 .

 4 Mean ± SD organ and fraction weights of broilers fed diets supplemented with KI to 427give I concentrations of 0, 1, 2.5 or 5 mg/kg (n = 6) 428

			Iodine supplementation (mg/kg)
		0	1.0	2.5	5.0
	Final body weight (g) 2211.2 ± 120.4 2184.7 ± 53.4 2226.8 ± 49.2 2296.0 ± 82.3
	Pectoral meat (g)	349.5 ± 40.6	323.0 ± 16.2	341.8 ± 30.1	347.1 ± 13.6
	Breast skin (g)	28.5 ± 6.4	25.4 ± 3.3	25.7 ± 3.7	25.1 ± 3.8
	Thigh (g)	437.9 ± 18.7	439.1 ± 11.8	439.9 ± 33.9	445.6 ± 13.6
	Heart (g)	11.1 ± 2.3	11.0 ± 1.3	9.6 ± 0.3	10.8 ± 1.4
	Liver (g)	46.7 ± 3.7	44.4 ± 2.6	47.2 ± 1.2	49.5 ± 4.5
	Spleen (g)	1.95 ± 0.48	1.74 ± 0.11	1.95 ± 0.32	2.07 ± 0.33
	Ventriculus (g)	28.8 ± 7.2	24.2 ± 4.2	22.2 ± 4.7	23.7 ± 4.5
	Abdominal fat pad (g)	27.0 ± 11.3	32.9 ± 6.9	34.3 ± 6.9	38.7 ± 6.2
	Thyroid gland (g)	0.14 ± 0.03	0.15 ± 0.03	0.17 ± 0.06	0.16 ± 0.05

Table 5 .

 5 Mean ± SD organ and fraction weights of broilers fed diets supplemented with 429Ca(IO 3 ) 2 to give I concentrations of 0, 1, 2.5 or 5 mg/kg (n = 6) 430

		Iodine supplementation (mg/kg)
	0	1.0	2.5	5.0

Table 6 .

 6 I concentration of samples collected from broilers fed diets supplemented with KI to 431give I concentrations of 0, 1, 2.5 or 5 mg/kg (median, Q25, Q75; n=6) 432 Within a row, means without a common superscript are significantly different at P <0.05. 433

				Iodine supplementation (mg/kg)
			0	1.0	2.5	5.0
	Pectoral meat	(µg/kg) 5.8 c 5.0 7.0 10.3 bc 9.1 10.4 39.3 ab 37.1 42.9 58.0 a 52.3 59.5
	Thigh meat	(µg/kg) 5.9 b 5.5 7.3	6.9 b 6.1 9.4 38.6 ab 32.3 44.8 63.1 a 58.2 66.1
	Liver	(µg/kg) 22.4 c 20.0 24.7 31.0 bc 27.2 35.4 103.6 ab 97.8 131.9 173.4 a 163.8 197.1
	Blood serum	(µg/l)	29.1 c 27.2 30.3 50.0 bc 41.9 68.5 226.4 ab 221.5 305.6 382.4 a 366.3 430.8
	Thyroid gland	(µg/g) 2317 b 1991 2385 3315 ab 3071 3448 4599 a 4304 4812 5053 a 4446 5899
	a,b,c

Table 7 .

 7 I concentration of samples collected from broilers fed diets supplemented with 434 Ca(IO 3 ) 2 to give I concentrations of 0, 1, 2.5 or 5 mg/kg (median, Q25, Q75; n=6) 435 ) 12.2 b 11.3 16.7 22.9 b 12.8 28.8 37.7 ab 35.6 41.1 67.8 a 62.9 78.4 Liver (µg/kg) 28.1 c 27.5 32.1 44.1 bc 41.9 47.6 104.5 ab 97.7 112.0 181.3 a 175.0 198.7 Blood serum (µg/l) 28.2 c 23.8 34.1 50.5 bc 48.0 68.9 179.4 ab 172.9 198.9 349.9 a 318.5 386.5 Within a row, means without a common superscript are significantly different at P <0.05. 436 437

			Iodine supplementation (mg/kg)
		0	1.0	2.5	5.0
	Pectoral meat	(µg/kg) 7.0 c 6.3 8.7 11.5 bc 10.6 12.7 27.9 ab 25.1 30.8 52.1 a 48.5 54.9
	Thigh meat (µg/kgThyroid gland (µg/g) 2323 b 1927 2723 3170 ab 2960 3517 3117 ab 2740 4206 3652 a 3154 3840
	a,b,c

E-mail: br.poultsci@bbsrc.ac.uk URL: http://mc.manuscriptcentral.com/cbps British Poultry Science