Stamatina Anastopoulou

Mike Sharples

Sharon Ainsworth

Charles Crook

Claire O'malley

Michael Wright

Creating personal meaning through technology-supported science inquiry learning across

Keywords: inquiry-based learning, technology enhanced learning, design study inquiry-based teaching, science education, secondary school

come

Introduction

We live in an era when many important aspects of scientific knowledge are challenged. Not only are there well publicised arguments against grand theories, such as species evolution, but the scientific basis of everyday life is constantly being undermined.

For example, recent articles in UK national newspapers have cited scientific evidence to assert that taking exercise makes you fat (Daily Telegraph, August 25th, 2009) and that using Facebook can raise your risk of cancer (Daily Mail, February 19 th , 2009). When the Internet can provide easy access to entertaining conspiracy theories and pseudo-scientific articles there is more need than ever to enable young people to engage in rational scientific discourse and practices.

Yet it is clear that young learners are not easily attracted to science as a school subject (Lyons, 2006a,b). Learners can be challenged by the modes of thinking that science investigation requires but it is now widely acknowledged that the barrier to engagement goes further than a struggle with cognitive demands such as 'control of variables' [START_REF] Kuhn | Beyond control of variables: What needs to develop to achieve skilled scientific thinking[END_REF]. The teaching of science must also create an experience of inquiry and interpretation that is inspirationally authentic. As Hodson has expressed it: 'Becoming scientifically capable involves considerably more than acquisition of scientific skills, knowledge and understanding. It also involves the development of personal qualities and attitudes, the formulation of one's own views on a wide range of issues that have a scientific and/or technological dimension and the establishment of an underlying value position' (Hodson, 1998, p. 3). One route to approach this challenge is for science education to embrace more firmly the traditions of inquiry learning, where inquiry is extended beyond a classroom, and teachers may need Personal inquiry learning 2 to revise their understanding of the content and pedagogy of inquiry. As collaboration between teacher and students enhances inquiry, greater levels of involvement are required by teachers than in traditional settings [START_REF] Crawford | Embracing the essence of inquiry: new roles for science teachers[END_REF].

Learning science by inquiry can help students to learn how to think like scientists [START_REF] Edelson | Addressing the challenges of inquiry-based learning through technology and curriculum design[END_REF], do real science [START_REF] De | Computer simulations -Technological advances in inquiry learning[END_REF][START_REF] Hodson | Teaching and learning science: Towards a personalized Approach[END_REF], and engage in scientific discourse [START_REF] Grandy | Reconsidering the character and role of inquiry in school science: Analysis of a conference[END_REF]. By carrying out authentic science investigations students can also come to understand the natural world as an arena of investigation, where they have agency in deciding what, where and how to study. They learn the benefits and limitations of scientific practice, such as the need for rigour in collecting data, the importance of systematic analysis and the problems of drawing conclusions from incomplete data. Active participation in science should also enable them to gain a greater understanding of the practice of scientists and to take part in discussions about scientific phenomena that affect, for instance, their health and environment.

However, the ideals of inquiry learning confront the realities of school education. [START_REF] Lakkala | Teachers' pedagogical designs for technology-supported collective inquiry: a national case study[END_REF]. School science must be framed by the curriculum and constrained by practicalities of timetabling and access to equipment. Only a limited range of science investigations can be conducted in a school classroom. A need for equity means that students cannot be required to carry out experiments in the home that require family resources or outdoor space. The school grounds could be a suitable environment to study natural and physical phenomena, but a restricted school timetable combined with concerns for safety may result in a lesson that is rushed and regimented, far distant from Dewey's advocacy of learning based on authentic science practice [START_REF] Dewey | Science as subject-matter and as method[END_REF].

In addition to the generic problems of managing inquiry learning in school, de [START_REF] De | Computer simulations -Technological advances in inquiry learning[END_REF] indicates some specific difficulties faced by students when engaging with inquiry learning. Students need support to:

-design appropriate experiments (e.g. what variables to choose, how many variables to change, how to state and test hypotheses), -implement experiments (e.g. make predictions, avoid being fixated with achieving particular results rather than testing hypotheses), -interpret and present results (e.g. compare and visualize data, then present these appropriately). This should also be combined with support for participating in the argumentation and debate that must surround real inquiry [START_REF] Mcalister | Combining interaction and context design to support collaborative argumentation using a tool for synchronous CMC[END_REF][START_REF] Osborne | Enhancing the quality of argument in school science[END_REF].

Underpinning these ambitions is the more fundamental challenge to assist students in coming to see science as personally meaningful and relevant to their everyday lives. Young children may live as scientists, exploring and challenging their world, yet school science is episodic and largely disconnected from the daily life of the learner [START_REF] Thier | Setting the S.T.S. agenda in K-8[END_REF]. Such a challenge is therefore one of making inquiry learning 'personal'.

Personal Inquiry Learning

In the United Kingdom the concept of personalized learning has become politicized following a speech in 2004 by the then Schools Minister David Milliband potential of every child and then gives them the means to achieve it'. This would be achieved through personalized learning that ensures every pupil achieves the highest standard possible. Some elements of the UK personalization agenda include developing teaching and learning strategies that build on individual needs, and developing a curriculum that engages and respects students.

While we recognize the need to engage and nurture the talents of every child, we do not take an instrumental and systemic approach to personalization. Instead, following [START_REF] Hodson | Teaching and learning science: Towards a personalized Approach[END_REF] we have pursued an approach of 'personal inquiry learning' based on enabling students to gain personal meaning from science activity within a rich and familiar environment. Traditional science teaching is typically characterized by an 'elimination of the personal' [START_REF] Donnelly | Instrumentality, hermeneutics and the place of science in the school curriculum[END_REF]. Yet, we suggest that students will both engage with and take a committed stance towards the scientific process by forming questions for which they genuinely want to know the answer, by carrying out investigations that relate to their own needs and concerns, and by discussing emerging findings with peers and experts.

A central aspect of personal inquiry learning is taking ownership of the inquiry process. A prerequisite is that students should gain a clear understanding of such a process and of their agency in gaining scientific knowledge. They should come to know what kind of questions are scientifically appropriate, how these can be framed as valid inquiries, who they can find and trust as scientific informants, what kind of studies are appropriate, why it is important to collect reliable data, how this can be analyzed and presented as valid evidence, and how the results of an inquiry can be shared and discussed. An important issue for research concerns how the domain of such inquiry is effectively defined as 'personal' and, thereby, how a sense of problem ownership is created. Existing approaches of this sort have tended to identify macro-social topics, such as ecology and environment (e.g., [START_REF] Walker | Promoting discourse about socio-scientific issues through scaffolded inquiry[END_REF]. The approach taken here is more micro-social -drawing upon topics that are prominent in the learner's own 'local' world.

In this paper we address the twin issues of how to support inquiry-based science learning within the school system and how to enable students to gain personal meaning from engaging in science investigations. We describe a case study where an investigation of healthy eating was undertaken by Year 9 students (aged 14) as part of the English National Science Curriculum. During the intervention, the students began a science investigation in the classroom supported by a teacher, they continued it at home, and then discussed and presented the results of the inquiry back in the classroom.

Representing the inquiry process

From a study of frameworks for representing the science inquiry process [START_REF] Bruce | Using the web to support inquiry-based literacy development[END_REF][START_REF] Shimoda | Student goal orientation in learning inquiry skills with modifiable software advisors[END_REF] we have produced a representation of personal inquiry that serves both as a visual map for learners and a structure to guide the investigation process (Scanlon, Anastopoulou, Kerawalla, & Mulholland, under review). It shows a cycle of questioning, investigation, evidence collection, analysis, sharing and reflection, but without prescribing a particular scientific method (Figure 1). This is not to indicate that all methods are equally appropriate, but that the framework can be adapted to suit different methods of inquiry, including [Insert Figure 1 about here ]

Technology Mediation for Personal Inquiry

Through a process of co-design of technology and pedagogy, a computer-based toolkit, called nQuire, has been designed that guides the inquiry process inside and outside the classroom. The design of nQuire follows an approach of 'scripted' support for learning [START_REF] Dillenbourg | Over-scripting CSCL: The risks of blending collaborative learning with instructional design[END_REF][START_REF] O'donnell | Scripted cooperation in student dyads: A method for analyzing and enhancing academic learning and performance[END_REF] where scripts are dynamic lesson plans that guide how students should interact and collaborate in following an inquiry. The scripts support students through a sequence of activities including planning, investigation, debate, data collection and presentation. nQuire is implemented using an open source platform to enable wide access to the toolkit and is accessed through a web browser running on desktop and portable devices. providing tools for the collection and visualization of data. It does not prescribe a fixed sequence of activities, but shows the resources and dependencies for each phase. For example, to collect evidence requires having an inquiry question and a plan and, depending on the topic, nQuire will offer a set of appropriate tools to collect and visualize data. The available options change in response to the student's actions.

The computer can be attached to probes to collect data and the information from each laptop can be synchronized with a central server when in school. A teacher has additional software tools to design an inquiry investigation or to adapt one from a preprepared template, for example to include local environmental settings or provide initial data. Figure 2 shows a screenshot of the current implementation of nQuire. On the left, the screen shows phases of the personal inquiry. Along the top, below the title of the investigation, the sequence of lessons is shown. In the centre is the current activity, in this illustration an example food diary entry.

Personal inquiry of healthy eating

We describe an application of this educational-technology system at one stage in its continuing development, to support personal inquiry learning on the topic of 'healthy eating'. The educational context was for the students to explore whether they ate a healthy diet, specifically to address the inquiry questions: 'What nutrients do I eat?' and 'Do I eat enough nutrients to be healthy?'. These questions had been developed collaboratively by the research team, the teacher and a food scientist. They collectively Personal inquiry learning 8 identified healthy eating as a topic of personal interest to Year 9 children, although given the time of year of the intervention and the time needed to implement the software, the students themselves were not involved in defining the topic. To address the questions the students were encouraged and supported to: understand the science of food and nutrition; collect accurate data on their own eating patterns; analyze their diets in terms of key nutrients to understand whether they were eating healthily; then present the results of their findings to their classmates.

The healthy eating inquiry was designed to achieve educational objectives whilst also enabling the research team to explore the value and practice of technology-mediated personal inquiry. First, the study allowed students to participate in multiple phases of an inquiry project, where each phase had a clear goal leading to a sequence of activities.

Second, we wished to explore how learning could be maintained between home and school, by developing learning activities that could be initiated in the classroom and then continued at home. These had to be relevant to the students and their families and to demonstrate a clear scientific rationale and practice. Third, the study aimed to give students responsibility for their scientific investigation. They contributed to the methods used to investigate the question, proposed questions to ask an expert food scientist and were responsible for collecting and recording data. The latter activity was expected to be particularly challenging as students would need to keep complete and accurate data for any inferences to be meaningful.

Investigations outside the classroom concerning healthy eating can be supported by a written diary but this research aimed to embed the investigation into an overall inquiry cycle, employing computer technology to structure and to support planning, the computer toolkit was designed to enable children to label the food content for each meal and retrieve relevant nutritional information, to link the images of the meals to the data, to view charts comparing specific meals with recommended nutrient intake, to merge and share data with peers and to present the digital information in class.

There is good evidence that inquiry approaches to science can be effective for learning [START_REF] Marx | Inquiry-based science in the middle grades: Assessment of learning in urban systemic reform[END_REF], even if they present organizational challenges for teachers [START_REF] Lakkala | Teachers' pedagogical designs for technology-supported collective inquiry: a national case study[END_REF]. Here, we are seeking to describe inquiry learning that is grounded in the personal and, more specifically, is mediated by tools designed to support effective organization of the activities involved. Accordingly, this research addressed the following questions:

1. How does personal inquiry support scientific reasoning, understanding, attitudes and communication? 2. How do students respond to inquiries that are grounded in the personal?

3. What issues arise from students taking personal ownership of the technology and the inquiry?

Method

A case study approach was used to study personal inquiry learning. Multiple methods of data collection were employed, the details of which are presented below.

Participants

The participants comprised of 28 students from Year 9 (14 years old) in an innercity school which serves a deprived area of Nottingham and more than half of the school students have special educational needs (Department for Schools, Children and Families, Achievement and attainment tables 2009). A complete class was recruited to the study (14 girls and 14 boys) that followed the Personal Inquiry activities. In addition, a second class of children (16 girls and 13 boys) from the same year group in school took the same tests at equivalent points in the school year. These children studied their normal school curriculum. Ideally, it would have been desirable for the second class to be undertaking an equivalent curriculum, including a healthy eating topic, but without the personal inquiry approach. However, that was not possible within the constraints of our collaborating school. Consequently, test results from the second class were compared to ensure that any claims made about changes in the outcome scores of the intervention class are not simply due to testing the children on multiple occasions or at different points of the school year.

Equipment

Each student was provided with the toolkit running in a web browser on an Asus EEE netbook computer. A web server installed on each netbook enabled nQuire to operate in stand-alone mode, without access to the Internet, so students without an Internet connection at home were able to carry out the activities. In addition, each student was loaned a digital camera to take pictures of their food.

Procedure

During the planning for the intervention, many informal sessions were held where the teacher together with a researcher discussed in detail the inquiry learning perspective in the context of lesson planning. These sessions had a dual benefit. The research team came to understand the school, the profile of the students to be involved in the study and the teacher's pedagogical goals, values and approaches. In return, the teacher gained a fuller understanding of the nature of inquiry learning.

Prior to starting the inquiry, the students took written pre-tests to assess their attitudes to science and their knowledge of the domain. Children in the intervention class then participated in the inquiry process as part of nine science lessons over a three week period. The Appendix shows the title and objectives of each lesson as well as homework activities that followed. Two objectives that applied to all lessons were: 1) understanding the inquiry process and 2) monitoring progression through the phases of inquiry. To summarize, the sequence of lessons combined two methods of inquiry: a study of personal eating habits based on an individual photographic diary and a group interview with a dietician. Each child was expected to take photos of all meals and snacks over a week, to import the data on to nQuire, to generate graphs that compared the nutrient intake for each day to the Recommended Nutritional Intake (RNI), and to prepare a group presentation of the inquiry. After the inquiry, children took the same tests of science attitudes and domain knowledge again. nQuire supported these activities in a number of ways. In class, the teacher introduced the inquiry activities for each lesson; at home students could carry out the one activity as current homework or catch up with previous phases of the inquiry. To support data collection, nQuire provided software tools to upload the photos, add descriptions of the food to each image and select the names of food items from a prescribed list. For example, a child who took a photograph of a sandwich might label it 'cheese sandwich' and then select 'bread, white, 2 slices' and 'cheese, 1 portion' from pull-down menu items. The toolkit then presented a table showing the nutritional content for that meal (carbohydrates, proteins etc.). To support students' understanding of the data they collected, the toolkit automatically produced a bar chart that compared their daily food intake to the RNI (Figure 3). To support reflection on their activities, nQuire provided comment boxes for students to record what they did in each phase and what problems they had, if any. The toolkit was also used to compose questions to the food expert and it provided links to an Impress file (the OpenOffice version of PowerPoint) to prepare the presentations of their inquiry.

Methods of analysis

The outcomes and processes of the sequence of nine lessons were assessed by multiple methods. Personal inquiry learning 13 which foods they can be found. It consisted of twelve multiple choice items with one correct answer and three distractors (e.g. 'Jane has a healthy diet except she is not having enough calcium. Which of these foods does she need? Pasta; Cheese; Fish; Oranges').

Outcome Analysis

The purpose of this was to establish whether students are still able to gain domain specific knowledge while undertaking in inquiry-based activity. Attitudes towards science were assessed with the Test of Science Related Attitudes (TOSRA) [START_REF] Fraser | TOSRA test of science related attitudes handbook[END_REF], where students respond on a five point Likert scale. Four of the most relevant scales were selected in advance of the study: Attitude to Scientific Inquiry, Adoption of Scientific Attitudes, Enjoyment of Science Lessons, and Leisure Interest in Science.

Process Analysis

Each of nine lessons was recorded by three video cameras, with two focused on specific small groups and one, sited at the rear of the classroom, on the overall class. These recordings were supplemented by notes made in class by a researcher. Timestamped log files from each computer provided information about students' use of the nQuire toolkit, including text responses to questions and photos uploaded.

The processes of personal inquiry learning and the subjective experiences of the participants were investigated through interviews with the students and their class teacher and by videotaped observations of classroom interactions. Informal interviews with the teacher were conducted after lessons 1, 3, 4, 5, 6, 7, 8, and 9 to elicit her reflection on the lesson, its successes and problems, and the extent to which it had met her expectations.

Additional interviews were held after lessons 5 and 6 to reflect on how the inquiry process met her expectations and her willingness to take this approach for the remainder Seven group interviews with students aimed to capture their views on the inquiry activities. Three interviews were conducted with a group of three boys after lesson 3, lesson 5, and at the end. Two interviews were held with a group of three girls after lesson 7 and at the end. Two further interviews were conducted towards the end of the intervention: with a group of four boys during lesson 8 and a group of four girls during lesson 9.

Results

We begin by briefly considering the outcomes of the personal inquiry lessons before focussing in more detail on the process of personal inquiry learning.

Outcome Analysis

Due to absences from class, some students missed either the pre or post test and so data is included only from students who took both tests leaving 14 of the 28 students from the Personal Inquiry and 13 of the 29 students from the control class. As the data failed tests of normality, non parametric analysis is used and median scores and interquartile range are shown in Table 1. A Mann-Whitney test found no difference between the intervention and control conditions at pre-test (U = 69) but a difference at post-test (U=40.5, p<.02). A related-sample Wilcoxon test showed a significant increase in the pre to post test scores for the intervention group (T = 1, p<0.005), whereas the pre and post test scores for the control group did not differ (T=6, p = ns). showed no main effect of condition (F(4,31) = .41) or time (F(4,31) = 1.7) but did reveal a condition by time interaction (F(4,31= 3.42, p<0.02). Univariate analysis found the only subscale that showed this effect was the Enjoyment of Science Lessons Scale (F(1,34) = 6.06, MSE = 7.76, p<0.02, n p 2 = .15). Post-hoc comparisons (Bonferoni corrected)

showed that the control group scores decreased significantly over time (2.61, p<0.015) whereas the Personal Inquiry group stayed the same.

Process Analysis

The experience of personal inquiry learning was probed with individual and group interviews. The interpretation of these interviews was grounded in an intimate familiarity with the core of the project work, which took place in the classroom. Three researchers were present in the classroom for all lessons. Their fieldnotes and viewing of recordings from three video cameras furnished a context of common knowledge that resourced the interviewing and its subsequent interpretation.

Three researchers studied transcripts of all the interviews to identify references to issues that could provide insight into the three issues addressed below. They also analyzed the computer logfiles for patterns of interaction with the computer and for text references to the issues (based on problems students recorded with their data collection).

These sources of evidence were integrated into the commentary below. Interviews are indicated by a letter and number, so, for example, T1 indicates the interview with the Teacher after Lesson 1, TA5 refers to the additional interview with the Teacher after lesson 5, and B3 indicates the interview with boys after Lesson 3. Analysis of teacher and student accounts is reported here in terms of the research questions. Respectively, these are concerned with issues of scientific reasoning and communication, inquiries that are grounded in the personal, and personal ownership of the teaching of inquiry.

Issue 1: Scientific reasoning and communication

Comments related to this issue were organised around four themes: methods of data capture, socially coordinating around data, systematising data, and task orchestration. The activity of data capture by keeping a daily food diary was regarded by the students as an appropriate method of investigating personal eating patterns, but some recognised the difficulty of keeping accurate records.

[B9] R: Were they, eventually were they easy or difficult to use, to do? To actually use them? A notable feature of the toolkit design concerned the opportunities for coordinating with others around data, as would be commonplace in an authentic scientific investigation.

During lesson 8, students were asked to create computer presentations to show their group's data. The video record indicated that the students tended to work independently instead of collaborating as a group. The layout of the classroom, with students along one side of each table facing the teacher, combined with difficulties in sharing data across devices and lack of a shared large display, meant that this group activity was not well supported.

[T9] T: I think a lot of them are trying to type, they're all trying to type in the same things on the slides into all the machines ... Really you just wanted it on, one machine would suffice wouldn't it?

Despite the technical and classroom impediments to collaboration, students indicated that they benefitted from seeing the presentations from other groups.

[B9] R: Did you enjoy the presenting part, where it's actually standing in front of the class? These challenges of data capture and coordination also involve a pressure to systematise the data in ways that support interpretation and dissemination. In the context of the present study, this was expressed as a challenge of coding and categorising the participant reports of their diet. Here there emerged a productively critical consideration of the difficulties of coding.

[B9] S1 It should be emphasized that the products of this empirical effort were recognized as useful and provocative. To produce data that represented accurately their daily nutrient intake, the students were required to carry out a sequence of actions that comprised taking a photograph of each meal, selecting the components of the meal and number of portions (e.g. 'white bread, 2 slices') from a pull-down menu, then for each day comparing the nutrients calculated by the computer with the RNI. Some students found this comparison revealing. In one interview extract, the students indicated that they had made connections between personal diet and body functions.

[B9] S: We kind of realized that like all the food groups, we all realized what they do for us. It's like carbohydrates for energy and fibre's for the toilet.

The teacher also described the sequence of activities as productive, though she suggested the need for more discussion and for the children to produce written summaries:

[T9]: T: I think in the data collection, the analysis and the presentation I think

Issue 2: Responses to inquiry grounded in the personal

A central aim of this study was to support students in carrying out science investigations that had personal relevance. If acting as scientists can show them something of immediate importance, then they might come to understand the relevance of the scientific method to their daily lives. A scan of teenage magazines shows a fixation with diet, and our early discussions with the teacher suggested that healthy eating would be a meaningful and appropriate inquiry topic. What we had not anticipated was that some students would find the process of taking and sharing photographs of their food embarrassing.

[T3] T: I'm quite surprised, I was surprised still they're not, some of them are just not taking any data, got any data. <Girl 1> because she was embarrassed. She was embarrassed about letting anyone else know. I don't think <girl 2> wanted anyone else to know either, what she was eating.

[G9] S1: ...when you had to take pictures, everybody didn't take pictures because they was too embarrassed to take pictures. ...

S2:

Because if people eat chips and chips and chips or something like that, then they don't want to take pictures because they eat chips.

S1: No, they don't want to take pictures every day of chips.

S2: Because they'll think that people'll bully them because they eat the same.

Once the students realized that others were taking photos, that the data was necessary to complete the investigation, and after the teacher and researcher had Personal inquiry learning 23 reiterated that they would not be identified in class by their photos, then all the students contributed some photographs, as shown by the computer logfiles.

[T5] T: I think they realized that they couldn't do part of the lesson, you know, it was themselves that were letting us down or letting themselves down, wasn't it? You know, they couldn't get on, because you know, the buck stops with them, they've got to take photos.

[B5] S: .. Personal inquiry learning 24 works from start to finish. So I think the learning curve of that was probably much steeper than the learning curve of content.

Issue 3: Personal ownership of the technology and the inquiry

Each student was lent a digital still camera and a netbook. Apart from nQuire, the netbook also provided a Linux operating system with an internet browser and a small suite of applications including a word processor and arcade style games that were preloaded on the machine. The students were asked to take this equipment home to capture their food diaries and to bring it into school for each science lesson. We found that student and teacher commentary on this issue centred on issues of ease-of-use, engagement, mobility and domestication.

The teacher indicated that the students found the computers easy to work with and did not find the software complicated :

[T1]. T: The good moments were that […] the children could easily access the computers, they didn't find it difficult to work them, or they didn't seem to find it that complicated.

This was supported by interviews with the students who tended to frame this in terms of familiarity with the medium.

[B9] S: I found it easy to use because I've got one like it at home.

Engagement with the toolkit was positive and this was often expressed in terms of a contrast between old and new learning media.

[B3] S: ...how can I put it? You're still learning though, because it's like you're not just copying things out of a textbook and because ...we're doing Despite this favourable judgement, as the lessons progressed, some students found it awkward to carry the laptop between home and school.

[G9] S: Well, to start it was alright. R: To start?

S: But then it kept on getting annoying because you had to carry something everywhere in your bag.

Allowing a novel digital device to blur the separation between home and school required that this device undergo a degree of domestication. The appearance of a personal inquiry resource stimulated various domestic reactions. Some required the learner to be protective. In explaining the scarcity of photo data, one student noted:

[B8] I only took a couple because my brother deleted them.

In general, there was a concern about caring for this new device.

[B9] S1: My parents didn't really mind about bringing it home, just as long as I looked after it.

S2: That was my Mum's main concern, that I'd break it.

Engagement by adult members of the family covered a range of possibilities.

These included minor hijacking.

[G9] R: How did your family react to the technology?

S1: Went straight on the science. Other family members provided a generalised encouragement to value the opportunity.

[B9] S: My Mum didn't really mind because it's like, she said it's good to do because when they were at school it was all just hard work, keep your face in a book or something. But we've got modern technology to use.

Not all family input was constructive. One student recounted a comment on the group work.

[G9] S1: After a bit, my Mum said 'Are you still doing that?' S2: Yes, so did my Mum. She said 'Well, it's a bit annoying.' She said 'There's no point doing it if everyone else is going to do it.

Discussion

The primary aim of the study was to investigate whether students aged 14 can be helped to understand and engage with the scientific process by carrying out investigations between school and home that relate to their personal needs and concerns. Although the students did not choose the inquiry questions ('What nutrients do I eat?' and 'Do I eat enough nutrients to be healthy?') they were framed to be relevant to their everyday lives Personal inquiry learning 27 and to relate to a concern of many teenagers with food and health. Evidence from the interviews suggests that the students were interested in the topic and genuinely curious to find the answers. Moreover, the learning activity was substantially different to normal classroom lessons and while this posed problems for the teacher of classroom management and coordination across lessons, it did allow the students to experience the uncertainty and challenge of an open investigation. The activity extended across nine lessons, so at the start the students were uncertain as to their agency in carrying out the investigation and how the work would progress and finish.

Whilst the main point of the intervention was to deepen their understanding of the practices of science (such as collecting relevant and accurate data, communicating results) we also expected they would increase their knowledge of the role of nutrients and of the nutritional content of foods. The results of the domain knowledge questionnaire showed that the intervention students on average increased their scores by 20% from pre to post-test. This is unlikely simply to be due to the effects of repeated testing as the nonintervention group (who had not studied healthy eating) showed no improvement.

The results for the attitude scores, however, show no change. Students completed four subscales of the TOSRA (Attitude to Scientific Inquiry, Adoption of Scientific Attitudes, Enjoyment of Science Lessons, and Leisure Interest in Science) before and after the study. The mean scores from the intervention students were unchanged on all scales. Those for the control group also remained unchanged, except for the 'Enjoyment of Science Lessons' subscale, which decreased. Longitudinal studies of attitudes to science lessons often report a decrease both over the school year and across the middle and high school period (e.g. [START_REF] George | A Cross domain Analysis of Change in Students' Attitudes toward Science and Attitudes about the Utility of Science[END_REF]. Given that students in the intervention group Personal inquiry learning 28 had participated in a research study, receiving extra attention and loan of computer equipment, it is not surprising they expressed more enjoyment of science lessons that the non intervention group. Moreover, it is disappointing that the attitudes to science did not alter from pre to post test. This acts as a timely reminder that students' attitudes to science can be resistant to change and that research teams should not be over optimistic in their predictions for the likely impact of short term interventions.

Meanwhile, it is evident from the process analysis that students became more aware of the different methods of data capture, difficulties inherent in data capture outside the classroom, as well as challenges surrounding collaboration and sharing of data. A further finding is that although owning the technology for a period of time allowed students to be engaged in learning between lessons, as time progressed some students expressed discomfort with the routine, particularly in finding it awkward to carry the laptop between home and school. Some also experienced difficulties at home, such as requests to be protective, appropriation of the technology by family members, and in a few cases discouragement from taking part in the study. These all point to challenges in adopting personal technology to maintain a continuity of learning between school and home. It also raises questions of how to exploit affective issues as opportunities, for example to discuss ethical issues of the scientific community, such as confidentiality and ownership of the research.

The research team and teacher recognized in advance that for the data analysis lessons (lessons 3-5) to proceed, the students had to collect valid data in the form of photographs of food, correctly labelled with nutritional content. Thus, the students were asked to take photographs from lesson one, with each subsequent lesson requiring more Since the school experienced general difficulty in engaging its students in science and it had a no homework policy, the project took a risk in asking students to collect data outside the lessons. Accordingly, data collection at homes was limited to taking photographs of food to act as reminders of what they ate. The students then added information about portions and types of food and needed to compare charts generated by the software. This way the experience did not focus on the numerical aspects such as counting mass and calories but rather on nutritional components of each meal together with comparisons of meals during a day or longer.

The decision to photograph meals had an unintended consequence as some students were reluctant to take or share photographs of their food. In retrospect, this might have been obvious. A photograph of a plate of food generally looks less appealing than the real dish, and many students eat meals and snacks of low nutritional content yet are concerned about their self-image. However, neither the researchers nor the teacher identified this as a problem in advance of the study and we are not aware of the consequences of 'too personal' investigations being identified in literature on inquiry learning.

The problem was addressed in this study by the expedient of emphasizing to the students that they would not be identified as authors of their photographs when they were presented in class, and by the teacher requiring them to take photographs as data for analysis in class. Although this had the desired effect of eliciting notated photographs from every child, it did not entirely resolve the issue of embarrassing self-disclosure. The Personal inquiry learning 30 students worked in groups to share and present their data, so they had to show their photos and daily nutritional charts to others in their group. This might have been avoided

by each child working alone, but the group discussions were an import element in modelling the scientific process of sharing and cross-checking data. It might be possible

for the students to work in groups with anonymised data from their peers, but that would require an additional layer of technical complexity and classroom management.

This raises a more general tension in inquiry science learning. To participate in the great scientific debates of contemporary society, students need to gain empathy with the scientific process, to think and act like scientists, to gain a direct appreciation of the challenges and delights of exploring the material world. The lure is through personalized inquiry, where each child devises an inquiry question of personal relevance, or the inquiry topic resonates with students' everyday concerns [START_REF] Hodson | Teaching and learning science: Towards a personalized Approach[END_REF][START_REF] Edelson | Addressing the challenges of inquiry-based learning through technology and curriculum design[END_REF]. But to be a scientist requires detachment from the scientific process, to collect data in a rigorous manner and subject it to objective scrutiny. Is it possible to find the 'sweet spot' between objective but unengaging, and meaningful but potentially embarrassing, personalised inquiry learning?

One way forward is to devise individual inquiry questions that students want to answer, but do not require such a large amount of self-disclosure. Another possibility is to collect personal data, then ensure that it is anonymised before being shared and analyzed, with the students fully informed that they will not be associated with their data.

Both approaches require more complex technology and classroom management. The first would need the computer and the teacher to accommodate a range of inquiry methods and probes. The second would need a process of separating the collectors from their data. Both are opportunities to be explored in future research. However, our preferred solution is for the students, through discussion amongst themselves and with the teacher, to propose an inquiry topic over which they have sense of ownership and collective interest. This is the approach taken for our subsequent study.

Conclusion

We have reported a case study of personal science inquiry learning with technology dwelling on the participants' own accounts of the experience as they reveal a number of specific ways in which personal inquiry works to configure a more realistic experience of science. Our findings have shown that students can be alerted to a number of challenges associated with fieldwork: the management of data, the demands of collaboration, the ambiguities of measurement, and the need for management of activities. This did not threaten their enjoyment of the lesson nor did it negatively impact on their understanding of topic, although it is disappointing that the students' responses to the science attitude scales did not reveal any enhanced interest in science. The findings also reveal practical challenges that will be important to consider in moving this approach to science learning forward. The toolkit was successfully accommodated within a traditional science classroom and moved inquiry into the personal space of the home. However, its arrival there, at least at first, can provoke a variety of responses in relation to domestication.

These need to be anticipated, although we believe they are easily managed and capable of being directed towards constructive forms of support. More deeply rooted challenges lie in the management of the 'too personal' dimension of personal inquiry learning. It may remain for the teacher (negotiating with students) to find the 'sweet spot' that takes Personal inquiry learning 32 advantage of the engagement arising from meaningful investigation while not serving as an identity threat to the participating learners.

Given that many students of this age are quite happy to reveal personal information about themselves in social internet activities, the finding that some do not want to share a picture of what they eat is a fascinating contradiction. It indicates the difficulty that teachers have in making links to the real world. Through this research we provided recommendations by acknowledging the problem, identifying that some topics may be too personal to study or data may be too embarrassing to share with peers. Yet, this needs to be balanced against topics that students have little motivation to study. What exactly works might be limited to a balance between motivation and embarrassment.

A challenging aspect of this study was that it took place within a school with significant attendance issues as well problems in engaging its students in science and in out of school work. This was positive in that we were working with a population that does not normally participate in trials of new technology and pedagogy. However, it did offer significant problems for the research (e.g. decreased sample size, high mortality rate) and for the pedagogy (e.g. pupils missing crucial lessons, forgetting equipment at home, and complaints about carrying equipment back and forth). Furthermore, it was not possible to work with an equivalent class of students who were following the same curriculum but without the Personal Inquiry approach. This limited any comparative claims we can make from this study. It offered, however, a rich environment to extract fruitful evidence of school inquiry-based science with a personal perspective.

This study aimed to explore how a personal inquiry can become integrated into school science. It highlighted how the ideals of inquiry learning confront the realities of Understand the inquiry process as a whole.

Reflect on the inquiries of others.

To take photos of what they eat in general.

  , and discussion with expert scientists. The hatched lines are intended to show an interconnection, with each phase building upon previous work and informing future action.

[

  Insert Figure 2 about here ] The role of nQuire differs depending on the context in which it is used. Within the classroom, it assists the teacher to elicit suggestions for topics and inquiry questions, and the students to share, analyze and present findings. Outside the classroom, it guides each of the inquiry process, showing what activities are available and

A

  domain knowledge questionnaire was based on the Key Stage 3, Year 9 (Age 14) UK curriculum and assessed what nutrients are needed for healthy eating and in

  Personal inquiry learning 14 of the lessons. An interview after the final lesson focused on her experience overall, her views on the students' learning, and possible future actions.

[

  met] my expectations, yes, they over-ran from what I expected, but I think that was a really good way of doing that and I think that worked. So I think it's more, […] about what is the point of some of the tasks and do we actually need a written answer for that? [...].more of a discussion and more of maybe them writing a summary of the topic or what they're looking to research or what we've decided on.

  . I got worried about what other people would think, so I deleted two of the pictures, But then when Miss said that, I was like 'Oh, you could have told me that like yesterday.' R: When I said what? S: When you said that no-one else will see your data in class. I thought it would be shown to everybody else in the class, so I deleted the picture. R: So maybe if we do something like this again it should be made quite obvious right from the start who would see your data. S: Yes. The teacher gave a positive assessment to of the students' learning outcomes, particularly with regard to understanding the inquiry process: [T9] T: Yes. I think they've certainly got a better understanding of how healthy their diet was and how important it is that, what crisps and fizzy drink are doing to you. And I think […] they've got a far better understanding of the inquiry process because we very rarely would do the whole thing in school and they've got no idea really how properly it

  photos to the Toolkit.

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	Personal inquiry learning 15
	[Insert Table 1 about here ]
	21 students in the intervention and 15 students in the control class took both
	attitude tests. A mean substitution following missing value analysis was performed for
	o F 0.01% of the data.
	r
	P [Insert Table 2 about here ]
	e e r
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Table 2

 2 shows the pre-test and post-test means and standard deviations for each sub-scale of the Test of Science Related Attitudes (TOSRA). A 2 (Intervention/Control) by 2 (Pre-test/Post-test) mixed MANOVA was carried out on the TOSRA subscales. It

uk International Journal of Science Education

  Yes, but if you had to like do pictures that wouldn't work, because people would lie. S4: Yes, if you went out, you forgot to take your camera, because some of them don't let you take your camera in the restaurant. And you write them on the piece of paper, so you might not be able to read it. S3: But I did. At MacDonald's I did.

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	Personal inquiry learning 18
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  Yes, because it's like it's not just keeping your photos to yourself, it's spreading it round everybody else to see what they think about it and compare it to other people's.[B3] S: Being in a group here, it's like, when it's like asking questions if we can all work together to figure out a question at least we're not on our own, like we normally are in class, having to put our own answers to everything while we get accused of copying and everything. So it's nice that we can actually have a proper discussion and work it out together.

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	Personal inquiry learning 19
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  things that we enjoy like on a laptop, which all kids are going to love anyway and, to be honest, working out of textbooks is boring. So if we're working on laptops, because it's more enjoyable it's easier to take it.

	International Journal of Science Education	Page 24 of 43
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  Ah, they used it then as well? S2: Yes, my family used it as well. For games. Only at first, just on games and looked at what it does and then my brother wanted one, but he can't put a disc in and it hasn't got Microsoft summat.

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	S2: Yes, they went straight on it.
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  Personal inquiry learning 29 accurate data collection. The students did come to realize for themselves the scientific truism that analysis of data needs data to analyze.

	F o
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	F o
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	Personal inquiry learning 31
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  Personal inquiry learning 33 school education and it articulated challenges emerging from taking new technology and new pedagogical activities from a research environment to integrating them into classrooms and homes. It does not offer a solution however for scaling up; it is rather the first step towards making such innovation routine. Given the rapid development of handheld technology, activities like recording, photographing, interpreting graphs may become commonplace, but there still needs to be a way of amalgamating these components. The nQuire toolkit offers such functionality through an authoring tool that teachers can use to modify or design new inquiries that fit their needs. It is yet to be explored how such interventions can be sustainable so that teachers and students are empowered to create personal meaning through science inquiry.

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uk International Journal of Science Education

  Appendix: Outline lesson plans for the Healthy Eating inquiry

			Personal inquiry learning 37
	Lesson	Domain objectives	Inquiry objectives	homework
	1: My Topic	Make predictions about the
	My Inquiry	food that they eat.
	Question
	F o F o F o F o
		r r r r
		P P P P
		e e r e e r e e r e e r
		R e v i e w R e v i e w R e v i e w R e v i e w
			O n O n O n O n
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		Intervention (n=14)	Control (n=13)
		Median	Inter-quartile	Median	Inter-quartile
			range		range
	Pre-test	46%	25	50%	38
	Post-test	67%	21	50%	13
	F o r
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manuscriptcentral.com/tsed Email: editor_ijse@hotmail.co.

Table 1 :

 1 Percentage Scores by Condition and Time (Median and Inter-Quartile Range)

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		Intervention(n=21) Control (n=15)
	Attitudes to scientific inquiry/50	Pre 32.23 (3.04)	30.95 (3.87)
		Post 32.28 (2.44)	31.68 (2.39)
	Adoption of scientific attitudes/50	Pre 28.98 (2.12)	29.40 (4.37)
		Post 29.88 (2.14)	28.89 (2.99)
	Leisure interest in science/50	Pre 29.19 (4.30)	27.47 (5.79)
	F Enjoyment of science lessons/50 o r	Post 29.47 (3.53) Pre 25.62 (3.65) Post 26.23 (2.70)	29.52 (5.62) 27.24 (3.71) 24.63 (2.89)
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Table 2 :

 2 Means (and standard deviations) for the Test of Science Related Attitudes by condition and time

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uk International Journal of Science Education


M Ali Choudhary
email: ali.choudhary@sbp.org.pk

Adnan Haider
email: adnan.haider@sbp.org.pk

Neural Network Models for Inflation Forecasting: An Appraisal

Keywords: Artificial Neural Network, Forecasting, Inflation JEL Classification: C51, C52, C53, E31, E37 Artificial Neural Networks, Forecasting, Inflation

We assess the power of diverse artificial neural-network models (ANN) as forecasting tools for monthly inflation rates for 28 OECD countries. In the context of short outof-sample forecasting horizon we find that, on average, the ANN models were a superior predictor for inflation for 45% while the AR1 model performed better for 23% of the countries. Furthermore, we develop arithmetic combinations of several ANN models and find that these may also serve as credible tools for forecasting inflation.

There is growing interest in using artificial neural network (ANN henceforth) as a complimentary approach to forecast macroeconomic series. 1 The reason for this rising popularity is that ANN pays particular attention to nonlinearities and learning processes, both of which can help improve predictions for complex variables.

In this paper, we extend important work by [START_REF] Nakamura | Inflation forecasting using a neural network[END_REF]', [START_REF] Mcnelis | Forecasting inflation with thick models and neural networks[END_REF]', [START_REF] Binner | A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia[END_REF] Gazely and [START_REF] Binner | The application of neural networks to the divisia index debate: evidence from three countries[END_REF] and Moshiri et al., (1999)' which highlight the potential role of ANN in the context of forecasting inflation. We contribute to this literature in two ways.

First, unlike previous literature which tended to focus on a particular type of ANN and on a specific country, we evaluate competing ANN techniques to forecast inflation for a large set of countries against the well known AR1 procedure. Second, we introduce arithmetic combinations of established ANNs, we call these quasi-ANNs, to improve the overall predictability of neural methodologies.

We forecast monthly inflation rates for 28 OECD countries using two established and two quasi-ANN techniques. The first two are commonly known as hybrid and dynamic ANN models. A third method simply averages the forecasts of the hybrid and dynamic ANN to predict inflation. A final method produces forecasts using the minimum distance criterion, whereby our algorithm generates a series by selecting points from the projections of either the hybrid or the dynamic models that are closest to the average forecasts of these two models.

Two results standout (i) the neural nets considerably outcompete the AR(1) tool to predict short horizons up to 3 months and (ii) the simple hybrid learning rule and the minimum distance quasi-ANN rules dominate other forms of neural nets procedures.

In the next section, we present the methodology which is followed by results. We end with concluding remarks.

Methodology

Neural networks are particularly useful for future predictions of variables for which the data generating process is not well known and may also be subject to nonlinearities. For example, inflation is an amalgamation of complex expectation formation 2 processes across the economy and as a result has become a popular candidate variable used in the study of neural nets as a forecasting tool. 3 Neural nets consist of layers of interconnected nodes which combine the data in a way to minimize the root mean squared error (RMSE) but the researcher may also employ some other minimizing criteria such as mean absolute percentage error (MAPE). One simple example of a network is a pyramid type structure 4 where each brick represents a node. Raw information is 2 See for example Brock and Hommes (1997). 3 Other examples are Alvarez-Diaz (2008) and Lin and Yeh (2007) studies that use ANNs to forecast exchange rates and stock markets option-pricing respectively. 4 Formally known as a 'feed-forward' mechanism. fed at the bottom of the pyramid where each node independently processes information and then transmits output, weighted by the importance of the node in question, to all the nodes sitting in the layer above. The nodes in this subsequent layer then process the processed data from the previous layer and then pass on their weighted outputs to the nodes in the layer sitting above. This process continues until a final node at the top of the pyramid transmits the output of interest to the researcher. The final output/series is then checked against a RMSE criterion and if the criterion is not met, learning happens by taking into consideration the size of the error and a rule which allows adjusting initial weights assigned to each node in each layer in the pyramid. One key point that deserves mentioning is that each node is equipped with a combination function which combines various data points into a single value using weights. These single values are then transformed into the unit circle using a trigonometric function.

This study uses a different version of the pyramid type structure to forecast inflation rates using two neural and two quasi-neural architectures. The first is known as a hybrid-network (see [START_REF] Nakamura | Inflation forecasting using a neural network[END_REF], Gazlely and Binner (2000) for example) whereby the properties of the pyramid like structure are retained with the advantage that the nodes sitting in between and the top and bottom layers can communicate with one another and pass on combined values that can help minimize the RMSE (or MAPE) in the final stages.

The functional form for the hybrid-network model is given by:

πhybrid,t+ j = ∑ k θ ik tanh(w k x t-1 + b k ) (1)
where x t-1 , θ ik , w k , and b k denote the vector of lagged inflation, the weight at the kth node positioned in the ith layer, the weight of the data point assigned to kth node and biases at kth node respectively. This model produces inflation forecasts for j months ahead.

The second ANN model is the dynamic extension of hybrid neural-network model with an inbuilt recursive behavior. Studies such as [START_REF] Elman | Finding structure in time[END_REF], [START_REF] Kuan | Forecasting exchange rates using feedforward and recurrent neural networks[END_REF], [START_REF] Balkin | Using recurrent neural network for Time series forecasting[END_REF] and [START_REF] Cameron | Static, dynamic and hybrid neural networks in forecasting inflation[END_REF] use this architecture to predict the economic variables. This model includes the lags of the dependent variable as an explanatory variable in the hybrid network to capture richer dynamics. The functional form for each node in this type of network is given by: πdynamic,

t+ j = ψ 0 ν dc + ∑ h ν ho ψ h ν ch + ∑ i ν ih πdynamic,t-j + ∑ j ν lh Γ j,t-1 (2)
where ν dc denotes the weight of the direct connection between the constant input and the output. ν ho denotes the weight for the connection between the constant input and nodes. The terms ν ch , ν ih and ν lh are weights of other connections. The functions ψ 0 and ψ h are activation functions and Γ j,t-1 represents the value of network output from the previous time unit of a dynamic network. The analytical algorithmic description of this model is extensively explained in [START_REF] Kuan | Forecasting exchange rates using feedforward and recurrent neural networks[END_REF] and [START_REF] Balkin | Using recurrent neural network for Time series forecasting[END_REF].

One caveat in both networks above is that they may produce wide forecasts, especially if data is volatile or contains a number of structural breaks, see, [START_REF] Medeiros | Building neural network models for time series: A statistical approach[END_REF]. Hence, to produce sharper forecasts, we introduce two quasi-neural network procedures. The first simply averages forecasts of (1) and (2) as: πaverage,t+ j = 1 2 πhybrid,t+ j + πdynamic,t+ j The second is an algorithm that selects values on the basis of minimum distance of hybrid (1) and dynamic (2) network forecasts from the average forecast (3). This can be written as πmin-dist,t+ j = min ( πhybrid,t+ j -πaverage,t+ j ), ( πdynamic,t+ j -πaverage,t+ j )

The implementation of neural and quasi-neural network models (1), ( 2), ( 3) and ( 4), require the following steps. First, to identify the variables, which help forecast the target variable and to process the input data. Second, to layer network architecture where a minimum of three layers are required and the decision on the maximum number of layers needs experimentation. Since this study considers monthly data on inflation rate, the number of layers is twelve. We could use more layers but that would make the training time costly. Furthermore, for a dynamic network the literature recommends at most fifteen layers. At the network specification stage we can adjust a number of default parameters or values that influence the behavior of the training process. These deal with the learning, error tolerance rates of the network, the maximum number of runs, stop value for terminating training and randomizing weights with some specified dispersion.

The final steps are training the network followed by the actual forecasting. We train our specified ANN models using the Levenberg-Marquardt (LM) algorithm, a standard training algorithm used in the relevant literature. The algorithm is terminated according to an early stopping procedure that avoids over fitting (see [START_REF] Nakamura | Inflation forecasting using a neural network[END_REF]). The forecast evaluation use root mean of squared errors (RMSE) and mean absolute percentage error (MAPE) criteria. The training algorithm is run on the training set until the RMSE / MAPE starts to decrease on the validation set.

Empirical Results 5

We use monthly year-on-year inflation rates for 28 OECD countries 6 based on IFS' consumer price index series from July-1991 to June 2008. We trained both neural and quasi-neural algorithms in MATLAB. 7 Initially, we normalize the data to bring it within the unit circle using:

π n t = 2 * (π t -π max t )/(π min t -π max t ) (5)
The normalized data is used as input of neural algorithms and hence transfers training function by using specified trigonometric function. A downside of such normalization is that it restricts forecast values to remain inside the unit circle ruling out predictions for unusual inflation movements. A noteworthy implication is that forecasters of developing countries, where inflation tends to be relatively more volatile, have therefore got to be cautious whilst interpreting ANN generated inflation-forecasts for their countries. 8 The Matlab neural network toolkit procedure 'trainlm' is extended with our specific neural algorithms provided in (1), ( 2), ( 3) and ( 4) to train the data. To validate our results we compare 5 Detailed results and codes are available upon request. 6 The countries are: Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, Norway, Poland, Slovak Republic, Spain, Sweden, Switzerland, Turkey, United Kingdom and United States. 7 In order to simulate our algorithms we used MATLAB neural network toolkit. The default parameter values are assigned as: hidden layers = 12; max lag = 12; training set = 80; forecast period = 12; learning rate = 0.25; learning increment = 1.05; learning decrement = 0.07; training parameter epochs = 1000 and target RMSE = 0.00005. 8 We thank the Referee for raising this point. the forecast from our ANN models with that of a simple AR1 regressions. Our Matlab codes report the procedure that minimizes the RMSE and MAPE for each country and forecasting horizons. In cases where the RMSE (or MAPE) is numerically similar for two or more forecasting procedures for a given country and time horizon, our codes identify and report all those techniques. The entire set of results are lengthy and tedious. 9 Instead, in Table 1 below we provide a summary of the success, in terms of RMSE and MAPE criterion, of each procedure in forecasting one-, three-and twelve-steps ahead out-of-sample forecasts for July 07 to June 08 for 28 countries. Rows one through five in Table 1 show the percentage of countries for which the forecast of a given procedure is as good as any other competing technique. For example, using the RMSE criterion for 1-step forecast the AR1 performs as good as any other competing procedure for 43% of countries in the sample. Rows six and seven are more focused in that they report the percentage of countries for which either the AR1 or any other neural network provided a clear superior forecast in terms of our criterion.

First, comparing rows six and seven we find that at the short-end of forecasts, i.e. 1-and 3-steps ahead, on average, for 45%10 of the countries neural networks are clearly a superior predictor using both the RMSE and the MAPE criterion. The AR1 process performs superior for only 23% of countries. Importantly, for the remaining 32% of countries either the AR1 or any other neural network technique perform equally well. Though these results lean towards a superior overall performance for neural networks, there exists a sizeable proportion of countries for which both types of procedure perform equally well. Consequently, it is not straight forward to rule in favor of one method over another. Second, continuing with the short-end of forecasts, the hybrid (1) and our newly developed quasi-minimum distance technique (4) dominate all other forms of forecasting but the AR1 technique is not far behind either. Finally, the last two columns show that there is no single technique that dominates long term forecasting; a result consistent with the literature. We show that, overall, neural network models and their combinations dominate the simple AR1 process for forecasting inflation rates in OECD countries over the short-term horizon. However, for a good number of countries the AR1 technique provided sound results as well. It may therefore always be advisable to continuously compare econometric procedures with that of neural networks when choosing a forecasting tool.

1

  See for example Fernandez-Rodriguez et al. (2000) and Redenes and White (1998), Nakamura (2006) and Moshiri and Cameron (2000)), Chen, Racin and Swanson (2001), Swanson and White (1997) and Stock and Watson (1998).

7

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9 Page 10 of 9 Editorial

 99 Swanson, N. R. and White H. (1997) A model selection approach to real-time macroeconomic forecasting using linear models and artificial neural networks, The Review of Economics and Statistics, 79, 540-550. [18] Xiaohong, C., Racin, J. and Swanson, N. (2001) Semiparametric ARX neural network models with an application to forecasting inflation, IEEE Transactions on Neural Networks, 12, 674-683. Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Table 1

 1

		: Out-of-sample forecast comparisons
		1 Step		3 Steps	12 Steps
		RMSE MAPE RMSE MAPE RMSE MAPE
	1. AR1	43	71	43	50	29	25
	2. Hybrid, Eq. (1)	43	68	57	57	18	18
	3. Dynamic, Eq. (2)	25	39	25	32	14	32
	4. Quasi-Avg, Eq. (3)	04	36	18	32	21	21
	5. Quasi Min-Dist, Eq. (4)	43	61	50	57	04	11
	6. AR1 Only	29	21	25	18	25	21
	7. Neural Only	43	29	57	50	71	71
	Note: The percentage of countries for which a procedure minimizes either RMSE or MAPE

The authors will provide results by country upon request.

This figure is obtained by simply averaging out row seven excluding the 12-step columns.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript


Giulia Segato

Cristiana Benetti

Roberto Angeletti

Clara Montesissa

Segato

Giancarlo Biancotto
email: gbiancotto@izsvenezie.it

Doxycycline and sulfadimethoxine

Keywords: Chromatography -LC/MS, Chromatography -HPLC, Risk assessment Additives/Contaminants: Veterinary drug residues -sulphonamides, Veterinary drug residues -tetracycline Food Types: Animal products -meat, Animal feedingstuffs sulfonamides, tetracyclines, cross contamination, animal feedstuff, residues 28 29

teaching and research institutions in France or abroad, or from public or private research centers.

Feed containing veterinary drugs and additives are commonly prepared following Good 31

Manufacturing Practice (GMP) guidelines and employed as medicated feed in livestock 32 to treat animal diseases, to guarantee animal welfare and consequently to increase 33 animal weight increase and producer's economic incomes. 34 35

In the feed industry, almost all mixed feed formulations are prepared in multi-product 36 plants and cross-contamination cannot be ruled out for carry-over of active ingredients 37 from previous formulations. To prevent cross-contamination several technical steps 38 must be taken by feed operators according to Annex II Reg 183/2005 (European 39 Parliament and Council 2005) further to the separation of medicated feedstuff 40 production lines from the non-medicated (Heberer et al. 2007). 41 42 However, it is well known that contamination of non-medicated feed by residues of 43 active ingredients like antimicrobials or coccidiostats, may occur at several steps during 44 the whole production process. Further to the critical contamination of production lines, 45 contamination can also occur during storage at feed mills, transport to farms and, 46 eventually, at the farm during storage, manipulation and mixing operations. 47 48

The accidental administration of feed contaminated with pharmacologically active 49 residues can cause the marketing of food of animal origin containing drug residues, for 50 which the zero level applies, or at concentrations greater than the authorised MRL. 51 In 2009 a guideline on the GMP and the use of medicated feed [START_REF] Fefac | European Feed Manufacturers Guide: A community guide to good 462 practice for the EU industrial compound feed and premixtures manufacturing sector for 463 food producing animals[END_REF] was 52 published, and the European Food Safety Authority (EFSA) has published several 53 opinions on the risk for animal and consumer's health related to the carry-over of 54 coccidiostats or histomonostats into non-target feed contaminated at the 2%, 5% and 55 10% of the highest authorised concentrations for target species (EFSA 2007(EFSA -2008)). 56

For those compounds EFSA generally concluded that adverse effects and consequently 57 the associated risk would be negligible; however the European Commission lowered the 58 maximum levels of coccidiostats and histomonostats in non-target feeds, at 1% or 3% of 59 the maximum adopted concentrations by Commission Directive 2009/8/EC (EU 60 Commission 2009). 61 62 Despite the fact that unavoidable contamination may occur with antimicrobials, no 63 similar approach has yet been adopted for such veterinary drugs. For these compounds 64 the carry-over can occur during medicated feed production as reported by McEvoy 65 (2002) and [START_REF] Kennedy | Regulatory problems caused bu 479 contamination, a frequently overlooked cause of veterinary drug residues[END_REF] reviewing the origins of cross-contamination and the 66 consequences on food safety. The results of the feed monitoring conducted in Northern 67

Ireland in 1996 confirmed tetracyclines, sulfonamides, penicillins and ionophores as the 68 most frequently contaminating drugs. 69 70

The occurrence of residues in eggs after sulfonamide administration via feed to laying 71 hens was studied by Roudouth and Garnier 2002;[START_REF] Tansakul | A sulfadimidine model to evaluate 510 pharmacokinetics and residues at various concentrations in laying hens[END_REF]; the occurrence 72 of coccidiostats in eggs was studied by Mortier et al. 2005;Rosen J. 2001;Yakkundi et 73 al., 2002. The distribution of residues between yolk and white in eggs was studied by 74 [START_REF] Kan | The risk of contamination of food with toxic 476 substances present in animal feed[END_REF], of chlortetracycline from feed to pig tissue ( Kennedy et al. 78 2000) and of diclazuril from poultry feed to meat (Mortier et al. 2005). 79 80The general picture confirmed that further to feed contamination levels the residual 81 concentrations in food of animal origin strictly depends on the chemical compound, the 82 animal species, and the period of feed administration. 83 84 As it is currently accepted that carry-over seems to be hardly avoidable under standard 85 production, storage and manipulation conditions, the cross-contamination of feedstuffs 86 by veterinary drugs is an important issue and still a matter of concern. 87 88 Doxycycline and sulfadimethoxine are currently used for the preparation of medicated 89 feed for swine therapy. For this purpose doxycycline is commercially available in 90 premix at 0.05% concentration and medicated feed is prepared by diluting 4000-6000 g 91 premix in 1000 kg of feed to get a final maximum concentration of doxycycline at 300 92 mg kg -1 in complete medicated feed.

Materials and methods 112

Feed and feed contamination 113

Complete feed for fattening chickens was produced by Agricola Tre Valli (Valpanteno,114 Verona, Italy). Composition: moisture 12.5%, crude protein 19%, fat 8.8%, crude fiber 115 3%, crude ash 5%, methionine 0.75%. Integration to 100 g of product: vitamin A 8.0 116 IU, vitamin D3 2.0 IU, vitamin E 20 mg, Cu(SO 4 )5H 2 O 10 mg, 6-Phyitase 600 FTU. 117 About 15 kg of feed for fattening chickens (free from any veterinary drug) were spiked 118 with an aqueous solutions of doxycycline to achieve a theoretical concentration of 4 119 mg kg -1 ; while other 15 kg were spiked with an aqueous solution of sulfadimethoxine to 120 produce a final concentration of about 3 mg kg -1 . Briefly: an aqueous solution of 121 doxycycline (60 mg in about 0.5 l) was sprayed over 15 kg of feedstuff previously 122 spread over a clean surface. Analogously an aqueous solution of sulfadimethoxine (45 123 mg in about 0.5 l) was sprayed over a second lot of 15 kg of feedstuff previously spread 124 (3.79 ± 0.28) mg kg -1 and (2.28 ± 0.17) mg kg -1 , respectively, (see section 3.Results). 139 140

Animals and treatments 141

The project fulfilled the requirements of Italian Law n. 116/92 and further amendments 142 on the protection and welfare of animals used in experiments. Twenty vaccinated 143 chickens about 20 days old were weighted, allocated in cages (2 birds per cage) and fed 144 dedicated feed and water ad libitum, for acclimatisation. After 18 days the animals 145 were weighted and randomly allocated in three groups: group 1 (control), 4 animals; 146 group 2 (doxycycline) 8 animals; group 3 (sulfadimethoxine) 8 animals. 147 The 8 chickens of group 2 were fed the same feed of the acclimatisation period, 150 contaminated with doxycycline (mean concentration 3.79 ± 0.28 mg kg -1 ). 151 152The 8 chickens of group 3 were fed the same feed used during the acclimatisation 153 period, contaminated with sulfadimethoxine (mean concentration 2.28 ± 0.17 mg kg -1 ). 154

Each animal received about 150 g daily of the feedstuff -contaminated or not -over a 155 period of ten days and was weighted at the beginning and at the end of the "treatment" 156 period. 157 158 On the eleventh day, animals were sacrificed and muscle (breast), liver and kidney were 159 The procedure was based on the method proposed by Cristofani et al., 2009. 204 3 g of homogenised tissue were extracted with 20 ml of succinic acid 0.1 M pH 4 and 205 with 20 ml of MeOH by mechanical shaking for 30 sec and by sonication in an 206 ultrasonic bath for 10 min. Liquid phase was separated from solid residue by 207 centrifugation at 5000 g for 10 min. Extraction was repeated a second time by adding 10 208 ml of succinic acid 0.1 M pH 4 and 10 ml of MeOH. After centrifugation at 5000 g for 209 10 min, extracts were re-unified and purified through Metal Chelate Affinity 210 Chromatography (MCAC) columns activated with 6 ml of distilled water, 3 ml of 211 CuSO 4 10 mM and 4 ml of distilled water. After extracts loading, columns were washed 212 with 2 ml of succinic acid 0.1 M pH 4, 2 ml of distilled water, 2 ml of MeOH and 2 ml 213 of distilled water; finally elution was performed by application of 8 ml of McIlvaine 214 buffer. 215

216

The eluate was purified by OASIS HLB® SPE columns (60 mg, 3 ml) previously 217 activated with 3 ml of MeOH, 3 ml of HCl 1 N, 3 ml of distilled water and washed with 218 3 ml of distilled water. Elution was achieved by 5 ml of MeOH. Solvent was evaporated 219

Insert table 1 225

Quantification was performed against external calibration in pure solvent, as matrix 226 effects were negligible, and quantified concentrations were corrected for recovery. 227

Metacycline was added as internal standard at the beginning of the procedure in 228 unknown samples, not for strict quantitative purposes, but to prove, by an estimate of its 229 detection, the good application of the method on each single sample. 230

The relevant method characteristics are reported in table 3. 231 232

Sulfadimethoxine in chicken tissues 233

To 2 g of homogenised tissue 8 ml of de-ionised water were added and the mixture was 234 shaken by vortex for 30 sec. Acetic acid (1% in ACN, 10 ml) was added and the tubes 235 were manually shacked for 30 sec; then an Agilent SampliQ® QuEChERS EN 236 extraction salt packet was added to each tube. Sample tubes were capped tightly and 237 shaken vigorously for 1 min. 238 239 After centrifugation at 5000 g for 5 min, an aliquot of the ACN layer (6 ml) was 240 transferred into an Agilent SampliQ® QuEChERS EN fatty dispersive-SPE 15 ml tube 241 containing 150 mg of PSA, 150 mg of C18EC and 900 mg of anhydrous MgSO4. The 242 samples were vortexed for 2 min and centrifuged at 5000 g for 5 min.

Feed and dosage 256

Feed contamination by both doxycycline and sulfadimethoxine was homogeneous. By 257 analysing 10 independent feed samples, stored for the same time and in the same 258 refrigerating conditions of the feed administered to chickens, no significant differences 259 in concentrations were observed in any tested samples proving the good homogeneity 260 and stability of contaminated feed (Grubbs test, p= 0.95). Measured mean 261 concentrations were: (3.79 ± 0.28) mg kg -1 of doxycycline and (2.28 ± 0.17) mg kg -1 of 262 sulfadimethoxine. 263 264 On the day of their arrival chickens were about 20 days old, with an average weight of 265 1.1 kg (in the range 1.0 -1.2 kg) and after the acclimatisation period the weights ranged 266 between 2.0 and 2.5 kg. Each chicken in group 2 received about 0.57 mg day -1 of 267 doxycycline, while chickens in group 3 received about 0.34 mg day -1 of 268 sulfadimethoxine. During the treatment period of 10 days, a theoretical daily dose of 269 0.22 mg kg -1 body weight (b.w.) and of 0.13 mg kg -1 b.w. was estimated for doxycycline 270 and sulfadimethoxine respectively, considering the average weights of birds. 271

272

Analytical methods 273

Trueness, precision, decision limit, detection capability, limit of detection (LoD) and 274 limit of quantification (LoQ) were estimated by analyzing 18 replicates of tissue spiked 275 at 4 concentration levels around the MRL on three different days (6 replicates per level 276 per day) under intra-lab reproducibility conditions. LoD and LoQ were extrapolated 277 according to ISO 11843 approach based on the same data set. 278

280

Insert table 3 281

In figure 1 representative HPLC-DAD chromatograms of a standard solution of 282 doxycycline (at a concentration corresponding to 50 µg kg -1 on sample), a negative 283 muscle sample, a muscle sample fortified with doxycycline at 50 µg kg 1 and an incurred 284 muscle sample (doxycycline measured concentration: 48 µg kg -1 ). 285

Insert figure 1 286

In figure 2 representative LC-MS/MS chromatograms of a standard solution of 287 sulfadimethoxine (at a concentration corresponding to 20 µg kg -1 on sample), a negative 288 muscle sample, a muscle sample fortified with sulfadimethoxine at 10 µg kg -1 and an 289 incurred muscle sample (estimated concentration: 7 µg kg -1 ). 290

Insert figure 2 291 292

Transfer of veterinary drugs in chicken tissues 293

Doxycycline and sulfadimethoxine concentrations measured in muscle, liver and kidney 294 are reported in table 4. In the case of sulfadimethoxine in chicken muscle, the estimated 295 concentrations were lower than the extrapolated LoD: when S/N ratio was still adequate 296 for an approximate quantitative evaluation these figures were considered and reported in 297 italic in table 4 mg kg -1 ) frequently detected in not-compliant feed samples, during official routine 304 controls. These levels correspond to about 1.3% and 0.11% respectively of the original 305 highest dose used in medicated feed for swine. Both the percentage levels adopted are 306 lower than those chosen by EFSA for the risk assessment of coccidiostats and 307 histomonostats (3-10%). 308 309The administration period was chosen on the basis of the worst case scenario: 310 considering that a typical commercial feedlot is produced to satisfy 1 week farming 311 requirements, a period of 10 days should guarantee residue accumulation comparable to 312 a real situation. Furthermore, considering that doxycycline and sulfadimethoxine half-313 times (t 1/2 ) in circulating blood are 15-22 hrs and about 16 hrs, respectively (EMEA 314 1996;[START_REF] Epstein | Tissue sulfonamide concentration and correlation 459 in turkeys[END_REF] the steady state concentrations (5 x t 1/2 ) might be 315 reached also in tissues within the experimental period of 10 days. 316 317 During treatments the animals did not show symptoms of sufferance and feed intake 318 was regular. Though feed dose was available in a single pot per cage, and both birds had 319 contemporary access to, the absence of significant different weight gains during the 320 experimental period minimize the possibility that single animals could be exposed to 321 significantly different drug intake. 322

323

The method adopted for doxycycline analysis in chicken tissues is actually a multi-324 residue method further applicable to the detection and quantification of tetracycline, 325 well known MCAC purification approach, followed by a further purification step 327 through an Oasis HLB SPE, which guarantees a much cleaner extract. For quantitative 328 analysis the recovery factor estimated through the validation process was applied. 329 330

The method adopted for sulfadimethoxine in chicken tissues is also a multi-residue 331 method applicable to the detection and quantification of sulfaquinoxaline, 332 sulfachloropiridazine, suladiazine, sulfadoxine, sulfaisoxazole, sulfamerazine, 333 sulfamethazine, sulfamethoxazole, sulfamethoxipiridazine, sulfamonomethoxine, 334 sulfapiridine, sulfathiazole (data not shown) further to sulfadimethoxine. A novel 335 QuEChERS approach, employing two subsequent different dispersive phases, was 336 tested and cleaner purified extracts with higher recoveries were achieved with respect 337 to traditional SCX SPE previously employed. The use of deuterated sulfadimethoxine 338 (sulfadimethoxine-d6), as appropriate internal standard, allowed a quantitative estimate 339 of sulfadimethoxine concentration in all samples without the application of recovery 340 factors. Both methods were validated according to the guidelines established by 341 Decision 657/2002/EC (EU Commission 2002) and were found to be fit-for-purpose. 342

The average residual concentration of doxycycline and sulfadimethoxine were different 343 in all tissues (see table 5). Mean doxycycline concentration was 31 µg/kg in muscle, 56 344 µg kg -1 in liver and 115 µg kg -1 in kidney, whereas sulfadimethoxine was found at an 345 average concentration of 5 µg kg -1 in muscle, 13 µg kg -1 in liver and 56 µg kg -1 in 346 kidney. 347 The different residual concentrations were not only a consequence of the different 349 contamination in administered feed, but likely a result of the different absorption, 350 distribution and metabolic behaviour of the drugs; doxycycline, despite the lower logP 351 than sulfadimethoxine, exhibits a greater persistence in tissues. The low 352 sulfadimethoxine dosages adopted in the experiments could have been eliminated faster 353 than expected; as reported for sulfamethazine and sulfadimethoxine (Tansakul et al. 354 2007;Takahashi 1986) some sulfonamides follow a dose dependent kinetics thus 355 potentially increasing the risk of greater residue concentrations in tissues when present 356 at higher concentration in feed. 357 358 At the experimental conditions adopted, doxycycline and sulfadimethoxine reached in 359 each tissue average concentrations lower than the correspondent MRL. Only in the case 360 of sulfadimethoxine concentrations close to the MRL were found in kidney. 361

362

Conclusions 363

This work verified the role played by feed, cross-contaminated with doxycycline or 364 sulfadimethoxine, in residue accumulation in chicken tissues. At the feed concentrations 365 chosen for this study and in absence of any withdrawal period, in none of the analysed 366 tissue were violative concentrations of doxycycline or sulfadimethoxine found, 367 notwithstanding the long administration period. In the case of doxycycline the highest 368 concentration found in muscle was lower than 50% of the MRL, whereas in liver and 369 kidney they were lower than 30%. In the case of sulfadimethoxine, the highest residual 370 concentration in muscle was lower than 10% of the corresponding MRL, the highest in 371

  Kan and Petz 2000 for veterinary drugs while Mulder et al. 2005 studied the distribution 75 of halofuginone and toltrazuril. Other papers have presented data on the transfer of 76 coccidiostats or anthelmintics at cross-contamination levels from bovine feed to milk 77

  93 94 Sulfadimethoxine premixes for oral administration to young swine contain 200g of 95 active ingredient in 1000g of product. The recommended dose in medicated feed is 96 prepared by diluting 1 kg premix in 100 kg feed to get a final maximum concentration 97 equal to 2000 mg kg -1 in complete medicated feed. 98 99 Such high concentrations can give rise to frequent contamination of feed later prepared 100 in the same line, for the same or other animal species. 101 therefore intended to generate information on the transfer of 102 sulfadimethoxine and doxycycline to chicken tissues, when feed contaminated at the 103 carry-over concentration magnitude-detected during official monitoring plans-is 104 administered to poultry. The conditions adopted were those of a "worse scenario" in 105 terms of length of administration and absence of withdrawal time. 106 107 Eventually the residue concentrations measured were compared with MRLs established 108 by the EU Regulation 470/2009 (EU Parliament and Council 2009) and by EU 109 Regulation 37/2010 (EU Commission 2010) for further consideration. 110 111

  surface. Each spiked material was mixed several times during the drying 125 period, and finally homogenised before sampling preparation. 126 127 Both contaminated feedstuffs were dried in a semi-dark room to get a final aqueous 128 content lower than 13%. From each bulk, single samples (150 g each) destined to each 129 individual animal were prepared in paper bags and stored in the refrigerator until the 130 day of their use; a total of 8 samples per day per treatment were stored for the study. 131To assess the average concentration of each compound in the corresponding bulk, one 132 further feed sample per day of administration was prepared, stored in the same 133 conditions and analysed by HPLC: 10 samples from each contaminated bulk were 134 therefore available to determine the actual average concentration and variations which 135 might occur during storage. 136 137 Measured doxycycline and sulfadimethoxine concentration in feed were (mean ± SD): 138

  group 1 remained the same of the acclimatisation period, free of any 148 veterinary drug. 149

  to detect and quantify doxycycline and sulfadimethoxine by 163 internal methods validated according to the guideline laid down by Decision 164 657/2002/EC (EU Commission 2002). 165 Briefly the methods are hereby summarised. 166 All solvents (methanol -MeOH, acetonitrile -ACN) were of HPLC grade and all of the 167 chemicals (succinic acid, oxalic acid, copper sulphate (CuSO 4 ), citric acid, disodium 168 hydrogen phosphate dihydrate (Na 2 HPO 4 .2H 2 O), hydrochloric acid (HCl) 1N, formic 169 acid, acetic acid, ethylenediaminetetracetic acid (EDTA), sodium chloride (NaCl), 170 aqueous ammonia solution 33% were all of analytical reagent grade. Distilled water 171 (Milan, Italy), sulfadimethoxine and metacycline pure standards 174 were purchased from Riedel de Haen (Milan, Italy). A stock solution of 1000 mg l -1 for 175 each drug was prepared by dissolving the appropriate amount of each substance in 176 methanol. The intermediate working standard solutions for fortification and calibration 177 curves (sulfadimethoxine, sulfadimethoxine-d6, doxycycline and metacycline: 1 mg l -1 ) 178 were prepared daily by diluting separately the correspondent stock solution with 179 methanol. 180 181 Chelating sepharose fast flow was purchased from GE Healthcare (Uppsala, Sweden); 182 OASIS HLB® 60mg/3ml solid phase extraction (SPE) columns were purchase by 183 Waters (Milford, MA, USA). 184 185 Agilent SampliQ® QuEChERS EN extraction kit 5982-5650 and Agilent SampliQ® 186 QuEChERS EN fatty dispersive-SPE kit for 15 ml 5982-5156 were purchased from 187 Agilent (Agilent Tecnologies Inc., DE, USA). 188 189 Analysis of doxycycline and metacycline (internal standard) was performed on a liquid 190 chromatographic system (HPLC) Alliance 2695 provided with a quaternary solvent 191 delivery system, a column heater module and a sample cooling device, coupled to a 192 diode array detector (DAD, 2996) from Waters (Milford, MA, USA); the 193 chromatographic column was a Supelco Ascentis Express® C18 column, 2.7 µm, (150 194 x 4.6) mm, purchased from Supelco (Milan, Italy). HPLC analysis of sulfadimethoxine 195 d6 was performed by an HPLC Alliance 2695 provided with a 196 quaternary solvent delivery system, a column heater module and a sample cooling 197 device coupled to a QuattroUltima® triple quadrupole system (MSMS) provided with 198 an ESI source by Waters (Milford, MA, USA); the chromatographic column was an 199 XTerra® Phenyl column, 3.5 µm, (100 x 2.1) mm, purchased from Waters (Milford, 200 MA, USA). 201 202 Doxycycline in chicken tissues 203

  N 2 streaming and the residue was re-dissolved with 0.5 ml of oxalic 220 acid 0.01 M before injection in HPLC-DAD. 221 222 Doxycycline determination was performed by HPLC-DAD according to the 223 chromatographic and detection conditions reported in table 1. 224

  performance characteristics of both methods are summarised in table 279

  and their epimers (data not shown). It is based on the 326

Figure 1 .Figure 2 .

 12 Figure captions 518

Figure 1 .Figure 2 .

 12 Figure 1. HPLC-DAD chromatograms. From top to bottom: doxycycline reference standard solution corresponding to 50 µg kg-1, a negative muscle sample, a muscle sample spiked at 50 µg kg-1 and a muscle sample from a group 2 animal (id. nb.142) 138x244mm (96 x 96 DPI)

Table 1 .

 1 HPLC-DAD conditions for doxycycline and metacycline.

	Time [minutes]	% Acetonitrile	% Oxalic 0.01M acid	% Methanol
	0.0 -1.0	9	70	21
	1.0 -6.0	22	70	8
	6.0 -12.0	22	70	8
	12.0 -13.0	9	70	21
	Flow rate		0.6 ml min -1
			C18, 2.7µm, (150 x
	HPLC Column		4.6)mm, Ascentis Express
			(Supelco)
	Injection Volume	20 µl
	Autosampler temperature:	5° C
	Column temperature:	30° C
	Detection system: UV-DAD. Operative conditions
	Monitoring wavelenght	355 nm
	UV spectrum range	210 -450 nm

Table 2 .

 2 HPLC-MSMS conditions for Sulfadimethoxine and Sulfadimethoxine-d6

	Time [minutes]	% Formic acid 0.1%	% Acetonitrile
	0.0 -1.0	95	5
	1.0 -15.0	70	30
	15.0 -16.5	70	30
	16.5 -17.0	10	90
	17.0 -19.0	9	70
	19.0 -20.0	95	5
	Flow rate	0.25 ml min -1
	HPLC column	Phenyl, 3.5µm, (100 x 2.1)mm, XTerra (Waters)
	Injection volume	10 µl
	Autosampler temperature:	5° C
	Column temperature:	30° C
	Detection system: MS/MS analyzer. Operative conditions
	Sulfadimethoxine MSMS transitions (Collision Energy)	m/z: 311 > 156 (20 eV)	m/z: 311 > 108 (28 eV)
	Sulfadimethoxine-d6
	MSMS transition	m/z: 317 > 156 (20 eV)
	(Collision Energy)
	Ionization mode	ESI +
	Capillary voltage	3.20 kV
	Cone voltage	40 V
	Source temperature	125° C
	Desolvation temperature	350° C
	Desolvation gas (nitrogen) flow	900 l hr -1
	Cone gas flow	50 l hr -1

Table 3 .

 3 Recovery, precision, decision limit (CCα) and detection capability (CCß), limit of detection (LoD) and limit of quantification (LoD) of the analytical methods for doxycycline and sulfadimethoxine determination in chicken muscle Mean value of 6 repetitions for each fortification level on each day, three series on three different days

		Fortification levels [µg kg -1 ]	Mean found a [µg kg -1 ]	Recovery (%)	Precision R.S.D. (%)	LoD [µg kg -1 ]	LoQ [µg kg -1 ]	CCα [µg kg -1 ]	CCß [µg kg -1 ]
		25	21.5	85.9	11.0
	Doxycycline	50 100	41.0 80.7	82.1 80.7	8.9 6.7	14.6	24.8	109	118
		150	119.7	79.8	7.0
		20	22.2	110.8	7.2
	Sulfadimethoxine	50 100	47.9 93.9	95.8 93.9	3.1 4.0	8.6	14.6	106	112
		150	141.5	94.3	4.0
	Note:

a

Table 4 .

 4 Measured concentrations of doxycycline and sulfadimethoxine in muscle, liver and kidney after 10 days administration of contaminated feed.

	Page 29 of 31			Food Additives and Contaminants
	Group	Animal number	Muscle [µg kg -1 ]	Liver [µg kg -1 ]	Kidney [µg kg -1 ]
		131	N.D. a	N.D.	N.D.
	CONTROL	132 133	N.D. N.D.	N.D. N.D.	N.D. N.D.
		134	N.D.	N.D.	N.D.
		135	34.2	52.9	107.4
		136	20.2	38.1	77.1
		137	20.2	43.3	77.7
	Doxycycline	138	22.5	40.1	89.2
		139	22.6	35.4	91.7
		140	49.7	95.5	185.2
		141	29.3	64.6	123.3
		142	47.5	78.6	165.7
	Average		30.8	56.1	114.7
	Standard dev.		12.0	21.8	40.8
	MRL		100	300	600
		143	6.9	18.9	80.1
		144	2.2	5.8	34.7
		145	6.2	15.5	87.4
	Sulfa-dimethoxine	146 147	6.8 4.4	17.6 9.5	82.7 40.2
		148	3.8	12.7	50.6
		149	5.9	16.3	48.6
		150	2.1	5.7	27.0
	Average		4.8	12.8	56.4
	Standard dev.		2	5.2	23.6
	MRL		100	100	100
	Note: a N.D. = not detected

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was de-ionised by an ultrapure water system (Sartorius Stedim Biotec, France).

Doxycycline and sulfadimethoxine-d6 (sulfadimethoxine-d6) pure standards were

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substances in foodstuffs of animal origin, repealing Council Regulation (EEC) No

2377/90 and amending Directive 2001/82/EC of the European Parliament and of the

McEvoy, J.D.G. 2002. Contamination of animal feedingdstuffs as a cause of residues in

food: a review of regulatory aspects, incidence and control. Anal. Chim. Acta, 473: 3-

Takahashi, Y. 1986. Mechanism of nonlinear pharmacokinetics of sulfadimethoxine in

Acknowledgements 383

The project was financially supported by Italian Ministry of Health. The authors would 384 like to thank Bruna Allegretta and Valentina Mozzo for their technical assistance 385 throughout the study. 386