Dr Angela Göbel
email: angela.goebel@bvl.bund.de

Andreas Kliemant

The German Minimisation Concept for Acrylamide

other groups to achieve a comprehensive minimisation in all food groups concerned. In contrast to legal regulations, the minimisation concept is a 'soft' risk management instrument.

It represents a novel approach, for instance to cope with situations when maximum levels are not available. Such a concept can be considered as a smart solution to bring about preventive measures resulting in health protection. As an innovative risk management tool, it has the potential to tackle the challenges of consumer protection in the future.

Introduction

The problem of acrylamide in food became topical in April 2002 when it was found by scientists of the Swedish National Food Administration in a wide variety of foodstuffs (Swedish National Food Agency 2002). Acrylamide was identified as a product of the heating process -a so called food-borne contaminant. This caused worldwide concern (World Health Organization (WHO) 2002, European Commission 2002). Up to that time, acrylamide was only known as a toxic industrial chemical for technical purposes, primarily in the production of polyacrylamide used for the fabrication of plastic materials and also in the processing of drinking water for instance. This new knowledge about acrylamide meant a big challenge to the authorities responsible for consumer protection because acrylamide has been found carcinogenic and mutagenic in animal tests [START_REF] Johnson | Chronic toxicity and oncogenicity study on acrylamide incorporated in the drinking water of Fischer 344 rats[END_REF][START_REF] Friedman | A lifetime oncogenicity study in rats with acrylamide[END_REF], Bull et al. 1984a, b, Robinson et al. 1986). The International Agency for Research on Cancer has also classified acrylamide as a Group 2A carcinogen (International Agency for Research on Cancer (IARC) 1994). Furthermore, a genotoxic mechanism was proven and attributed to glycidamide, the main metabolite formed in mammals [START_REF] Doerge | DNA adducts derived from administration of acrylamide and glycidamide to mice and rats[END_REF].

However, no comprehensive risk assessment for man is currently available due to a lack of data (Joint FAO/WHO Expert Committee on Food Additives (JECFA) 2005). But JECFA calculated a margin of exposure (MOE) of 300 for the cancer risks caused by acrylamide with mean intake and of 75 with high intake, which is extremely low for a compound that is probably carcinogenic and genotoxic for man (Joint FAO/WHO Expert Committee on Food Additives (JECFA) 2005).

The usual approach to deal with substances which are carcinogenic and genotoxic is the ALARA-principle (as low as reasonably achievable). Another possible management measure to cope with contaminants in food is setting maximum levels. With acrylamide, however, this appeared far from realistic because there were no adequate toxicological data available to deduce such a limit, nor was the technical feasibility given. The mechanism by which acrylamide is formed during the thermal processing of food was largely unknown at that time, and it appeared rather difficult to predict the amount to which acrylamide would actually be formed by a given treatment. It was obvious that the formation of acrylamide during the processing as well as the preparation of food depends on a spectrum of influencing variables, such as raw materials and processing techniques. The mutual aim can be described as the achievement of a gradual reduction of acrylamide content in foodstuffs by avoiding formation of this food borne contaminant as far as possible.

This requires the development of processing and preparation techniques which would lower the formation of acrylamide in food without changing the main character of the product. This action followed the ALARA principle: as low as reasonably achievable. So the acrylamide minimisation concept represented an instrument which was expected to accomplish a preventive health protection of consumers, irrespective of the absence of a sufficient risk assessment and maximum levels.

The Minimisation Concept for Acrylamide

The signal value

The core of the minimisation concept is the signal value which ensures the dynamic strategy of this concept. It is calculated for each commodity group. Originally the minimisation concept covered seven commodity groups. Now the number has been expanded to 13. These commodity groups are potentially highly contaminated by acrylamide, posing the highest hazards for the consumers. The signal value is defined as the lowest level of those 10% of products of each commodity group which have the highest acrylamide content. This is illustrated in figure 1. The 20 weights are increasing. The lowest level of the highest 10 % would correspond to the 19 th weight in the row. This would be the signal value of a given commodity group. In addition the signal value has to meet certain conditions. It must be a really existing acrylamide level (not the statistically calculated 90 th percentile) in that particular commodity group, it must not exceed 1000 µg/kg, and once it is established, it must not be increased, but has to be maintained or reduced. This definition is necessary to guarantee the dynamics of the concept towards a gradual reduction of acrylamide contents in foodstuffs. Furthermore the signal value acts as a threshold. If the acrylamide content of a product exceeds the current signal value of the corresponding commodity group, a so-called minimisation dialogue will start, ideally followed by appropriate minimisation measures. Therefore, the signal value serves as the bearing instrument and as an indicator of success of minimisation efforts within the relevant assessment period. Nevertheless, the validity of the signal value is limited because of deliberate restrictions within the relevant calculation procedure.

The observer value

Another important item of the minimisation concept is the observer value. Because of the deliberate limitations of the signal value, it was established to allow a more accurate description of the current minimisation situation. The observer value is calculated basically in the same way as the signal value, but it does not have to meet any condition. That means that the observer value may increase as well as exceed 1000 µg/kg. Thereby it offers the possibility to describe the actual success of minimisation efforts even if the signal value is unchanged. For instance, it can show a decrease from 2380 µg/kg to 1030 µg/kg as is the case for example for the commodity group coffee extract, with the signal value being 1000 µg/kg. Of course it also indicates an increase for example in the case of potato crisps from 1029 µg/kg to 1333 µg/kg, with the signal value being 1000 µg/kg. Accordingly this allows a comprehensive description of the real situation of minimisation. extreme values and outliers. The potential of minimisation for a particular commodity group is also reflected in this way. That means, the bigger the difference between the median and the signal value, the bigger is the potential of minimisation in that group of foodstuffs.

The German minimisation concept for acrylamide

A schematic illustration of the German minimisation concept for acrylamide is shown in scheme 1. It works as follows:

Scheme 1: Workflow of the minimisation concept

Data collection:

The official food surveillance authorities of the German federal states take samples of foodstuffs and analyse them for acrylamide. They collect the data and forward them to the The BVL maintains a database on acrylamide in which 10294 data sets were collected up to March 2006. It calculates the signal value as described under 3.1 based on the data from the previous year (in general from the 1 st of September to 31 st of August) for all 13 commodity groups once a year. This strategy balances the effects of seasonal fluctuations in acrylamide contents in foods caused by the seasonal variations in the raw materials as well as the seasonal availability of some product groups. Moreover a signal value of 1000 µg/kg is automatically applied to foodstuffs which are not part of any of the 13 commodity groups of the minimisation concept for acrylamide. That means if the BVL gets aware of such a product with an acrylamide content of more than 1000 µg/kg, the minimisation dialogue will start in the same way.

Signal value exceedance:

The BVL checks all incoming data whether any of the current signal values is exceeded. If a product exceeds the signal value of the respective commodity group, or has an acrylamide content of more than 1000 µg/kg, the BVL will inform the official food surveillance authority of that federal state in which the producer of this product is located.

Minimisation Dialogue:

The respective federal state authority gets in contact with the particular producer and starts the minimisation dialogue. That is to say they check together with the producer whether ingredients or process parameters could be changed and in which way the acrylamide content of the product could be minimised while its special product properties are maintained.

Minimisation Measures:

The successful application of the minimisation measures developed will then lead to a reduction of the acrylamide content of that product.

Success:

The sum of all minimisation dialogues and the application of minimisation measures will lead to a mitigation of acrylamide contents in foodstuffs and therefore to a reduction of signal values in the following year. All in all, this dynamic process results in a gradual reduction of acrylamide in food.

To sum up, the biggest challenge for the success of the minimisation concept is to implement minimisation measures within the boundaries of nearly unchanged product properties. This approach takes account of the very sensitive consumer reaction to changes of product quality. A possibility is to ensure that quality criteria like crispness of potato crisps are unchanged after a minimisation of acrylamide contents. Another solution is to inform the consumer about the necessity to change the product quality. A good example for that is the slogan "golden yellow, not carbonized" to advertise the new colour of chips with low acrylamide contents. Both strategies are helpful to obtain consumers´ acceptance of acrylamide-minimised products.

Assessment of success of the acrylamide minimisation concept

Observation of individual products

Observation of individual products is a special tool within the acrylamide minimisation concept. The aim is to provide evidence about the extent of acrylamide minimisation concerning a single product before and after the implementation of minimisation measures.

Thus, observation of single products provides a rationale for the understanding of minimisation processes. Such an example for the successful implementation of minimisation measures is the development in the acrylamide content of diabetics' cakes. As shown in figure 2, it was possible to achieve not only a gradual mitigation of the acrylamide contents in these cakes but also a strong reduction of the statistical spread of acrylamide values. The big differences as regards the number of samples therein demonstrate the pragmatic approach of this concept, since there is no coordination concerning number and kind of products as is the case in a monitoring programme. This is a result of the decision to leave a margin to all suppliers of data. This strategy was necessary within a concept voluntarily agreed among the partners. The wide range between the minimum and maximum levels within one commodity group reflects the broad variety of different products on the market. It also reflects, in particular for products based on potatoes, the change in composition of the raw material potato during a longer period of time, e.g. during storage. However, differences found between products within one food group are driving forces for minimisation efforts, because products with low acrylamide contents demonstrate the possibilities of minimisation. The difference between the signal value and the associated median value indicates the potential of minimisation within a commodity group. The commodity group "Coffee extract"

shows a difference of less than 150 µg/kg acrylamide between the signal value and the corresponding median. This could reflect either complete absence of minimisation activities, or, more plausibly, an only small minimisation potential (figure 3). In contrast, the food group "Gingerbread" shows a difference of approximately 750 µg/kg acrylamide between the signal value and the corresponding median. This situation allows the interpretation that the minimisation potential in this food group is high. However, a more complex approach regarding the minimisation potential of a food group is the evaluation of the frequency distribution. In addition to the evaluation of development of signal, observer and median values, the evaluation of the frequency distribution provides a further tool for a comprehensive assessment of the success of the minimisation concept. This evaluation is based on all acrylamide contents available for a specific commodity group for the relevant assessment period, thus providing a survey on all acrylamide contents in a given commodity group. When assessing the frequency distribution, a "shift to the left" compared to the normal curve reflects an increase in products with acrylamide concentrations below 300 µg/kg, in some product groups even below 200 µg/kg or 100 µg/kg. This shift can be used in combination with the associated signal and maximum values as an indicator for the minimisation potential of the corresponding food group. As can be seen for the examples "Chips, prepared" (figure 4) and "Diabetics' cakes and biscuits" (figure 5) the decrease of the signal value and the fact that more than 66 resp. 70 percent of all tested products have acrylamide contents of less than 300 µg/kg, allows to state a minimisation success. Moreover, the characteristics of the frequency distribution, the "shift to the left", can be used in combination with the concomitant existence of high acrylamide levels with more than 1000 µg/kg as an indicator for a very high minimisation potential.

Future prospects of the minimisation concept

So after four years of operating this concept it is necessary to think about modifications, like deleting commodity groups with a very small or almost no minimisation potential, to thereby concentrate the capacity on food groups with a high potential of minimisation or to introduce new food groups which contain potentially less acrylamide but are consumed in larger amounts, like bread. Also you could think of the implementation of Good Manufacturing Practices. Furthermore, a survey of consumption data is in progress in Germany at the moment. Later the German Federal Institute for Risk Assessment will make a risk assessment with consideration of current acrylamide data and consumption survey data. The results may also have some influence on the handling of the minimisation concept.

Acrylamide minimisation concept -a modern risk management tool

The German acrylamide minimisation concept represents an innovative risk management tool. Unfortunately, up to now it is a national approach to tackle a global problem. However, there are many alternative endeavours aiming to minimise acrylamide contents in foodstuffs.

For example, the European Commission has issued some information on ways to lower the levels of acrylamide formed in food (European Commission 2003). The Confederation of the Food and Drink Industries of the EU has created the acrylamide "Toolbox", describing in detail acrylamide formation and providing a set of measures to minimise the formation of acrylamide during manufacturing or processing of food (Confederation of the food and drink industries of the EU (CIAA) 2005).

The experience obtained during four years of operating the acrylamide minimisation concept allows the general recommendation that strategies of gradual minimisation should also be applied with substances other than acrylamide. The acrylamide minimisation concept typifies a pragmatic, flexible solution. Therefore, in cases when a legal limit is not available, this concept has the potential to be the first choice among other strategies for preventive health protection of consumers.

4

  In this situation the German minimisation concept for acrylamide was established by the Federal Office of Consumer Protection and Food Safety in August 2002. It was a pragmatic concept based on a voluntary agreement among the Federal Office of Consumer Protection and Food Safety (BVL), the Federal Ministry of Food, Agriculture and Consumer Protection (BMELV), the German federal state authorities and the stakeholders of the affected industry.

Figure 1 :

 1 Figure 1: Illustration of the signal value

  special instruments of the minimisation concept (the signal value and the observer value), the degree of success as regards the minimisation of acrylamide in foodstuff can also be measured by classical statistical instruments like the mean, the median and the frequency distribution. These values are important complements, because in particular the median and the frequency distribution clearly indicate how many products of a given commodity group have what acrylamide content, and whether many products have acrylamide contents close to the signal value or whether the signal value is caused by

Figure 2 :•

 2 Figure 2: Time course of acrylamide contents of diabetics' cakes, whole grain

Figure 3 :Figure 4 :Figure 5 :

 345 Figure 3: Frequency distribution of coffee extract

Table 1 :

 1 Signal values, 5 th calculation, valid from 21 October 2005

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

Table 2 :

 2 Signal values and observer values, 1 st to 5 th calculation

		1 st category: Signal value decreased
		Diabetics' cakes and biscuits
		45
			57 %
		40
	Frequency (number of products, n = 125)	0 5 10 15 20 25 30 35		70 %				Signal value: 545 µg/kg
		< 100	101-200	201-300	301-400	401-500	501-600	601-700	701-800	801-900	901-1000 1001-2000	higher
							Acrylamide
							[µg/kg]
	Commodity group	Signal (observer) value 1 st calculation 17 Sep 2002 acrylamide (µg/kg)	Signal (observer) value 2 nd calculation 31 Jan 2003 acrylamide (µg/kg)	Signal (observer) value 3 rd calculation 26 Nov 2003 acrylamide (µg/kg)	Signal (observer) value 4 th calculation 17 Nov 2004 acrylamide (µg/kg)	Signal (observer) value 5 th calculation 21 Oct 2005 acrylamide (µg/kg)
	Fine bakery
	ware, short
	pastry		800			660		575	575 (760)	300
	Breakfast
	cereals		260			260		200	200 (240)	180
	Coffee, roasted		370			370		370 (520)	370 (420)	370(537)
	Potato crisps	1000 (1500)		1000 (1200)	1000 (1470)	1000 (1029)	1000 (1333)
	Crispbread		610			610		610 (1260)	610 (640)*	590
	Chips,
	prepared		770			570		570		540	530
	Potato fritters,
	prepared	1000		1000 (1300)	1000 (1080)	1000 (1215)	1000 (2520)
	Gingerbread	1000		1000 (1370)	1000 (1460)	1000 (1020)

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.ukFood Additives and Contaminants

* not calculated

As shown in table 2, the signal value of the commodity group "Fine bakery ware, short pastry" was reduced from 800 to 300 µg/kg within the framework of the minimisation concept.

The signal value for "Breakfast cereals" decreased from 260 to 180 µg/kg. A slow mitigation appeared to be possible also in the commodity group "Potato crisps". The signal value for "Chips, prepared" decreased from 770 to 530 µg/kg and that for "Thin almond biscuits" from 1000 to 560 µg/kg. In the food group "Diabetics´cakes and biscuits", the signal value was even reduced from 1000 µg/kg with an observer value of 1740 µg/kg to a signal value of 545 µg/kg. On the other hand there seemed to be no changes in the acrylamide contents in all three coffee food groups, the "Crispbread" and the "Gingerbread and bakery ware containing gingerbread" group. This indicates either a very small potential of minimisation in those particular commodity groups or too little minimisation efforts by the industry. So, obviously, besides its success the minimisation concept has also clear limits.

Limits of the minimisation concept

In times of international trade many producers are located in foreign countries and therefore outside the area of competence of the German food surveillance authorities. Small handicraft enterprises and the catering businesses, too, which have neither the know-how nor the possibilities to obtain current scientific information, are difficult to reach because of their greater numbers. The same applies to the home preparation of foodstuff. And finally, as mentioned above, not all commodity groups are accessible for minimisation measures in the same way.


Martin Rose

Shaun White

Roy Macarthur

Rupert G Petch

Joseph Holland

Andrew P Damant

Single-Laboratory Validation of a GC/MS Method for the Determination of 27 Polycyclic Aromatic Hydrocarbons (PAHs) in Oils and Fats

Keywords:

A protocol for the measurement of 27 polycyclic aromatic hydrocarbons (PAHs) in vegetable oils by GC/MS has undergone single-laboratory validation. PAHs were measured in three oils (olive pomace, sunflower and coconut oil). Five samples of each oil (one unfortified, and four fortified at concentrations between 2 µg/kg and 50 µg/kg) were analysed in replicate (four times in separate runs). Two samples (one unfortified and one fortified at 2 µg/kg) of five oils (virgin olive oil, grapeseed oil, toasted sesame oil, olive margarine and palm oil) were also analysed. The validation included an assessment of measurement bias from the results of 120 measurements of a certified reference material (coconut oil BCR CRM458 certified for 6 PAHs).

The method is capable of reliably detecting 26 out of 27 PAHs, at concentrations less than 2 µg/kg which is the EU maximum limit for benzo[a]pyrene, in vegetable oils, olive pomace oil, sunflower oil and coconut oil. Quantitative results were obtained that are fit for purpose for concentrations from below 2 µg/kg to 50 µg/kg for 24 out of 27 PAHs in olive pomace oil, sunflower oil and coconut oil. The reliable detection of 2 µg/kg of PAHs in five additional oils (virgin olive oil, grapeseed oil, toasted sesame oil, olive margarine and palm oil) has been demonstrated. The method failed to produce fit-forpurpose results for the measurement of dibenzo[a,h]pyrene, anthanthrene and cyclopenta [c,d]pyrene. The reason for the failure was the large variation in results. The likely cause was the lack of availability of 13 C isotope internal standards for these PAHs at the time of the study. The protocol has been shown to be fit-for-purpose and is suitable for formal validation by inter-laboratory collaborative study.

Introduction

PAHs are a group of substances, some of which are known or suspected as being genotoxic carcinogens. As such there is no safe level of exposure and intake from food should be as low as reasonably practicable. PAHs have been known for a long time to occur at low levels in food (Scientific Committee on Food, 2002), especially where food is smoked or dried during the production process. Legislation has been introduced within the EU that establishes maximum limits for benzo [a]pyrene legally allowed in food , with an instruction to monitor other PAHs with a view to including these in future legislation (European Union, 2005). Most work done on PAHs monitors just benzo[a]pyrene. The maximum limit for benzo [a]pyrene in oils and fats is 2 µg/kg and there are other food types included in the legislation with limits ranging from 1 µg/kg for baby foods and infant formulae to 10 µg/kg for smoked bivalve molluscs. Foodstuffs represent a major source of exposure of humans to PAHs although there are a few incidents of direct exposure due to combustion processes that have been recorded.

Previous work on the UK Total Diet Study Survey (from 2000) revealed that the oils and fats group contained one of the highest concentrations of PAHs at 11.05 µg/kg (sum of 19 PAHs) (Food Standards Agency, 2002).

The PAHs that are recommended for further investigation include the 15 EU priority PAHs together with one PAH (benzo[c]fluorine) highlighted by the JECFA committee on Food Additives in 2005. Because of the development of European legislation in this area, analytical methodology for multiple PAHs in food is required in order to get a clearer picture of human exposure [START_REF] Wenzl | Analytical methods for polycyclic aromatic hydrocarbons (PAHs) in food and the environment needed for new food legislation in the European Union[END_REF].

In view of uncertainties about the levels of PAHs in foods, especially those possessing both genotoxic and carcinogenic properties as identified by the former EC SCF, a review of regulations will be undertaken by early 2007. Information is required to support this review, and the European Food Safety Authority (EFSA) has been asked to initiate and coordinate PAH-data collection in various food categories. In response, EFSA has established an on-line analytical database in collaboration with EU Member States in 3 order to collect data. This information should also show whether or not benzo [a]pyrene is a suitable marker for more general PAH exposure [START_REF] Wenzl | Analytical methods for polycyclic aromatic hydrocarbons (PAHs) in food and the environment needed for new food legislation in the European Union[END_REF].

Many laboratories undertaking the analysis of foods for PAHs use an extraction method, sometimes including solid phase extraction (e.g. C 18 cartridges), followed by high performance liquid chromatography (HPLC) with fluorescence detection (European Union, 2004). This methodology has prevailed for many years due to the inherent fluorescence of PAHs giving rise to reasonably high selectivity and sensitivity.

However, final analysis using low resolution gas chromatography-mass spectrometry (GC/MS) with stable isotope dilution GC/MS offers an alternative procedure with greater accuracy, precision and selectivity than that offered by HPLC.

There are several reference methods for PAHs. These include ISO 15302 (1998) 'Methods of analysis of fats and fatty oils. Other methods. Determination of benzo [a]pyrene content by reverse-phase HPLC' which is an international standard for the determination of benzo [a]pyrene in crude or refined edible oils and fats by reversephase HPLC using fluorimetric detection in the range from 0.1 µg/kg to 50 µg/kg. ISO/FDIS 15753 (2006) 'Animal and vegetable fats and oils -Determination of polycyclic aromatic hydrocarbons' is an international standard describing two methods for the determination of 15 polycyclic aromatic hydrocarbons (PAHs) in animal and vegetable fats and oils: (i) a general method, and (ii) a method specific for coconut oil and short-chain vegetable oils. These ISO methods are not quantitative for the more volatile compounds such as naphthalene, acenaphthene and fluorene. Due to interferences provided by the matrix itself, palm oil and olive pomace oil cannot be analysed using these methods. The quantification limit is 0.2 µg/kg for almost all compounds analysed, (for fluoranthene and benzo(g,h,i)perylene the quantification limit is 0.3 µg/kg, and for indeno(1,2,3c,d)pyrene the quantification limit is 1.0 µg/kg). The PAHs are extracted with an acetonitrile/acetone mixture followed by purification on C18 reversed-phase and then florisil bonded-phase cartridges. Determination is achieved by HPLC and fluorescence detection. describes a HPLC procedure which has been validated for coconut, olive, sunflower and bean oils. The limit of determination for the PAHs is 0.1 µg/kg. The validated concentration range of the method is 0,1 µg/kg to 3,5 µg/kg for each individual PAH. If it is expected that the level of the (light) PAHs in samples to be analyzed will be > 3.5 µg/kg, these samples have to be diluted prior to analysis. Seventeen PAHs can be determined by this method: anthracene, phenanthrene, fluoranthene, pyrene, chrysene, 1,2-benzanthracene, benz(e)pyrene, benz(a)pyrene, perylene, 1,12-benzperylene, The method described in this paper forms ISO/TC 34/SC11 N850 Working Draft 'Animal and vegetable fats and oils -Determination of polycyclic aromatic hydrocarbons (PAH) by gas chromatography/mass spectrometry (GC/MS), and has been proposed in response to the need for a fully validated GC/MS PAH method using isotope-labelled internal standards within the oils and fats sector as raised by various international standards committees including that of CEN/TC307, ISO/TC34/SC2 and ISO/TC34/SC11. Internal standardisation using ¹³C-labelled isotopes of the PAHs provides an automatic correction for recovery. Moreover, the inclusion of 13 C-isotopes in every sample also corrects for any variation due to matrix effects. The high accuracy of data obtained using stable isotope dilution is well recognised and is the recommended confirmatory method for measurement of dioxins and other substances (European Union, 2002a, European Union, 2002b). The implementation and enforcement of the maximum limits for PAHs also highlights the need for standardised analytical methods that have been validated and collaboratively tested across food control laboratories.

This paper presents data obtained from the single-laboratory validation of a low resolution GC/MS method, using isotope-labelled internal standards. The study was designed to produce a suitable protocol for possible submission to CEN and ISO as a future international standard.

Materials and Methods

Analytical standards

PAH reference standards and 13 C-labelled PAHs were purchased from LGC Promochem (Teddington, London, UK) and Qm x (Thaxted, Essex, UK) as solutions in n-nonane, isooctane or n-hexane with a specified 10% tolerance on the stated concentration. An internal standard solution containing sixteen 13 C-isotopes of the selected PAH substances (ca. 100 pg/µl), except for dibenzo [a,i] Oils used as test materials were purchased from retail outlets. Homogeniety was ensured by thoroughly mixing the oils prior to fortification with PAHs and by vigorously shaking afterwards. The samples were then mixed using a magnetic stirrer overnight and were re-mixed frequently as aliquots were dispensed.

All equipment was scrupulously cleaned and thoroughly rinsed with dichloromethane prior to use. Care was taken to avoid airborne contamination of containers by keeping vials capped even when empty and covering flasks and concentration tubes with cleaned aluminium foil.

Each batch of extracts was prepared and 'spiked' according to a randomised batch format with respect to oil type (sunflower oil, coconut oil or olive pomace oil) and spike level (un-spiked, 1 µg/kg, 2 µg/kg, 10 µg/kg, or 50 µg/kg PAH).

Extraction

The test portion (5 g) was accurately weighed into a Duran bottle and thoroughly mixed with an aliquot (50 µL) of PAH internal standard solution. Following the addition of 2M methanolic potassium hydroxide (200 mL) the mixture was homogenised and shaken for 30 min at 60°C. The saponified mixture was decanted through a glass funnel containing glass wool into a glass separating funnel while still warm. Once cool, cyclohexane (100mL) was added and the mixture shaken. Following phase separation, the cyclohexane layer was decanted into a second separating funnel (250 mL). This process was repeated and the cyclohexane layers combined in the second separating funnel. Methanol/water (4:1, v/v, 50mL) was added and the mixture shaken for 1 min.

Following phase separation the methanol/water phase was run to waste. Using a measuring cylinder the volume of the cyclohexane layer was measured and then returned to the same separating funnel. The same volume of dimethylformamide/water solution (9:1, v/v) was added and the mixture shaken for 3 min. The phases were allowed to separate fully. The lower dimethylformamide phase was transferred to a measuring cyclinder, the volume measured, and then returned to the empty separating funnel. The same volume of sodium chloride solution (1%, w/v) was added along with cyclohexane (100 mL) and the extract shaken for 3 min. The phases were allowed to separate, the cyclohexane layer decanted into an evaporation vessel and the extract was concentrated to approximately 3 mL. This relatively long extraction procedure has been shown over a period of several years application in the laboratory to produce highly purified extracts thus allowing best possible detection limits with minimal interferences in extracts.

Solid phase clean up

Prepared silica gel (2.5 g) was suspended in cyclohexane and poured into a chromatographic column. The cyclohexane was eluted until a 0.5 cm depth remained on top of the silica gel bed. The extract was quantitatively transferred to the top of the prepared column and eluted with cyclohexane (100mL). The concentrate was evaporated to approximately 25 µL under nitrogen. An aliquot (50 µL) of sensitivity standard was added to the concentrate, which was subsequently evaporated under nitrogen to approximately 50 µL ± 2 µL. The concentrated extracts were transferred to autosampler vials ready for analysis by GC/MS.

GC/MS determination of PAHs

GC/MS was performed on a TRACE GC/MS quadrupole instrument (Thermo Finnigan).

Chromatographic separation was performed using a 60 m (5%-phenyl)methylpolysiloxane capillary column, film thickness 0.25 µm. Programmed temperature vapourisation (PTV) injections of 5 µL were made with a temperature program of 50°C, hold 1 min; 10°C/sec to 320°C, hold 40 min. The chromatograph temperature programme consisted of a 2.5 min isothermal period at 60°C followed by heating at 7°C/min to 215°C with a 5 min isothermal period then at 2°C/min to 260°C with a 3 min isothermal period and finally at 3.5°C/min to 340°C with an isothermal period of 15

Quality control

Each GC/MS run was preceded by analysis of the PAH RRF solution used to check the overall system performance and calibration validity. Hard copies of all integrated chromatograms were scrutinised to assess chromatographic peak shape, resolution and signal-to-noise ratio. GC/MS data were acceptable, or better when measured against stated quality criteria, for all congeners in all samples. Extracts were prepared in batches of 12 including at least one full method blank and at least one reference material. The blank was assessed for internal standard recoveries and for the presence of native PAHs.

Blank analyses were satisfactory in all cases. The quality control samples were a reference material prepared by the BCR, CRM458, PAHs in spiked coconut oil, which ,2,3-cd]pyrene and pyrene [START_REF] Luther | The certification of the mass fraction of pyrene, chrysene, benzo[END_REF].

was certified for the 6 PAHs benzo[a]pyrene, benzo[g,h,i]perylene, benzo[k]fluoranthene, chrysene, indeno[1
.

Treatment of results

Measurement variation

Estimates of the between-run variation associated with the measurement of each analyte were gained by calculating the between-run standard deviation for the results produced by the determination of each analyte at each level of fortification (unfortified, 1, 2, 10 and 50 µg/kg) across three matrices (olive pomace oil, coconut oil and sunflower oil) measured in four analytical runs. Analytical variation was assumed to have the form shown in Equation 1 [START_REF] Eurachem | laying down the sampling methods and the methods of analysis for the official control of dioxins and the determination of dioxin-like PCBs in foodstuffs[END_REF].

Measurement bias

For each analyte the measurement bias was estimated by carrying out a weighted least squares linear regression of the mean absolute apparent recovery of each analyte against the concentration of analyte added for the analyte in that material [START_REF] Draper | Applied Regression Analysis[END_REF]. For each analyte, the regression was weighted by the reciprocal of the variance displayed by results for the measurement of that analyte. Results for analytes with an intercept significantly (p<0.05) different from zero (displaying constant bias) or a gradient significantly different from one (displaying proportional bias) were judged to require correction. The uncertainty associated with constant bias and proportional bias was estimated from the standard error associated with the intercept and gradient parameters produced by the regression. An assessment of bias was also made by comparing the results of a large number (n=120) of measurements of 6 PAHs in the certified reference material (CRM 458), to the assigned value for each PAH.

Measurement uncertainty

Uncertainty was calculated using the top-down approach from reference material data as laid out in the Eurachem Guide [START_REF] Eurachem | laying down the sampling methods and the methods of analysis for the official control of dioxins and the determination of dioxin-like PCBs in foodstuffs[END_REF]. The calculated uncertainty was dependent on the levels detected in the samples and the limit of detection of each individual PAH. The relationship between the standard uncertainty associated with measurement results and concentration was estimated by combining between-run variation with the uncertainty associated with measurement bias.

Limit of detection

The critical level (lowest measurement result that confirms the presence of an analyte)

was estimated from the standard uncertainty at zero concentration multiplied by 3 (a factor recommended by IUPAC) [START_REF] Thompson | Harmonized guidelines for singlelaboratory validation of methods of analysis[END_REF]. The limit of detection or minimum detectable value [START_REF] Currie | Nomenclature in evaluation of analytical methods including detection and quantification capabilities (IUPAC Recommendations[END_REF], the lowest true concentration that will reliably give a measurement result above the critical level, was estimated as the concentration at which 'value minus expanded uncertainty' was equal to the critical level. The critical level was estimated using equation 3a and the minimum detectable value was estimated using equation 3b. The limit of quantification is the lowest concentration that can be measured with a sufficiently low relative uncertainty. If the relation between standard uncertainty u and analyte concentration c is given by equation 3c, then the limit of quantification is given by equation 3d.

2 2 2 0 c RSU u u × + = Equation 3c 2 2 2 0 arg RSU et T u LOQ - = Equation 3d
Target is the maximum fit for purpose relative standard uncertainty and will vary depending upon the requirements of the user. The Harmonized guidelines for singlelaboratory validation of methods of analysis [START_REF] Thompson | Harmonized guidelines for singlelaboratory validation of methods of analysis[END_REF] recommends that the LOQ is not reported for single laboratory validation exercises since it may be misinterpreted; on this basis the LOQ is not discussed further in this paper.

Fitness-for-purpose

For each analyte, the fitness-for-purpose of results produced by the measurement method was assessed by comparing the size of the standard uncertainty associated with measurement results to the value of the modified Horwitz function [START_REF] Thompson | Recent trends in inter-laboratory precision at ppb and sub-ppb concentrations in relation to fitness for purpose criteria in proficiency testing[END_REF].

The result of the comparison was expressed as the lowest concentration of each analyte for which the relative standard uncertainty was equal to the modified Horwitz relative standard deviation. The ability of the method to reliably detect each analyte at the legal limit for benzo[a]pyrene was assessed by a comparison of the minimum detectable value with the legal limit. The difference between the results of the measurement of PAHs in five unfortified oils (virgin olive oil, grapeseed oil, toasted sesame oil, olive margarine and palm oil) and the same oils fortified with 27 PAHs at 2 µg/kg was compared to the critical level for each PAH. Where the difference in measured concentration between the unfortified oil and the fortified oil was greater than the critical level, the addition of the PAH was declared to have been detected.

Results and Discussion

Measurement variation

Examination of between-run measurement variation (Table 2) showed that the size of measurement variation was a function of the identity of the PAH, and PAH concentration, rather than sample type. This indicated that it was reasonable to include the range of sample types employed in the validation within a single scope and that it was reasonable to use a model of the form given by Equation 1to describe the relation between between-run variation and sample concentration. The parameters that describe the relation between between-run variation and sample concentration were calculated. In general (24 analytes) the concentration dependant contribution to between-run variation (rsd) was less than 0.20; however rsd was greater than 0.3 for dibenzo[a,h]pyrene (0.314), anthanthrene (0.344) and cyclopenta[c,d]pyrene (0.477). These are compounds for which no 13 C internal standard was available so poorer accuracy and precision was to be expected.

Measurement bias

The regression of mean recovery of analyte against concentration showed that the recoveries of benz[a]anthracene, coronene, dibenz[a,h]anthracene, dibenzo[a,e]pyrene, dibenzo[a,l]pyrene and phenanthrene displayed statistically significant constant bias, and that the recoveries of 13 PAHs showed statistically significant proportional bias (Table 3). However only the results of the measurement of dibenzo[a,h]pyrene, anthanthrene and cyclopenta [c,d]pyrene displayed a large (>30%) proportional bias. These are the same compounds that were shown to give poorer accuracy and precision, and again the explanation may be that these are compounds for which no 13 C internal standard was available so poorer accuracy and precision was to be expected. The results of 120 did not display significant bias (Table 4). The table also shows that the standard deviation for repeat analysis of these samples is well within the values predicted by Horwitz (as modified) [START_REF] Thompson | Recent trends in inter-laboratory precision at ppb and sub-ppb concentrations in relation to fitness for purpose criteria in proficiency testing[END_REF] for measuring trace levels of contaminants in foods.

Measurement uncertainty

Parameters describing the relation between measurement uncertainty and concentration (Equation 2b) are shown in Typical uncertainty at concentrations of 0.1, 1, 10 and 100 µg/kg are shown in Table 6 for the six reference material congeners.

Limits of detection

Estimates for limits of detection are given in Table 7. For 26 PAHs (including benzo[a]pyrene), the minimum detectable concentration was less than 2 µg/kg. For one PAH (cyclopenta[c,d]pyrene) the minimum detectable concentration was much higher (14 µg/kg). The reason for the higher limit of detection is the high concentration dependent between-run variation (0.48) for that analyte. run relative standard deviation associated with their measurement. These are the three PAHs for which internal standards were not available.

Fitness for purpose

Detection of the addition of 2 µ µ µ µg/kg of PAHs in other oils

Table 8 shows the results of the analysis of five unfortified oils (virgin olive oil, grapeseed oil, toasted sesame oil, olive margarine and palm oil) and the same oils fortified with 27 different PAHs. The difference between results for unfortified and fortified oils was greater than the critical level, and hence was detected, for all but 3 out of the 135 measurements. The addition was not detected for anthanthrene (one of the PAHs found to produce variable results) in grapeseed oil, cyclopenta[c,d]pyrene (the PAH found to have a high lowest detectable value) in grapeseed oil and phenanthrene (high concentration in the unfortified oil) in toasted sesame oil.

Conclusions

The method for the measurement of PAHs been shown to be capable of reliably detecting 26 out of 27 PAHs, at concentrations less than 2 µg/kg (the EU maximum limit for benzo[a]pyrene in vegetable oils) in olive pomace oil, sunflower oil and coconut oil.

The method has been shown to produce quantitative results that are fit-for-purpose for a range of concentrations that extends from below 2 µg/kg to 50 µg/kg for 24 out of 27

PAHs in olive pomace oil, sunflower oil and coconut oil. The reliable detection of the addition of 2 µg/kg of PAHs to five additional oils (virgin olive oil, grapeseed oil, toasted sesame oil, olive margarine and palm oil) has been demonstrated.

The method failed to produce fit-for-purpose results for the measurement of dibenzo[a,h]pyrene, anthanthrene and cyclopenta[c,d]pyrene. The reason for the failure was the large variation displayed by measurement results. The likely cause for the poor performance is the lack of availability of 13 C-internal standards for these analytes. The same reason may also be the cause of the high recovery measured for 5-methylchyrsene.

However it may be possible to improve the performance of the protocol for these PAHs (even if 13 C-internal standards do not become available) through the judicious selection of a different suitable internal standard. ,2,3-c,d]

F

  /SC 11 N863 Working Draft 'Animal and vegetable fats and oils -Determination of polycyclic aromatic hydrocarbons in edible fats and oils by on-line donor acceptor complex chromatography and HPLC with fluorescence detection'

  cd)pyrene, benz(a)fluoranthene, benz(b)fluoranthene and benz(k)fluoranthene. The PAHs in edible oils are determined by on line coupling of Donor Acceptor Complex Chromatography and reversed phase HPLC with fluorescence detection. The oil samples are eluted through a modified stationary phase (DACC column), which acts as an electron acceptor. This column retains the PAHs (electron donors) by p-p interactions. The PAHs are transferred on-line to the analytical reversed phase column. The individual PAHs are detected at different wavelengths and are identified according to retention time and quantified using external calibration.

  Spanish, Italian and Greek olive pomace oils were found to be contaminated with PAHs (European Union, 2002c), and products were withdrawn from sale. As a result of the variability within results reported for the same products, the European Commission made inspection visits of laboratories and production facilities in all olive oil producing countries. The report following the inspection highlighted the need for the establishment of harmonised maximum limits for the concentration of PAHs in oils and also the need for standardised methods of analysis(European Union, 2002c).

  ionisation (70 eV) with selected ion monitoring was used (Table1), and the two most abundant ions from the molecular ion cluster were measured for each homologue. The 15 PAHs identified as priority to the EU SCF are shown in bold in this table(SCF 2002). Chromatograms including figures showing the resolution of critical pairs of analytes are shown in Figure1.The PAH RRF standard solution was analysed by GC/MS and PAH peaks identified on the basis of MS response and GC retention time. Each sample concentrate was analysed using the same conditions and the retention time and the area of each observable PAH peak recorded.Data handlingMasslynx 3.5 software was used for identification and quantification of PAH congeners from GC/MS analysis. PAH concentrations were calculated using the internal standard method. This method automatically corrects for recovery of the internal standard. The RRF solution contains sensitivity standard and internal standards at the 100% recovery level as well as known amounts of the target substances. Internal standard recoveries were calculated by comparing the ratio, (internal standard response)/(sensitivity standard response), to the ratio of the integrals of the same peaks found for the RRF.The mass of analyte present in the test portion extract is calculated by reference to the RRF solution. Using the response ratio obtained from the RRF, the mass of analyte present in the test portion was calculated.Results were exported into Microsoft Excel for additional processing before transcription to a Microsoft Access database for collation.

  the between-run standard deviation associated with the measurement of analyte at concentrations close to zero and rsd= value of between-run relative standard deviation for the measurement of analyte at high concentrations analyte, estimates of the values of s 0 and rsd were gained by examining how the size of between-run variation changed with concentration. Values of s 0 were estimated from the root-mean-square of the between-run standard deviations displayed by results of the analysis of unfortified samples of each matrix. The values of rsd were estimated using the slope of a linear regression line describing the relation between measurement result and between-run standard deviation across matrices.

  the between-run standard deviation associated with the measurement of analyte at concentrations close to zero rsd= value towards which the between-run relative standard deviation associated with the measurement of analyte at high concentrations tends u bias0 = the standard uncertainty associated with (constant) bias at zero concentration expressed as a standard uncertainty rsu bias = the relative standard uncertainty associated with concentration dependent (proportional) bias u 0 = value of the combined standard uncertainty at zero concentration and rsu= value towards which the combined relative standard uncertainty tends for high concentrations of analyte

  value of standard uncertainty at zero concentration which the relative standard uncertainty tends for high concentrations of analyte L C = critical level and L D = limit of detection

F

FigureFigure 1c :

 1c Figure 1a: RRF Standard at 200pg/ul

Figure 1d :

 1d Figure 1d: Resolution of benzo[e]pyrene and benzo[a]pyrene

Figure 1g :

 1g Figure 1g: Sunflower oil spiked at 2µg/kg

Table 5 .

 5 The component describing the concentration dependent contribution to standard uncertainty (rsu) is less than 0.2 for 24 of the

analytes. For the remaining analytes which are the same ones that gave poorest accuracy and precision and measurement bias (dibenzo[a,h]pyrene, anthanthrene and cyclopenta[c,d]pyrene), rsu > 0.3.

Table 7

 7

	also shows estimates of the lowest concentration for which the relative standard
	uncertainty associated with results is lower than the equivalent modified Horwitz relative
	standard deviation. The lowest concentration that produced fit for purpose results was
	less than 2 µg/kg for 24 PAHs. For the remaining 3 PAHs (dibenzo[a,h]pyrene,
	anthanthrene and cyclopenta[c,d]pyrene) the fitness for purpose criterion could not be
	met for the measurement of any concentration of analyte because of the high between-

Table 1 : Ions monitored by GC/MS for PAH analysis

 1

	The method has been shown to be fit-for-purpose and with the use of more appropriate
	internal standards for dibenzo[a,h]pyrene, anthanthrene and cyclopenta[c,d]pyrene, is
	recommended as suitable for formal validation by collaborative trial and for submission
	to CEN and ISO as a future international standard method.
	o r
	P
	e
	e r
	R
	e
	v i e
	w
	O n l
	y
	16

Table 2 : Recovery of PAHs from fortified olive pomace, sunflower and coconut oils

 2

	Analyte	Spike	Mean result /	Mean	Between-run	Relative
		µg/kg	µg/kg	recovery	standard deviation	standard
				(%)	/ µg/kg	deviation
	5-methylchrysene	1	1.3	126	0.11	0.09
	5-methylchrysene	2	2.4	120	0.18	0.07
	5-methylchrysene	10	12.7	126	1.36	0.11
	5-methylchrysene	50	56.3	113	4.38	0.08
	acenaphthene	1	1.7	96	0.22	0.13
	acenaphthene	2	2.7	96	0.34	0.13
	acenaphthene	10	10.0	94	0.52	0.05
	acenaphthene	50	50.0	99	5.52	0.11
	acenaphthylene	1	1.5	97	0.19	0.12
	acenaphthylene	2	2.5	96	0.24	0.10
	acenaphthylene	10	10.2	95	0.31	0.03
	acenaphthylene	50	50.9	01	4.53	0.09
	anthanthrene	1	0.3	23	0.16	0.65
	anthanthrene	2	0.6	29	0.38	0.63
	anthanthrene	10	4.8	46	1.40	0.29
	anthanthrene	50	28.6	57	9.91	0.35
	anthracene	1	1.2	06	0.18	0.15
	anthracene	2	2.2	00	0.16	0.07
	anthracene	10	9.8	97	0.43	0.04
	anthracene	50	51.9	03	5.67	0.11
	benz[a]anthracene	1	1.2	01	0.18	0.16
	benz[a]anthracene	2	2.1	98	0.17	0.08
	benz[a]anthracene	10	9.7	95	0.38	0.04
	benz[a]anthracene	50	48.8	97	6.63	0.14
	benzo[a]pyrene	1	1.1	98	0.06	0.05
	benzo[a]pyrene	2	2.0	94	0.11	0.05
	benzo[a]pyrene	10	9.5	94	0.32	0.03
	benzo[a]pyrene	50	48.4	97	6.93	0.14
	benzo[b]fluoranthene	1	1.0	94	0.21	0.21
	benzo[b]fluoranthene	2	2.0	95	0.25	0.12
	benzo[b]fluoranthene	10	9.3	92	1.02	0.11
	benzo[b]fluoranthene	50	49.8	100	8.08	0.16
	benzo[b]naphtho[2,1-
	d]thiophene	1	0.9	87	0.13	0.15
	benzo[b]naphtho[2,1-
	d]thiophene	2	1.7	85	0.19	0.11
	benzo[b]naphtho[2,1-
	d]thiophene	10	8.6	86	1.08	0.12
	benzo[b]naphtho[2,1-
	d]thiophene	50	47.1	94	4.64	0.10

Table 2 (continued): Recovery of PAHs from fortified olive pomace, sunflower and coconut oils

 2

	Analyte	Spike	Mean result /	Mean	Between-run	Relative
		µg/kg	µg/kg	recovery	standard deviation	standard
				(%)	/ µg/kg	deviation
	benzo[g,h,i]fluoranthene	1	1.2	94	0.29	0.25
	benzo[g,h,i]fluoranthene	2	2.0	89	0.30	0.15
	benzo[g,h,i]fluoranthene	10	9.0	89	0.87	0.10
	benzo[g,h,i]fluoranthene	50	48.1	96	3.83	0.08
	benzo[j]fluoranthene	1	1.1	04	0.10	0.09
	benzo[j]fluoranthene	2	2.1	02	0.12	0.06
	benzo[j]fluoranthene	10	10.9	08	0.56	0.05
	benzo[j]fluoranthene	50	52.9	06	6.01	0.11
	benzo[k]fluoranthene	1	0.9	88	0.15	0.17
	benzo[k]fluoranthene	2	1.9	92	0.25	0.14
	benzo[k]fluoranthene	10	8.9	88	1.08	0.12
	benzo[k]fluoranthene	50	46.1	92	8.29	0.18
	chrysene	1	1.6	03	0.55	0.35
	chrysene	2	2.5	97	0.52	0.21
	chrysene	10	10.1	95	0.49	0.05
	chrysene	50	50.6	100	6.66	0.13
	coronene	1	1.3	128	0.18	0.13
	coronene	2	2.4	117	0.45	0.19
	coronene	10	11.6	115	1.50	0.13
	coronene	50	57.9	116	5.15	0.09
	cyclopenta[c,d]pyrene	1	0.7	53	0.47	0.64
	cyclopenta[c,d]pyrene	2	1.5	65	0.71	0.47
	cyclopenta[c,d]pyrene	10	8.3	79	3.85	0.47
	cyclopenta[c,d]pyrene	50	30.2	60	14.50	0.48
	dibenz[a,h]anthracene	1	1.0	101	0.05	0.04
	dibenz[a,h]anthracene	2	2.0	97	0.07	0.03
	dibenz[a,h]anthracene	10	9.6	96	0.21	0.02
	dibenz[a,h]anthracene	50	49.5	99	7.85	0.16
	dibenzo[a,e]pyrene	1	1.2	121	0.12	0.10
	dibenzo[a,e]pyrene	2	2.3	116	0.37	0.16
	dibenzo[a,e]pyrene	10	11.4	114	1.30	0.11
	dibenzo[a,e]pyrene	50	55.5	111	3.96	0.07

Table 2 (continued): Recovery of PAHs from fortified olive pomace, sunflower and coconut oils

 2

	Analyte	Spike	Mean result /	Mean	Between-run	Relative
		µg/kg	µg/kg	recovery	standard deviation	standard
				(%)	/ µg/kg	deviation
	fluoranthene	2	2.9	99	0.52	0.18
	fluoranthene	10	10.6	99	1.15	0.11
	fluoranthene	50	51.9	102	7.02	0.14
	fluorene	1	2.3	90	0.34	0.15
	fluorene	2	3.2	91	0.35	0.11
	fluorene	10	10.6	92	0.41	0.04
	fluorene	50	49.3	96	5.77	0.12
	indeno[1,2,3-cd]pyrene	1	1.0	97	0.03	0.03
	indeno[1,2,3-cd]pyrene	2	2.0	94	0.09	0.04
	indeno[1,2,3-cd]pyrene	10	9.4	94	0.30	0.03
	indeno[1,2,3-cd]pyrene	50	50.4	101	7.82	0.16
	phenanthrene	1	3.8	108	0.66	0.17
	phenanthrene	2	4.6	91	0.84	0.18
	phenanthrene	10	10.7	85	1.15	0.11
	phenanthrene	50	42.3	79	3.58	0.08
	pyrene	1	2.0	93	0.95	0.48
	pyrene	2	2.9	93	0.85	0.30
	pyrene	10	10.2	90	0.68	0.07
	pyrene	50	48.1	94	6.08	0.13

Table 3 : Measurement bias for fortified samples

 3

	Analyte	Constant	s.e. constant	Proportional se proportional p(constant) p(proportional)
		µg/kg	µg/kg
	5-methylchrysene	0.096	0.087	1.16*	0.05	0.387	0.071
	acenaphthene	0.027	0.062	0.93*	0.02	0.701	0.056
	acenaphthylene	-0.015	0.044	0.97	0.01	0.760	0.130
	anthanthrene	-0.319	0.080	0.52*	0.04	0.057	0.007
	anthracene	0.073	0.059	0.97	0.02	0.340	0.243
	benz[a]anthracene	0.055*	0.010	0.95*	0.00	0.029	0.008
	benzo[a]pyrene	0.045	0.161	0.93*	0.01	0.806	0.013
	benzo[b]fluoranthene	0.000	0.062	0.94	0.03	1.000	0.160
	benzo[b]naphtho[2,1-
	d]thiophene	-0.065	0.070	0.90	0.03	0.448	0.095
	benzo[e]pyrene	0.076	0.075	1.11	0.03	0.419	0.070
	benzo[g,h,i]perylene	0.000	0.077	0.92	0.03	0.998	0.120
	benzo[g,h,i]fluoranthene	-0.061	0.042	1.08	0.02	0.282	0.063
	benzo[j]fluoranthene	-0.016	0.033	0.91	0.02	0.669	0.028
	benzo[k]fluoranthene	0.048	0.021	0.94	0.01	0.153	0.013
	chrysene	0.063	0.030	0.95	0.01	0.168	0.032
	coronene	0.122*	0.027	1.15	0.01	0.046	0.006
	cyclopenta[c,d]pyrene	-0.125	0.173	0.69	0.08	0.545	0.058
	dibenz[a,h]anthracene	0.055*	0.012	0.95*	0.00	0.040	0.009
	dibenzo[a,e]pyrene	0.098*	0.014	1.11*	0.01	0.021	0.004
	dibenzo[a,h]pyrene	-0.269	0.070	0.45*	0.04	0.061	0.005
	dibenzo[a,i]pyrene	0.030	0.103	0.95*	0.00	0.801	0.010
	dibenzo[a,l]pyrene	0.201*	0.044	0.98	0.02	0.045	0.376
	fluoranthene	0.054	0.066	0.97	0.03	0.503	0.338
	fluorene	-0.045	0.032	0.93*	0.01	0.289	0.015
	indeno[1,2,3-cd]pyrene	0.039	0.017	0.93*	0.01	0.150	0.021
	phenanthrene	0.266*	0.036	0.78*	0.01	0.018	0.001
	pyrene	0.054	0.021	0.91*	0.01	0.122	0.009
	s.e: standard error
	p(constant): probability of observed results being produced by an unbiased system
	p(proportional): probability of observed results being produced by an unbiased system
	*significant values

Table 4 : Measurement bias for certified reference material BCR CRM 458 PAHs in coconut oil

 4

	Analyte	Assigned value /	mean result/	s.d. results /	u(mean) / µg/kg U(assigned) /	mean/assigned /	u(mean/assigned)	p (bias)
		µg/kg	µg/kg	µg/kg		µg/kg	µg/kg
	benzo[a]pyrene	0.93	0.97	0.05	0.005	0.09	1.05	0.10	0.62
	benzo[g,h,i]perylene	0.97	0.91	0.03	0.003	0.07	0.93	0.07	0.37
	benzo[k]fluoranthene	1.87	1.75	0.08	0.008	0.18	0.94	0.10	0.51
	chrysene	4.90	4.60	0.11	0.011	0.4	0.94	0.08	0.45
	indeno[1,2,3-cd]pyrene	1.00	0.97	0.03	0.003	0.07	0.97	0.07	0.69
	pyrene	9.40	9.71	0.30	0.029	1.5	1.03	0.16	0.83
	s.d. results: between-run variation associated with measurement of PAH in reference material
	u(mean): standard deviation associated with mean result
	u(assigned): standard uncertainty associated with certified value for concentration of PAH
	u(mean/assigned): uncertainty associated with the ratio of mean result to certified value
	p(bias): probability of observed results being produced by an unbiased system

Table 5 : Relation between standard uncertainty and concentration for the measurement of PAHs in vegetable oils

 5

	Analyte			u 0 / µg/kg					rsu
	5-methylchrysene				0.089							0.090
	acenaphthene				0.25							0.11
	acenaphthylene				0.22							0.089
	anthanthrene				0.094							0.35
	anthracene				0.079							0.11
	benz[a]anthracene				0.026							0.14
	benzo[a]pyrene				0.16							0.15
	benzo[b]fluoranthene				0.064							0.17
	benzo[b]naphtho[2,1-d]thiophene				0.070							0.10
	benzo[e]pyrene				0.079							0.086
	benzo[g,h,i]perylene				0.077							0.15
	benzo[g,h,i]fluoranthene				0.056							0.078
	benzo[j]fluoranthene				0.033							0.12
	benzo[k]fluoranthene				0.022							0.18
	chrysene				0.048							0.13
	coronene				0.037							0.088
	cyclopenta[c,d]pyrene				0.29							0.48
	dibenz[a,h]anthracene				0.013							0.16
	dibenzo[a,e]pyrene				0.026							0.070
	dibenzo[a,h]pyrene				0.074							0.32
	dibenzo[a,i]pyrene				0.11							0.049
	dibenzo[a,l]pyrene				0.052							0.076
	fluoranthene				0.19							0.13
	fluorene				0.23							0.12
	indeno[1,2,3-cd]pyrene				0.033							0.16
	phenanthrene				0.38							0.073
	pyrene				0.095							0.11
	u 0 :value of standard uncertainty at zero concentration
	rsu: value towards which the combined relative standard uncertainty tends for high concentrations of analyte
	Measurement results should be reported as	y	±	2	×	u	2 0	+	rsu	2	y	2	where y is the measurement result and
	the range of values is equivalent to a confidence interval of approximately 95%.

Table 6 : Measurement Uncertainty for PAHs in vegetable oil % Relative Standard Uncertainty

 6

	Spike level (µg/kg)	0.1	1	10	100
	pyrene	29.0	21.1	21.0	21.0
	chrysene	25.5	15.9	15.8	15.8
	benzo[k]fluoranthene	26.2	17.0	16.9	16.9
	benzo[a]pyrene	26.2	17.0	16.9	16.9
	indeno[1,2,3-cd]pyrene	25.5	15.9	15.8	15.8
	benzo[g,h,i]perylene	25.5	15.9	15.8	15.8

Table 7 : Measurement performance for low concentrations of PAHs in vegetable oils

 7

	Analyte	Critical	Minimum	HORRAT=1
		level / µg/kg	detectable	/ µg/kg
			concentration /
			µg/kg
	5-methylchrysene	0.27	0.45	0.44
	acenaphthene	0.74	1.3	1.30
	acenaphthylene	0.66	1.1	1.09
	anthanthrene	0.28	0.74	rsu>HoR
	anthracene	0.24	0.41	0.42
	benz[a]anthracene	0.078	0.14	0.15
	benzo[a]pyrene	0.48	0.86	0.98
	benzo[b]fluoranthene	0.19	0.34	0.44
	benzo[b]naphtho[2,1-d]thiophene	0.21	0.36	0.36
	benzo[e]pyrene	0.24	0.40	0.39
	benzo[g,h,i]perylene	0.23	0.41	0.49
	benzo[g,h,i]fluoranthene	0.17	0.29	0.27
	benzo[j]fluoranthene	0.099	0.17	0.18
	benzo[k]fluoranthene	0.067	0.12	0.18
	chrysene	0.14	0.25	0.27
	coronene	0.11	0.19	0.18
	cyclopenta[c,d]pyrene	0.88	14	rsu>HoR
	dibenz[a,h]anthracene	0.039	0.069	0.087
	dibenzo[a,e]pyrene	0.078	0.13	0.12
	dibenzo[a,h]pyrene	0.22	0.53	rsu>HoR
	dibenzo[a,i]pyrene	0.32	0.53	0.49
	dibenzo[a,l]pyrene	0.16	0.27	0.25
	fluoranthene	0.58	1.0	1.1
	fluorene	0.69	1.2	1.2
	indeno[1,2,3-cd]pyrene	0.098	0.18	0.22
	phenanthrene	1.1	1.9	1.8
	pyrene	0.28	0.49	0.50

Table 8 (b): Detection of the addition of

 8

		2 µ µ µ µg/kg PAH to grapeseed oil
	Analyte	Unspiked /	Spiked	Difference	Critical	Addition
		µg/kg	/ µg/kg	/ µg/kg	Level /	Detected
					µg/kg
	5-methylchrysene	0.03	2.22	2.19	0.27	y
	acenaphthene	0.35	2.36	2.01	0.74	y
	acenaphthylene	0.19	2.13	1.94	0.66	y
	anthanthrene	2.47	2.41	-0.06	0.28	N
	anthracene	0.84	2.80	1.96	0.24	y
	benz[a]anthracene	9.62	11.56	1.94	0.08	y
	benzo[a]pyrene	20.49	22.31	1.82	0.48	y
	benzo[b]fluoranthene	12.12	13.08	0.96	0.19	y
	benzo[b]naphtho[2,1-d]thiophene	0.68	2.36	1.68	0.21	y
	benzo[e]pyrene	16.90	19.80	2.90	0.24	y
	benzo[g,h,i]perylene	20.63	23.50	2.87	0.23	y
	benzo[g,h,i]fluoranthene	13.20	15.59	2.39	0.17	y
	benzo[j]fluoranthene	9.80	10.75	0.95	0.10	y
	benzo[k]fluoranthene	3.95	5.44	1.48	0.07	y
	chrysene	19.81	20.94	1.13	0.14	y
	coronene	8.46	10.804	2.34	0.11	y
	cyclopenta[c,d]pyrene	52.74	53.35	0.61	0.88	N
	dibenz[a,h]anthracene	0.86	2.73	1.87	0.04	y
	dibenzo[a,e]pyrene	1.21	3.50	2.29	0.08	y
	dibenzo[a,h]pyrene	0.04	0.466	0.43	0.22	y
	dibenzo[a,i]pyrene	0.19	2.302	2.12	0.32	y
	dibenzo[a,l]pyrene	1.51	3.71	2.20	0.16	y
	fluoranthene	18.75	22.84	4.08	0.58	y
	fluorene	0.56	2.58	2.02	0.69	y
	indeno[1,2,3-cd]pyrene	12.82	14.62	1.81	0.10	y
	phenanthrene	7.34	9.09	1.75	1.13	y
	pyrene	47.67	49.94	2.28	0.28	y
	y = yes; N = no

Table 8 (c): Detection of the addition of 2µ

 8

		µ µ µg/kg PAH to toasted sesame oil
	Analyte	Unspiked /	Spiked	Difference	Critical	Addition
		µg/kg	/ µg/kg	/ µg/kg	Level /	Detected
					µg/kg
	5-methylchrysene	0.02	2.22	2.19	0.27	y
	acenaphthene	3.88	5.48	1.60	0.74	y
	acenaphthylene	1.65	3.68	2.03	0.66	y
	anthanthrene	0.02	1.07	1.05	0.28	y
	anthracene	1.75	3.94	2.18	0.24	y
	benz[a]anthracene	0.52	2.56	2.05	0.08	y
	benzo[a]pyrene	0.26	2.19	1.93	0.48	y
	benzo[b]fluoranthene	0.39	2.36	1.97	0.19	y
	benzo[b]naphtho[2,1-d]thiophene	0.56	2.44	1.88	0.21	y
	benzo[e]pyrene	0.36	2.52	2.16	0.24	y
	benzo[g,h,i]perylene	0.23	2.24	2.00	0.23	y
	benzo[g,h,i]fluoranthene	0.29	2.09	1.80	0.17	y
	benzo[j]fluoranthene	0.18	2.35	2.17	0.10	y
	benzo[k]fluoranthene	0.15	2.04	1.90	0.07	y
	chrysene	1.59	3.56	1.97	0.14	y
	coronene	0.058	2.19	2.13	0.11	y
	cyclopenta[c,d]pyrene	0.04	2.06	2.01	0.88	y
	dibenz[a,h]anthracene	0.07	2.07	2.00	0.04	y
	dibenzo[a,e]pyrene	0.04	2.23	2.19	0.08	y
	dibenzo[a,h]pyrene	0.006	1.16	1.15	0.22	y
	dibenzo[a,i]pyrene	0.016	2.12	2.11	0.32	y
	dibenzo[a,l]pyrene	0.09	2.19	2.10	0.16	y
	fluoranthene	3.51	5.62	2.11	0.58	y
	fluorene	10.24	12.47	2.23	0.69	y
	indeno[1,2,3-cd]pyrene	0.20	2.23	2.02	0.10	y
	phenanthrene	21.73	20.24	-1.50	1.13	N
	pyrene	1.64	2.35	0.71	0.28	y
	y = yes; N = no

Table 8 (d): Detection of the addition of

 8

		2 µ µ µ µg/kg /kg PAH to olive margarine
	Analyte	Unspiked /	Spiked	Difference	Critical	Addition
		µg/kg	/ µg/kg	/ µg/kg	Level /	Detected
					µg/kg
	5-methylchrysene	0.01	2.33	2.33	0.27	y
	acenaphthene	0.29	2.30	2.01	0.74	y
	acenaphthylene	0.08	2.07	1.99	0.66	y
	anthanthrene	0.01	0.37	0.36	0.28	y
	anthracene	0.04	2.07	2.04	0.24	y
	benz[a]anthracene	0.16	2.14	1.98	0.08	y
	benzo[a]pyrene	0.28	2.19	1.91	0.48	y
	benzo[b]fluoranthene	0.26	2.21	1.95	0.19	y
	benzo[b]naphtho[2,1-d]thiophene	0.07	1.95	1.87	0.21	y
	benzo[e]pyrene	0.29	2.52	2.23	0.24	y
	benzo[g,h,i]perylene	0.29	2.32	2.03	0.23	y
	benzo[g,h,i]fluoranthene	0.07	1.94	1.87	0.17	y
	benzo[j]fluoranthene	0.13	2.28	2.15	0.10	y
	benzo[k]fluoranthene	0.12	2.07	1.95	0.07	y
	chrysene	0.36	2.36	2.00	0.14	y
	coronene	0.11	2.414	2.31	0.11	y
	cyclopenta[c,d]pyrene	0.04	1.26	1.21	0.88	y
	dibenz[a,h]anthracene	0.07	2.00	1.93	0.04	y
	dibenzo[a,e]pyrene	0.06	2.46	2.40	0.08	y
	dibenzo[a,h]pyrene	0.00	0.354	0.35	0.22	y
	dibenzo[a,i]pyrene	0.02	2.092	2.07	0.32	y
	dibenzo[a,l]pyrene	0.13	2.37	2.24	0.16	y
	fluoranthene	0.38	2.35	1.97	0.58	y
	fluorene	0.43	2.46	2.03	0.69	y
	indeno[1,2,3-cd]pyrene	0.24	2.22	1.98	0.10	y
	phenanthrene	0.71	2.81	2.10	1.13	y
	pyrene	0.40	2.41	2.00	0.28	y
	y = yes; N = no

Table 8 (d): Detection of the addition of

 8

		Food Additives and Contaminants		Page 32 of 36
		2 µ µ µ µg/kg PAH to palm oil
	Analyte	Unspiked /	Spiked	Difference	Critical	Addition
		µg/kg	/ µg/kg	/ µg/kg	Level /	Detected
					µg/kg
	5-methylchrysene	0.02	2.33	2.31	0.27	y
	acenaphthene	1.26	3.20	1.94	0.74	y
	acenaphthylene	0.20	2.12	1.92	0.66	y
	anthanthrene	0.01	0.40	0.39	0.28	y
	anthracene	0.12	2.14	2.02	0.24	y
	benz[a]anthracene	0.05	1.97	1.92	0.08	y
	benzo[a]pyrene	0.14	2.03	1.89	0.48	y
	benzo[b]fluoranthene	0.14	2.04	1.90	0.19	y
	benzo[b]naphtho[2,1-d]thiophene	0.08	1.86	1.77	0.21	y
	benzo[e]pyrene	0.17	2.32	2.15	0.24	y
	benzo[g,h,i]perylene	0.28	2.26	1.99	0.23	y
	benzo[g,h,i]fluoranthene	0.10	1.93	1.83	0.17	y
	benzo[j]fluoranthene	0.07	2.14	2.07	0.10	y
	benzo[k]fluoranthene	0.05	1.93	1.88	0.07	y
	chrysene	0.16	2.09	1.93	0.14	y
	coronene	0.17	2.478	2.31	0.11	y
	cyclopenta[c,d]pyrene	0.03	1.10	1.07	0.88	y
	dibenz[a,h]anthracene	0.04	2.02	1.98	0.04	y
	dibenzo[a,e]pyrene	0.04	2.41	2.38	0.08	y
	dibenzo[a,h]pyrene	0.01	0.36	0.35	0.22	y
	dibenzo[a,i]pyrene	0.02	2.018	2.00	0.32	y
	dibenzo[a,l]pyrene	0.05	2.30	2.25	0.16	y
	fluoranthene	0.81	2.72	1.90	0.58	y
	fluorene	1.97	3.93	1.96	0.69	y
	indeno[1,2,3-cd]pyrene	0.19	2.16	1.97	0.10	y
	phenanthrene	4.63	6.78	2.15	1.13	y
	pyrene	1.15	3.06	1.92	0.28	y
	y = yes; N = no

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  pyrene and dibenzo[a,h]anthracene

								02-Apr-2004						23:47:59
	XC5639													Scan ES+
	100		27.43											1.10e8 TIC
					34.59
					36.23
		23.81
							47.16
											58.55
	%
													70.04
		22.08 21.45	25.95	27.63	45.22 44.81 o F r	52.22		61.22 60.80	69.79 67.86	71.33	79.51 77.77 80.16
			24.86	31.72		P 44.58 51.60					72.11
	0	20.00	25.00	30.00	35.00	40.00	45.00	50.00 e 55.00	60.00	65.00	70.00	75.00	80.00	Time
	xc5622							e r			Scan ES+
	100		77.79				79.53		R			302 6.84e6
											80.18	e v i e
														w
	%										80.53			O n l
															y
	0													Time
		77.00	77.50	78.00	78.50	79.00		79.50	80.00	80.50	81.00	81.50	82.00
	Figure 1f:	Resolution of dibenzo[a,l]pyrene, dibenzo[a,e]pyrene,
	dibenzo[a,i]pyrene and dibenzo[a,h]pyrene
															36

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.ukFood Additives and Contaminants


Sara Lucy Stead

Marianne Caldow

Anisha Sharma

Helen Mary Ashwin

Angelique De-Rijk

Jaques Stark

A new

Keywords: antimicrobial screening, Premi®Test, metal ion chelation Additives/Contaminants: Veterinary drug residues -tetracycline, Antimicrobial compounds Tetracyclines, Premi ® Test, metal ion chelation, antimicrobial screening assay, food safety

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Tetracyclines are antibiotics that inhibit protein synthesis by preventing the attachment of aminoacyl-tRNA to the ribosomal acceptor (A). Tetracycline molecules comprise a tetracyclic nucleus to which various groups are attached, the general structure is shown in Figure 1.

[Insert Figure 1 about here]

Tetracyclines are broad-spectrum agents that exhibit activity against a wide range of Gram-positive and Gram-negative bacteria; they have been widely used in both human and veterinary medicine. In some countries the use of antibiotics has been expanded to include addition, at sub-therapeutic levels, to animal feeds for the purpose of growth promotion. The prevalence of bacterial resistance to the tetracycline class of antibiotics has increased following the widespread usage of these compounds within clinical, veterinary and agricultural practice. The spread of bacterial resistance has major implications to human health (World Health Organisation, 2006).

Under EU Legislation, members of the tetracycline class of antimicrobial compounds are permitted for use in the treatment of bacterial diseases in food-producing animal species. Maximum Residue limits (MRLs) have been set under Council Regulation (EEC) No. 2377/90 (as amended) ranging from 100 -600 µg kg -1 dependent on the species and tissue type. The current MRL for tetracycline residues in muscle tissue of Previous studies [START_REF] Stead | Meeting maximum residue limits: an improved screening technique for the rapid detection of antimicrobial residues in animal food products[END_REF]Stead et al. 2005) have shown that the Premi ® Test is a cost-effective screening tool capable of detecting penicillins, cephalsporins, sulfonamides, tetracyclines and some macrolides in a wide range of tissues from a variety of species; including muscle, liver and kidney along with eggs and fish. [START_REF] Carson | Simultaneous determination of multiple tetracycline residues in milk using metal chelate affinity chromatography[END_REF]. Calcium forms a 2:1 metal-ion to ligand complex.

Formation of the calcium complex involves the addition of one metal ion to the C-10, C-11 site with subsequent addition of a second metal ion at the C-12 and C-1 site [START_REF] Newman | Circular dichroism spectra of tetracycline complexes with Mg+2 and Ca+2[END_REF]. Using a calcium and sodium containing buffer, metal ion chelation was investigated as a mechanism for disrupting the antimicrobial activity of tetracyclines and thus selectively reversing the primary Premi ® Test positive response into a secondary negative response. It is postulated that, on formation of the metalion chelation complex tetracyclines can no longer permeate the bacterial cell membrane via binding to the active transport mechanism, the Tet(R) efflux pump [START_REF] Chopra | Tetracycline antibiotics: Mode of action, applications, Molecular biology and epidemiology of bacterial resistance[END_REF]. validated [START_REF] Stead | Meeting maximum residue limits: an improved screening technique for the rapid detection of antimicrobial residues in animal food products[END_REF]Stead et al. 2005) chlortetracycline, determination of false positive and false negative rates, analyte specificity and ruggedness. A single blind trial was also conducted whereby six samples (a mixture of spikes and blanks) were prepared and blind coded by one analyst and analysed by a second analyst in accordance with the integrated antimicrobial screening procedure [START_REF] Stead | Meeting maximum residue limits: an improved screening technique for the rapid detection of antimicrobial residues in animal food products[END_REF]Stead et al. 2005), with the addition of the newly developed tetracycline assay. The tetracycline selective assay was transferred to a second laboratory, DSM (Delft, The Netherlands) for an interlaboratory assessment of the method in combination with the previously reported fluid expression extraction procedure (EuroResidue IV, [START_REF] Offereins | EuroResidue IV Conference poster presentation[END_REF].

This paper reports the development and validation of a third post-screening assay, selective for tetracyclines. The use of these simple secondary-screening assays allows a suspect positive sample to be reliably directed to the appropriate chemical method for confirmatory analysis.

Materials and methods

Chemicals and reagents

Instrumentation -The scanner system

The Premi ® Test end-point was measured instrumentally using a flatbed scanner in combination with the Premi ® Scan (custom version) software, provided by DSM (Delft, The Netherlands). A scanner value (Z score) of greater than zero was classified as a positive result and a scanner value of zero or lower was classified as a negative result.

Samples

The validation experiments were undertaken using known blank chicken muscle. The UK National Bee Unit, based at CSL, York, supplied honey (wild-flower blossom set honey), for the matrix specificity experiments.

TCN buffer (calcium containing buffer) preparation

Sample extraction procedure

To tissue (4 g), acetonitrile: acetone 70:30% (10 mL) and anhydrous sodium sulphate (6 g) were added prior to homogenisation for 45 seconds. The extract was centrifuged (4500 rpm, 4 o C, 10 minutes), the supernatant (8 mL) was removed and evaporated to near dryness under a stream of nitrogen at 35-40 o C and then re-suspended in Lab

Lemco Broth (600 µL). The extract (100 µL) was then applied directly to the Premi ® Test, the ampoules were sealed using plastic film and the incubation was carried out according to the test kit instructions (DSM webpage, Premi ® Test demo).

Post screening method for tetracyclines (TCN assay)

To a portion of extract (100 µL) TCN buffer (50 µL) [or water (50 µL) in the case of blank and spiked controls] was added, vortex mixed for approximately 30 seconds and allowed to stand at room temperature for 10 min. Treated extract (100 µL) was applied to the Premi ® Test and incubated as described above. Initial experiments showed that the incubation of the sample extract with calcium and sodium ions in TCN buffer selectively reversed the primary screen positive response for tetracyclines observed on the Premi ® Test. In these initial experiments the quantity of TCN buffer required was also optimised (data not shown). On the basis of this mechanism the validation study was conducted.

The result of the specificity experiment is summarised in graphical format in Figure 3.

These data show the Z score values recorded using the Premi ® Scan scanner system following incubation of extracts on the Premi ® Test with and without the addition of TCN. This experiment was conducted for a variety of primarily Gram-positive inhibitory antimicrobial compounds including; penicillin G, cloxacillin, sulfadiazine, tylosin and a range of tetracycline compounds. The primary matrix was chicken muscle, although a set of honey samples was also included to assess matrix-to-matrix variations.

These data indicate that, in the presence of the TCN buffer, the control (positive)

response recorded for the tetracyclines; oxytetracycline, doxycycline, tetracycline and chlortetracycline spiked into chicken muscle at the MRL concentration of 100 µg kg -1 is reversed to a negative response. In the case of the non-tetracycline class compounds spiked into muscle (at or above their respective MRL concentrations) the control positive response is not significantly affected by the addition of the TCN buffer. This finding demonstrates that the addition of TCN buffer to sample extract Currently there are no approved antibiotics for the treatment of European Foul Brood within the EU. Nevertheless oxytetracycline has been detected in retail honey samples. Therefore, in order to investigate the wider applicability of this assay honey was spiked with oxytetracycline at 100 µg kg -1 , following the extraction procedure the extracts were treated with TCN buffer and applied to the Premi ® Test. These results, shown in Figure 3, indicate that TCN assay performs comparably in both muscle tissue and the second matrix type, honey.

[Insert Figure 3 µg kg -1 were analysed. Twenty samples out of the twenty-one were found to give a positive response, therefore the qualitative CCβ (β error of 5 %) was determined to be less than or equal to 100 µg kg -1 , equivalent to the MRL for tetracyclines in muscle tissue. This finding also indicates the false negative rate of the Premi ® Test primary screen for tetracyclines is equivalent to 5%.

The 21 sample extracts were also treated according to the TCN assay procedure (Figure 2b) and applied to the Premi ® Test. In the presence of TCN, all 21 samples gave a negative test response identifying the presence of a tetracycline. This result suggests that the probability of falsely recording a tetracycline positive using the TCN assay is zero at the 95% confidence interval.

An inter-laboratory assessment of the TCN post-screening assay was conducted at DSM (Delft, The Netherlands) using the alternative fluid expression procedure (data not shown). This simple extraction procedure, which is based on the use of a garlic press to release meat juices, can be performed without the use of any laboratory reagents or equipment and is applicable for use as a field test. A total of 10 known blank chicken muscle samples were fortified with oxytetracycline at 200 µg kg -1 (twice the MRL in muscle tissue) extracted following the fluid expression procedure and 100 µL of the resulting fluid treated with 50 µL of TCN as detailed in Figure 2b.

All 10 spiked replicates showed a strong positive control response on the Premi ® Test.

However, following the addition of TCN only 8 out of the 10 extracts produced a negative response. This suggests that the TCN post-screening assay was only 80% Results of the blind trial are shown in Table 1. Using the fully integrated antimicrobial procedure for β-lactams, sulfonamides and tetracyclines all 6 blind coded samples were correctly characterised by the analyst, showing the procedure to be applicable to routine screening. Following treatment with TCN, the strong positive control responses seen for samples 1, 3 and 5 were selectively reversed, the responses remained unaffected by the other post-screening procedures. From these observations the analyst correctly identified the class responsible for eliciting the positive control response as tetracycline. In presence of β-lactamase, the strong positive control response seen for sample 2 was selectively reversed, indicating the presence of a βlactam compound. The test response for sample 4 was found to be negative and not significantly affected by any of the post-screening assays, therefore this sample was recorded as a blank. In the case of sample 6, the strong positive control was not significantly affected by any of the post-screening assays. Following the three postscreening treatments, the strong positive response observed in the control is reduced from approximately 7.5 units to between approximately 5 and 5.5 units. The analyst concluded that this was either the result of multiple classes of compound being present in the extract and the combined antimicrobial potency reduced in the presence [Insert Table 1 about here]

Conclusions

The recently developed and validated TCN assay complements the existing integrated antimicrobial screening strategy in combination with the Premi ® Test and scanner system. The fully integrated strategy offers a rapid, broad-spectrum primary screen for Gram-positive inhibitors with a secondary identification of penicillin, sulphonamide and now tetracycline residues in foods of animal origin.

The metal ion chelation (TCN buffer) approach is cost-effective, requiring the preparation of only basic laboratory reagents and is simple to use. The concentration of calcium and sodium ions and the ratio of TCN buffer to extract were optimised in order to compensate for the differences in the antimicrobial potency within the tetracycline class and the potential for very high residue levels. Thus, a fixed volume of TCN buffer is combined with a fixed volume of extract following the solvent extraction procedure.

The

  is 100 µg kg -1 and no MRLs currently exist for honey. During Defra funded surveillance exercises conducted in the UK between 2001 and 2004 residues of tetracyclines have been found in pig, poultry and calf kidney in the range of 780 -4020 µg kg -1 (Veterinary MedicinesDirectorate, 2006). It is therefore, important that effective screening and confirmatory procedures are available for detecting tetracyclines in foods of animal origin in order to provide legislators and consumers with confidence that the food products entering the food chain are compliant with the current legislation regarding permissible MRLs.

  Test methodology incorporating a solvent extraction and post-screening assays for β-lactam and sulphonamide classes of antimicrobials has been previously

  reagents were of analytical grade and purchased from Sigma-Aldrich (Poole, Dorset, UK) with the exceptions of; Lab Lemco broth and β-lactamase, which were purchased from Oxoid (Basingstoke, Hampshire, UK), acetonitrile, acetone and HPLC grade water were purchased from Fisher Scientific (Loughborough, Leicestershire, UK). The Premi ® Test kits were provided by DSM (Delft, The Netherlands).

Figures

  Figures 2a and 2b outline the existing antimicrobial screening procedure -including

  positive control response of the tetracycline class and does not significantly affect the positive response caused by a range of other antimicrobial compounds.

Figure 4

 4 Figure 4 presents the results of the investigation conducted to determine the efficiency

  the response of a tetracycline following the crude fluid expression procedure. It may be that tissue derived substances in the raw fluid expressed are competing for the divalent calcium ions. Further method optimisation is required in order to improve the efficiency of the TCN assay in combination with the fluid expression extraction.

  -screening treatment or another Gram-positive inhibitory antimicrobial class, for example a macrolide compound.

Figure 3 .(Figure 4 .

 34 Figure3. Effect of TCN on the Premi ® Test response to a range of antimicrobial compounds; 200 µg kg -1 sulfadiazine, 600 µg kg -1 cloxacillin 100 µg kg -1 tylosin 10 µg kg - 1 penicillin-G and a range of tetracycline compounds; doxycycline, tetracycline, oxytetracycline and chlortetracycline at 100 µg kg -1 -fortified chicken muscle and honey

co.uk Food Additives and Contaminants

  Figure 1. The chemical structure of some of the tetracycline compounds Figure 2a. The existing integrated antimicrobial screening procedure -including the postscreening class specific assays for β-lactam and sulfonamide antimicrobial compounds.

	Page 17 of 22 Page 19 of 22	Food Additives and Contaminants Food Additives and Contaminants	Page 18 of 22
	Figure 2b. The integrated antimicrobial screening procedure -including the new post-
	screening detection of a range of tetracyclines when present below, at and above the Stead S, Richmond S, Sharman M, Stark J, Geijp E. 2005. A new approach for screening class specific assay for tetracyclines.
	MRL concentration in both chicken muscle tissue and a secondary matrix, honey. detection of antimicrobial drugs in food -Premi ® Test coupled to scanner technology.
	Analytica Chimica Acta 529: 83-88. Sample (4g) Sodium Sulfate (6g) Extract (500 µL) stored at 4 o C (for post-screening	Acetonitrile/ acetone (70:30v/v) (10 ml)
	analysis if required)
	F o r P e e r R e v i e w O n l F o r P e e r R e v i e w O n l F o r P e e r R e v i e 1. Homogenise (45 seconds) World Health Organisation Webpage, Foodbourne Diesease Surveillance. Available at: http://www.who.int/foodborne_disease/resistance/en/ Accessed 2006 April 12. Veterinary Medicines Directorate, Defra Webpage, MAVIS publications Available at: http://www.vmd.gov.uk/Publications/MAVIS/mavis.htm Accessed 2006 April 12. F o r P e e r R e v i e w Conserved structure Compound R 1 R 2 R 3 R 4 Tetracycline H OH CH 3 H Chlortetracycline Cl OH CH 3 H Oxytetracycline H OH CH 3 OH Doxycycline H CH 3 H OH F o r P e e r R e v i e w 2. Centrifuge (4500rpm 4 o C for 10 minutes) 3. Remove 8 ml of supernatant 4. Evaporate supernatant to near dryness 35-40 o C 5. Resuspend in Lab Lemco broth (600 µL) Extract (500 µL) stored at 4 0 C (for further analysis if required) 100 µL of extract applied to the Premi ® Test Incubate ampoules at 64 o C until Test produces a YELLOW colour F o r P Test produces a YELLOW F o Sample contains a TETRACYCLINE r Tetracycline assay + 50 µL TCN buffer and apply to Premi ® Test colour residue P yellow colour change in negative control Negative = Sample does NOT contain a sulfonamide, β-e e r Test produces a PURPLE colour Sample contains a NON-TETRACYCLINE residue e e r lactam or Test produces a PURPLE colour tetracycline Positive = confirmation of class identity required Sulfonamide assay + 50 µL of p-ABA apply to Premi ® Test Test produces a YELLOW colour Test produces a PURPLE colour Sample contains a sulfonamide residue Sample contains a non-sulfonamide residue R e v i e w R e v i e w w O l n O n l y O n l y β-lactam assay +20 µL of β-lactamase apply to Premi ® Test Test produces a YELLOW colour Sample contains a β β β β-lactam residue O n O n Test produces a PURPLE colour Sample contains a non-β-lactam residue l y l y
			y y y
	TCN assay has been successfully validated in accordance with the 2002/657/EC
	guidelines for qualitative screening methods. The method was	shown to be
	applicable to two different matrix types; class specific and applicable to the post-
		13 15

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Acknowledgments

We acknowledge the funding of this work by the Defra Veterinary Medicine Directorate.