Andrew Turcu

Dabalus Marivil

Mariana Schweikert Islam

Turcu

Andrew Cannavan
email: a.cannavan@iaea.org

Comparison of Methods for the Estimation of Measurement Uncertainty for an Analytical Method for Sulphonamides

Keywords: Chromatography -HPLC, In-house validation Additives/Contaminants: Veterinary drug residues -sulphonamides uncertainty, top-down, bottom-up, sulphonamides, HPLC, decision limit

Comparison of Methods for the Estimation of Measurement Uncertainty for an Analytical Method for Sulphonamides

Marivil Dabalus Dabalus Islam, Mariana Schweikert

Introduction

To conform to Codex Standards and national and international regulations, analytical methods used to test compliance with regulatory limits for contaminants in food must be validated to demonstrate that they are 'fit for purpose'. The validation includes parameters such as accuracy, repeatability/reproducibility, sensitivity, ruggedness and limit of detection, and an estimate of the measurement uncertainty associated with the method. Measurement uncertainty is defined by the International Organization for Standardization (ISO) (Anon. 1993a) as 'a parameter associated with the result of a measurement that characterizes the dispersion of the values that could reasonably be attributed to the measurand'. It is a requirement for laboratories working under the ISO 17025 quality system (Anon, 2005) that the measurement uncertainty associated with a result should be made available and reported if it is required by the client, is relevant to the validity of the test results, or if it may affect compliance with a specification, for example compliance with a maximum residue limit for veterinary drugs or pesticides in food.

The traditional method for estimating measurement uncertainty is by a 'bottom-up' approach, as described by EURACHEM/CITAC (Anon. 2000). Also referred to the as the uncertainty budget, this approach aims to estimate the individual contribution of every step and input to the analytical process to the overall uncertainty, and can be time consuming and difficult to apply to analytical methods for food contaminant regulation. This approach was developed by metrologists and physicists and proposed by ISO in the 'Guide to the expression of uncertainty in measurement' (GUM) (1993b;[START_REF]veterinary drug residues in foods. FAO/WHO[END_REF], and subsequently adopted by EURACHEM for analytical chemistry.

Another way of estimating the measurement uncertainty of analytical results is a more practical 'top-down' approach, as described by, inter alia, EURACHEM (Anon. 2000) and ISO (Anon. 2004) using the data obtained from inter-laboratory studies. However, this method is not ideal for application in residues laboratories, because it assumes that a standardized method is used in all laboratories, which is generally not true for residues analysis. One option is to apply a 'top-down' approach using the data generated by the in-house validation of the method. This approach is relatively simple, requires no extra practical work, and provides an initial uncertainty estimate that can be updated if necessary as further data becomes available through routine application of the method, for example from proficiency test results or repeated analysis of samples (Anon. 2007a).

Many laboratories, especially those in developing countries, have difficulty with the estimation of the measurement uncertainty associated with analytical methods [START_REF] Cannavan | Third world regulatory concerns and collaborative work to protect consumers and facilitate international trade[END_REF][START_REF] Cannavan | The implementation of veterinary drug residues monitoring programmes in developing countries[END_REF]. This laboratory has had many requests for assistance in this area from developing simple liquid-chromatographic multiresidue method for the determination of sulphonamides in animal tissues as a model to evaluate different practical approaches to the estimation of measurement uncertainty. The method had been developed and undergone preliminary validation in our laboratory as a suitable method for application in regulatory laboratories in resource-limited developing countries as well as in the developed world. The sulphonamides are an important class of antibacterial drugs widely used in veterinary practice and animal production. The European Union has set a maximum residue limit (MRL) of 100 µg kg -1 for the sulphonamides in a range of species and tissues (Anon. 1992), and Codex Alimentarius has adopted the same MRL for sulphamethazine in animal tissues (Anon. 2006). The method was validated in line with EU Commission Decision 2002/657/EC (Anon. 2002), including calculation of the decision limit (CCα) and the detection capability (CCβ). Two methods were used for the uncertainty estimation.

The first, a 'bottom-up' approach, was based on the approach of Štĕpán et al. (2004), which included some elements of a 'top-down' estimation in that it included repeatability data generated by fortification experiments at different stages of the method to estimate the contribution of each step to the overall uncertainty. The 'bottom-up' approach was also used to identify critical steps in the analytical procedure. The uncertainties associated with the gravimetric and volumetric measurements and analytical standards were estimated and integrated in the calculation of the total combined uncertainty using the 'bottom-up' approach. The second was a 'top-down' method, based on in-house validation data. The data were treated in two ways; as an overall intralaboratory reproducibility value and as three separate values generated from repeatability experiments performed on separate occasions and combined to produce an uncertainty estimate.

In both top-down and bottom-up approaches, it is considered that the uncertainty of each analytical step consists of random and systematic error components; hence estimates for both components were incorporated into the combined standard uncertainty.

The uncertainty estimates in this study were calculated and presented as relative standard uncertainties (%), which permits their application over a range of values around and close to a reference point such as a maximum residue limit. All solvents were of HPLC grade and other chemicals were of analytical reagent grade, unless otherwise stated. Ethyl acetate, methanol, acetonitrile, glacial acetic acid (99.8%), hydrochloric acid (32%), n-hexane (UniSolv) and anhydrous sodium sulphate were all purchased from Merck (Darmstadt, Germany). Ultra-pure water was produced in the laboratory with a Milli-Q gradient system produced by Millipore (Molsheim, France) and was used throughout the study. Sulphadiazine (SDZ), sulphathiazole (STZ,), sulphapyridine (SPD), sulphamerazine (SMR), sulphamethazine (SMT), sulphamethizole (SMZ), sulphamethoxypyridazine (SMP) and pdimethylaminobenzaldehyde were purchased from Sigma-Aldrich (Steinheim, Germany). Stock solutions (1 mg ml -1 ) were prepared for each individual standard in methanol and were stable for at least 3 months when stored at -18 ºC. An intermediate mixed standard solution (10 µg ml -1 ) was prepared by dilution of the stock standards in methanol and was stable for at least 1 month at 4 ºC.

Materials and methods

Chemicals and reagents

Working standard solutions were freshly prepared for every chromatographic run by diluting the intermediate mixed standard with mobile phase and stored at 4 ºC.

Porcine kidney was purchased from a supermarket, sub-sampled, chopped and homogenized thoroughly before dividing into several analytical portions, and deep-frozen. One analytical portion was analysed by HPLC before freezing and found to be negative for sulphonamides.

Extraction and sample processing

Portions (3 g) of the homogenized porcine kidney tissue were weighed into centrifuge tubes. If required, samples were spiked by the quantitative addition of standards in methanol, as described in the 'Method validation' and 'Experimental design for bottom-up uncertainty estimation' sections below, and allowed to stand at room temperature for at least 20 minutes. Hydrochloric acid (0.1M, 500 µL) was added and each tube was vortex-mixed at full speed for 20 seconds.

Anhydrous sodium sulphate (2 g) was then added whilst mixing. Ethyl acetate (9 mL) was added and each sample was homogenised immediately after addition using an Ultra-turrax homogeniser (IKA, Germany) at 9500 rpm for 45 seconds. The resulting mixture was centrifuged (650 g, 4 ºC, 10 minutes). An aliquot (6 mL) of the supernatant was transferred into a conical test tube and evaporated to dryness using a Turbo-Vap (Zymark, Hopkinton, MA, USA) at 55 ºC under a stream of nitrogen. The residue was reconstituted in methanol (100 µL), glacial acetic acid (40 µL) and water (360 µL) with vortex mixing at each addition. The extract was washed with 2 mL of n-hexane by vortex mixing at low speed (low speed mixing avoided emulsion formation). The aqueous layer was transferred into an auto-sampler vial for HPLC-UV analysis.

High Performance Liquid Chromatography and data processing

Chromatography was performed using a Dionex (Sunnyvale, CA, USA) system equipped with automated sample injector (ASI-100), photodiode array detector (PDA-100), binary pumps (P680), degassing system (DG-1210) and thermostatted column compartment (TCC-100).

A post-column derivatization apparatus was manually assembled using a 5 m x 0.5 mm i.d. coil of peek tubing (VICI International) and connected to a post-column pump and the analytical column via a zero dead-volume T-piece. The outlet of the reaction coil was connected to the detector. A Luna C18(2) 250 x 4.6 mm x 5 µm column with a C18, 4 x 3 mm guard column (Phenomenex, Macclesfield, Cheshire, UK) was used for chromatographic separations. The injection volume was 50 µL. The column was maintained at 25 ºC and the post-column coil was at ambient temperature.

The UV detector wavelength was set to 450 nm.

A pre-mixed mobile phase comprising methanol/acetonitrile/glacial acetic acid/water (10/6.5/1.5/82, v/v/v/v) was pumped at a flow rate of 1.2 mL min -1 in isocratic mode for a total run time of 21 minutes. A derivatizing solution containing p-dimethylaminobenzaldehyde (1.5 g) in a mixture of methanol (30 mL), water (20 mL) and glacial acetic acid (50 mL) was delivered by the post-column pump at a constant flow rate of 0.5 mL min -1 . The mobile phase and derivatising solution were filtered by vacuum through a 0.45µm HVLP filter (Phenomenex) before use.

Calibration curves were constructed using five concentrations of mixed sulphonamide standard, plus one 'zero'standard, covering the range required.

Chromeleon software (version 6.5 SP2 Build 968) was used to control all instrument conditions and for processing of the chromatographic data.

Method Validation

The trueness and precision of the analytical method were measured by the analysis of six negative porcine kidney samples fortified with each of the sulphonamides at three concentrations (50, 100 and 150 µg kg -1 , equivalent to 0.5, 1.0 and 1.5 times the Codex or EU MRL) on three separate occasions. The intra-laboratory reproducibility was checked by a second operator on a single occasion using HPLC equipment of similar specifications. The ruggedness of the method was evaluated by introducing minor alterations to seven variables in the extraction procedure and testing method recovery and repeatability against the nominal method. The variables selected were: molarity of the hydrochloric acid at the extraction stage (0.1M versus 0.01M), weight of sodium sulphate added (2 g or 3 g), homogenization time (45 seconds or 90 seconds), centrifugation time/speed (650 g for 10 min or 1200 g for 15 min), evaporation temperature (55 ºC or 80 ºC), evaporation just to dryness before removing from the turbo-vap compared with allowing to remain in the turbo-vap for 10 minutes after evaporation, and the volume of hexane for the final wash step (2 mL or 3 mL). Four replicates of blank matrix spiked at 100 µg kg -1 were tested for each variable.

The decision limit (CCα) and the detection capability (CCβ) were calculated using the calibration curve procedure according to ISO 11843 as described in Commission Decision 2002/657/EC.

Experimental design for bottom-up uncertainty estimation

Eighteen replicates of homogenized negative porcine kidney tissue were weighed into centrifuge tubes as described above. Six replicates were fortified with the analytes at 100 µg kg -1 at three stages of the analytical method, as described below:

Fortification 1 (F1)
Intermediate mixed sulphonamide standard (10 µg ml -1 , 30 µL) was added to each of six portions (3 g) of homogenized negative porcine kidney in centrifuge tubes prior to the extraction step. The spiked samples were mixed briefly and allowed to stand for 20 minutes before commencing the extraction procedure.

Fortification 2 (F2)

Aliquots (6 mL) of the supernatants obtained after the extraction and centrifugation of six negative control samples were transferred into conical test tubes and spiked with mixed sulphonamide standard (10 µg ml -1 , 20 µL) prior to the evaporation step. The succeeding steps of the procedure were completed as described above.

Fortification 3 (F3)

After evaporation of the supernatant from six of the negative control samples, mixed sulphonamide standard (10 µg ml -1 , 20 µL) was added. The sample was reconstituted as described above, but using 80 µL rather than 100 µL methanol, and the final hexane wash step and HPLC analysis were completed.

Results and discussion

Method validation

Typical chromatograms for a negative kidney sample, a mixed sulphonamide standard equivalent to 100 µg kg -1 and a negative kidney fortified at 100 µg kg -1 are presented in Figure 1. The chromatograms are free from interference at the retention times of the analytes. Matrix-matched calibration standards were compared with standards prepared in mobile phase and no differences were observed in the calibration curves, so solvent standards were used thereafter. The method was validated for seven sulphonamides commonly used in veterinary medicine, but was shown to be suitable for the quantitative analysis of a range of sulphonamides, with some alterations to the mobile phase and the use of gradient elution for some compounds (data not presented). The simple isocratic method for seven compounds was chosen as a model for the measurement uncertainty study presented here.

Since certified reference materials for this matrix-analyte combination were unavailable, the trueness and precision of the analytical method were measured by fortification of negative sample matrix, as described above. The results are summarised in Table 1. The precision for all analytes is within the ranges recommended by Commission Decision 2002/657/EC and by Codex CAC GL 16 (Anon. 1993c). Analyte recovery for this matrix ranged from 61.6% to 91.7%, recoveries for sulphathiazole and sulphamethizole being somewhat lower than for the other compounds. The recoveries obtained are acceptable for a multiresidue analytical method. Higher recoveries for sulphathiazole and sulphamethizole may be achieved using adjusted pH [START_REF] Koesukwiwat | Solid-phase extraction for multiresidue determination of sulfonamides, tetracyclines, and pyrimethamine in Bovine's milk[END_REF], multiple extraction stages or, for example, alternative extraction solvents/buffers in combination with solid phase extraction clean-up. However, a key objective in the development of this method was that the method should be inexpensive and simple, making it applicable in a range of laboratories in both developed and developing countries. The simple extraction and minimal clean-up, with specificity enhanced by post-column derivatization, achieves this.

The introduction of minor variations in the seven individual factors described in the 'Method validation' section above caused no significant variation in repeatability or recovery, demonstrating the ruggedness of the method. The protocol employed for ruggedness testing, performing replicate analyses with variations in one factor at a time, was relatively labour intensive. An alternative method, recommended in Commission Decision 2002/657/EC, is to apply a fractional factorial design, as described by [START_REF] Youden | Statistical manual of the AOAC -Association of Official Analytical Chemists[END_REF]. In this approach, The decision limit (CCα) and the detection capability (CCβ) for each analyte are listed in Table 2. These values were calculated using the data from day 2 of the validation. Different values would be obtained using the values from the validation data obtained on alternative days. As noted previously [START_REF] Yakkundi | Development and validation of a method for the confirmation of nicarbazin in chicken liver and eggs using LC -Electrospray MS-MS according to the revised EU criteria for veterinary drug residue analysis[END_REF], there is no clear guidance in Decision 2002/657/EC on how to address this. Adoption of the lowest value of CCα as the decision limit would maximise consumer protection, but would concomitantly increase the risk of α (false non-compliant) errors.

Conversely, adoption of the highest value of CCα would minimise the risk of α errors, but could be viewed as siding too much with the producer. One possible alternative is to pool all the data from the within-laboratory reproducibility experiments for calculation of CCα and CCβ, but this approach also has disadvantages. Since the parameters are calculated from plots of the signal obtained, rather than calculated concentration, against the added concentration, the measurement system must be proven to be stable over the time period of the reproducibility experiments, which may be several days, or even longer, so that the signal data (typically peak area or height) can be pooled. This is not always the case in HPLC systems where detector response, for example, may vary slightly from day to day, depending on factors such as the lifetime of the detector lamp, or in mass spectrometric systems, where the condition of the ion source can cause significant changes in ion abundances obtained between (and even within) analytical sequences. Where a reproducible signal can be demonstrated, or an internal standard is applied and the 'signal' is plotted as a ratio of analyte response to internal standard response, the use of the pooled reproducibility data would provide a more robust estimation of CCα and CCβ.

The method is applied routinely in a separate laboratory for the post-screening quantification of sulphonamides under an EU National Surveillance Scheme. A comparison of the validation data between the laboratories and an evaluation of method performance with 'real' incurred samples is being prepared and will be published elsewhere.

The "bottom-up" method

The identification and estimation of all individual uncertainty components, even for a relatively simple method such as that described here, can be difficult and time consuming. The approach used in this study aimed to identify and quantify the significant contributing factors to overall uncertainty, whilst using repeatability data for three individual steps of the procedure to account for the many other minor contributors inherent in each of the steps. The main sources of uncertainty evaluated using repeatability data were the extraction, the evaporation step, and the final wash/HPLC determination (hereafter referred to as the HPLC step).

In both the 'bottom-up' and 'top-down' approaches, the uncertainty estimation was based on both random and systematic error components of each individual analytical step. Random error typically arises from unpredictable variations of influence quantities, which give rise to variation in the repeated observations of the measurand (Anon. 2000). The uncertainty of the mean due to random effects is represented here by the repeatability of analytical measurements at each of the 3 fortification stages, expressed as the percentage relative standard deviation (or coefficient of variation, CV) of the arithmetic mean of replicate determinations of the analyte as shown in equation (1). where: r 1 represents the combined random error (repeatability) of all individual steps; r 2 is the combined random error of evaporation and HPLC; r 3 is the random error of the HPLC step.

Similarly, the random error of the evaporation step, r Evap, is calculated using equation (3). The repeatability for each analyte fortified at each of the three steps (F1, F2 and F3) and the random error calculated for each step of the method using the equations above are presented in Table 3.

The systematic error, defined as a component of error, which in the course of a number of analyses of the same measurand, remains constant or varies in a predictable way (Anon, 2000), is represented here by the analytical recovery (R). The uncertainty of recovery was estimated assuming a rectangular distribution using equation (4). The systematic component of the uncertainty associated with the individual analytical steps is estimated using equations ( 5), ( 6) and ( 7):

For the extraction step

2 1 2 3 2 2 2 1 ) ) ( ) ( ) ( ( ) ( R u R u R u R u Ex - - = …………………………………………………….(5)
For the evaporation step

( ) 2 1 2 3 2 2 ) ( ) ( ) ( R u R u R u Evap - = ……………………………………………………(6)
For the HPLC step ( ) The estimated uncertainties associated with the recovery for each analyte at each of the three steps evaluated are presented in Table 4. where: S w is the standard deviation of repeated weighings of a 25 mg calibration weight (n=6); m std is the mass of analytical standard used for preparation of stock solution (m=25 mg).

3 2 1 2 3 ) ( ) ( ) ( R u R u R u HPLC = = …………………………………………………… (

Other sources of uncertainty

A systematic error component may be introduced due to, for example, preparation of a stock analytical standard solution when the actual amount of the standard material weighed is somewhere within the balance's stated tolerance, resulting in a bias in analytical results. This factor was estimated using the tolerance interval, a (+ 0.05 mg), declared in the calibration certificate and substituted in equation ( 9). A rectangular distribution is assumed.

Uncertainty of dilution

The random uncertainty of dilution, r dil , was determined using equation (10): Using the confidence interval, b, specified by the supplier of the volumetric flask (+ 0.04 mL), the systematic error component was calculated using equation (11):

100 ) 3 ( (%) 2 1 × × = V b dil σ …………………………………………………………………(11)
where: V is the volume of volumetric flask (25 mL). The stated purity of each analytical standard was taken into account when preparing standard solutions. It was assumed that uncertainty due to both standard purity and calibration non-linearity was negligible.

Uncertainty of standard purity & calibration

Calculation of combined relative standard uncertainty

The combined uncertainty of both random and systematic error components associated with extraction, evaporation, HPLC, weighing and, dilution was calculated using equations (12-16):

( ) ( ) 2 1 2 2 (%) Ex Ex Ex R u r u c + = ………………………………………………..…(12) ( ) ( ) 2 1 2 2 (%) Evap Evap Evap R u r u c + = ……………………………………………….….(13) ( ) ( ) 2 1 2 2 (%) HPLC HPLC HPLC R u r u c + = ………………………………………….…….....(14) ( ) 2 1 2 (%) (%) bal bal bal r u c σ + = …………………………………………….….....(15) ( ) 2 1 2 (%) (%) dil dil dil r u c σ + = …………………………………………….
.……( 16)

The combined uncertainty associated with the preparation of stock standard was calculated by integrating the uncertainty of weighing and dilution of the standard using equation ( 17):

( ) 2 1 2 2 (%) cdil cbal cStd u u u + = ...………………………………………………...( 17
)
Using the values calculated for each of the above factors, the combined standard uncertainty, u c (%), associated with the method was calculated using equation ( 18):

( ) 2 1 2 2 2 2 (%) cStd cHPLC cEvap cEx c u u u u u + + + = …………………………………………………(18)
As shown in Table 5, the combined relative standard uncertainties, estimated by the 'bottom-up' method, ranged from 5.1% to 9.0% for the seven analytes.

The individual contribution of each of the parameters investigated to the combined uncertainty of the method for each of the seven sulphonamides is illustrated in Figure 2. The contribution of the extraction step to the combined uncertainty was higher than the other steps in the method for six of the seven analytes. The combined uncertainty arising from weighing and dilution of the analytical standard is minor compared to the uncertainty due to the major stages of the analytical procedure. Components of uncertainty that do not contribute significantly to the total uncertainty of the test result can be neglected (Anon. 2000). The 'top-down' method'

Intra-laboratory reproducibility (top-down method 1)

The intra-laboratory reproducibility data for the method were used in the estimation of the uncertainty by the 'top-down' method. As for the 'bottom-up' method, both random and systematic error components were considered in the calculations. The overall reproducibility for all four days at all fortification levels for each analyte (random error, r ave ), expressed as relative standard deviation, was taken from Table 1. The uncertainty of recovery (systematic error, u(R))

was calculated using equation ( 19):

2 1 ) 3 ( ) 100 ( 5 . 0 ) ( ave ave R R u - × = …………………………………………………………..( 19
)
where

: ave R u )
( is uncertainty of mean recovery for all occasions;

ave R is mean recovery of analyte obtained for all occasions.

The combined uncertainty is represented by equation ( 20):

( ) ( ) 2 1 2 2 (%) ave ave ave c R r u + = ………………………………………………………………….( 20
)
Table 6 shows the two components and the combined uncertainty values for each analyte using the 'top-down' approach.

This approach is in line with that accepted in the EU. Although measurement uncertainty is not explicitly mentioned in Commission Decision 2002/657/EC, the guidelines for implementation of the Decision (Anon. 2004b) specify that, when all relevant influencing factors are taken into account, including uncertainty of recovery, and recovery-corrected data are used, the intralaboratory reproducibility of a method can be regarded as a good estimate of combined uncertainty. Since the reproducibility data were obtained using recovery data, the systematic error component, u(R), fulfils the requirement for the use of recovery-corrected data.

Repeatability data on three occasions (top-down method 2)

The repeatability data(r o1→o3 ), expressed as relative standard deviations, for six replicates of negative matrix spiked at 100 µg kg -1 on three separate days (day 1-3) were used (Table 1). The uncertainty of recovery (systematic error, u(R)) was calculated using equation ( 21):

2 1 3 1 3 1 ) 3 ( ) 100 ( 5 . 0 ) ( o o o o R R u → → - × = ………………………………………………………… (21)
where: The combined uncertainty for each occasion was calculated using equation ( 22):

( ) ( ) 2 1 2 3 1 2 3 1 3 1 (%) o o o o o o c R u r u → → → + = …………………………………………………………..( 22
)
The quadratic mean, representing the combined uncertainty for each analyte, was calculated using equation ( 23):

2 1 2 3 2 2 2 1 3 (%)         + + = o o o cAv c c c u u u u ………………………………………………………….( 23
)
The combined uncertainties estimated using the 'top-down' method ranged from 8.0-12.6%

(method 1, using reproducibility) and 5.4-9.5% (method 2, using repeatabilities) for the seven analytes. Comparison with the values produced using the 'bottom-up' method, which were between 5.1-9.0%, (Table 8) showed that there was no significant difference between the estimates generated by the different approaches. The 'top-down' method using repeatability data can be easily applied where a method has undergone a preliminary validation, and can provide a realistic estimate of uncertainty which can be further elaborated upon additional validation.

Compliance assessment

In the field of veterinary drug residues, the result of an analysis is frequently used to decide whether it indicates compliance or non-compliance with a regulatory limit. One decision rule that is currently widely applied is that a result implies non-compliance with a limit if the measured value exceeds the limit by the expanded uncertainty (Anon. 2007b). The expanded uncertainty provides an interval which may be expected to encompass a large fraction of the distribution of values which could reasonably be attributed to the measurand and is obtained by multiplication of the combined standard uncertainty by a coverage factor. A coverage factor of 2 is generally recommended unless the combined uncertainty is based on statistical observations with less than about six degrees of freedom, in which case a greater factor may be required (Anon, 2000), but the factor is typically in the range 2 -3. In EU legislation a decision rule is applied that provides a low probability of false rejection, or a high confidence of correct rejection. Commission Decision 2002/657/EC specifies that the result of an analysis shall be considered non-compliant if the decision limit (CCα) of the method is exceeded. The decision limit is based on the α error, which is the probability that the test sample is compliant, even though a non-compliant measurement has been obtained (false non-compliant decision), and is the concentration above which it can be decided with a statistical certainty of 1-α that the permitted limit has truly been exceeded. In the case of substances with established MRLs, such as the sulphonamides, α = 5%, whereas for compounds with no established permitted limit, α = 1%, giving an even lower probability of a false rejection. For the control of compliance, the measurement uncertainty is taken into account by applying the decision limit, CCα. (Anon. 2004b). To ensure consistent regulation, it is of the utmost importance that both the decision rule to be applied and the method of calculating the critical value are fully specified and agreed by the regulating body or competent authority and the laboratory.

Conclusions

The analytical method described was validated using in-house performance-based criteria and was

shown to be suitable for screening or for quantitative analysis of a range of sulphonamide drugs.

By choosing the fortification levels for validation experiments in line with EU Commission

Decision 2002/657/EC, the data produced to characterize the method in terms of precision and recovery could also be used to calculate values for CCα and CCβ, in line with the requirements of the EU. The method is cheap, simple and rapid and is suitable for application in both developed and developing country regulatory laboratories.

The method was used as a model to compare two different approaches to the estimation of uncertainty. The 'bottom-up' method is a relatively comprehensive and exhaustive approach, which required more time and care in designing and performing experiments to provide additional data for the estimation. This approach does, however, enable the analyst to identify the critical stages of the method and is useful in method optimization or troubleshooting during development.

The 'top-down' approach is much simpler and more cost-effective, using data from the method validation process. This would be especially relevant in situations where a laboratory wishes to validate an existing method (such as a Codex endorsed method, or a method from the literature) for routine use. Since there was no statistical difference between the uncertainty values obtained by either approach, for this analytical method the analyst would be justified in applying a 'topdown' estimation using method validation data, rather than performing additional experiments to obtain uncertainty data. However, the approach used for uncertainty estimation must always be agreed by the customer of the laboratory, and by any accrediting or certifying body. The use of uncertainty information is important in compliance assessment with respect to MRLs and other regulatory levels. It is imperative that the criteria to be applied in making a decision on compliance or non-compliance of an analytical result, and the methods for calculating the decision limit, are specified by regulatory authorities and laboratories to ensure conformity of application between laboratories.

F

(%) R 1 R 2 R 3 u(R) 1 u(R) 2 u(R) 3 u(R) Ex u(R) Evap u(R

  introduced at once, minimising the number of analyses, time and effort required to detect influences on the measurement results.

  = the arithmetic mean and S is the standard deviation of repeated determinations (n=6); 3 1→ r = repeatability of analyte for each individual step; expressed as CV (%). The results generated by the samples fortified before the extraction step include the random errors of not only the extraction step, but also the subsequent evaporation and HPLC steps. The random error component of the uncertainty of the extraction step, r Ex, is therefore calculated using equation (..........…………………………………………………..(2)

  the HPLC step the random error, r HPLC , is represented by the repeatability of the replicates fortified at the final stage of the method.

  analyte recovery for each individual fortification step.

  7) where: u(R) 1 = the systematic uncertainty component of all three steps combined; u(R) 2 = systematic uncertainty component of evaporation and HPLC; u(R) 3 = systematic uncertainty component of the final wash/HPLC step.

  As in the above estimation of uncertainty for individual analytical steps, both the random error and systematic error components were considered in this process. The random uncertainty of the balance, r bal , was determined using equation (8

  ff is the standard deviation of repeated weighings of the volumetric flask filled with methanol (n=6, V=25mL); m ff is the average weight of the volumetric flask filled with methanol; m ef is the average weight of the empty volumetric flask.

  r → represents repeatability of analyte obtained for each occasion;

  recovery of analyte obtained for each occasion.

Figure 1 .

 1 Figure 1. Typical HPLC-UV chromatograms for: (a), a negative kidney sample; (b), a mixed sulphonamide standard equivalent to 100 µg kg -1 and (c), a negative kidney fortified at 100 µg kg -1 .

Figure 2 .

 2 Figure 2. Contributions of the uncertainty of the different stages of the analytical method and other uncertainty sources to the overall uncertainty (CV%).

Table 1 .

 1 Method validation data for porcine kidney at fortification levels equivalent to 0.5 x MRL, MRL and 1.5 x MRL.

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

Table 2 .

 2 Decision limit (CCα) and detection capability (CCβ) values obtained for the seven sulphonamides

		SDZ	STZ	SPY	SMR	SMT	SMZ	SMP
	CCα (µg kg -1 )	106.7	105.6	106.3	107.0	106.8	108.5	108.2
	CCβ (µg kg -1 )	113.2	112.7	113.4	114.4	114.2	119.0	114.8

Table 3 .

 3 Repeatabilities (random error) of individual analytical steps

		F1	F2	F3	Extraction	Evaporation	HPLC
	CV(%)	r 1	r 2	r 3	r Ex	r Evap	r HPLC (= r)
	Sulphadiazine	5.2	3.5	2.0	3.3	2.9	2.0
	Sulphathiazole	5.4	3.5	1.9	3.6	2.9	1.9
	Sulphapyridine	4.6	3.8	1.8	1.9	3.3	1.8
	Sulphamerazine	5.0	3.3	1.9	3.2	2.7	1.9
	Sulphamethazine	5.3	3.2	2.3	3.5	2.2	2.3
	Sulphamethizole	6.2	2.7	2.0	5.2	1.8	2.0
	S'methoxypyridazine	5.7	4.5	3.3	1.2	3.1	3.3

Table 4 .

 4 Uncertainties of recoveries (systematic error) of individual analytical steps

	F1	F2	F3	F1	F2	F3	Extraction Evaporation	HPLC
	CV

  ) HPLC

	Sulphadiazine	89.0	97.5	101.5	3.18	0.72	-0.43	3.06	0.58	-0.43
	Sulphathiazole	74.0	93.0	99.8	7.51	2.02	0.06	7.23	2.02	0.06
	Sulphapyridine	88.3	97.8	101.2	3.38	0.64	-0.35	3.30	0.53	-0.35
	Sulphamerazine	89.2	97.8	102.2	3.12	0.64	-0.64	2.99	0.00	-0.64
	Sulphamethazine	89.3	97.2	101.7	3.09	0.81	-0.49	2.94	0.64	-0.49
	Sulphamethizole S'methoxypyridazine F o r 75.8 80.3	93.2 88.2	102.8 94.5	6.99 5.69	1.96 3.41	-0.81 1.59	6.66 4.77	1.79 3.01	-0.81 1.59
		P
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Table 5

 5 Combined uncertainties of sulphonamide compounds by "bottom-up" method

		u cEx (%)	u cEvap (%) u cHPLC (%)	u cBal (%)	u cDil (%)	u c (%)
	Sulphadiazine	4.491	2.930	0.866	0.069	0.109	5.4
	Sulphathiazole	8.096	3.567	0.110	0.067	0.109	8.8
	Sulphapyridine	3.790	3.389	0.624	0.066	0.109	5.1
	Sulphamerazine	4.406	2.698	1.207	0.066	0.109	5.3
	Sulphamethazine	4.605	2.316	1.129	0.065	0.109	5.3
	Sulphamethizole	8.453	2.548	1.617	0.065	0.109	9.0
	S'methoxypyridazine	4.423	4.294	5.239	0.064	0.109	8.1

Table 6 .

 6 Systematic and random components and combined uncertainty of each sulphonamide by the "top-down" method

		r ave (%)	u(R) ave (%)	u c (%)
	Sulphadiazine	7.0	5.3	8.3
	Sulphathiazole	7.5	9.3	12.0
	Sulphapyridine	6.2	5.6	7.9
	Sulphamerazine	7.0	5.2	8.3
	Sulphamethazine	6.1	4.9	8.2
	Sulphamethizole	9.1	9.0	12.5
	S'methoxypyridazine	6.1	6.1	8.6

Table 7 .

 7 Uncertainties of sulphonamide compounds by 'top-down' method 2, using repeatability data (Operator 1) for negative matrix spiked at 100 µg kg -1

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Table 8 .

 8 Combined uncertainties by "bottom-up" and 'top-down" approach. Top down 'method 1' used intra-laboratory reproducibility, 'method 2' used repeatability data from three occasions.

		u c (%)	u c Av (%)	u c Ave (%)
		Bottom-up	Top-down (method 1)	Top-down (method 2)
	Sulphadiazine	5.4	8.3	6.2
	Sulphathiazole	8.8	12.0	9.3
	Sulphapyridine	5.1	7.9	5.9
	Sulphamerazine	5.3	8.3	5.9
	Sulphamethazine	5.3	8.2	5.4
	Sulphamethizole	9.0	12.5	9.5
	S'methoxypyridazine	8.1	8.6	7.5

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Occasion 1

Occasion 2 Occasion 3

Sulphonamides


Timothy H Begley

Johannes Brandsch

William Limm

Hartmut Siebert

Otto Götz Piringer

Piringer

The diffusion behaviour of additives in polypropylene in correlation with polymer properties

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

The global consumption of polypropylene (PP) steadily increased during the last years to 26 reach 44 million tons in 2006. The improvement in the PP properties stems from a wide 27 into three categories: monophasic homopolymer (h-PP), monophasic random copolymer 32 (r-PP) and heterophasic copolymer (heco-PP). The h-PP can be either isotactic, 33 syndiotactic or atactic; but the isotactic h-PP is particularly useful. Its stereoregularity 34 and the resulting high crystallinity endow isotactic h-PP its outstanding characteristics. 35

Therefore, commercially produced h-PP is up to 95 % isotactic. 36 37

The h-PP is also one of the lightest thermoplastics, having a density ranging from 0.85 to 38 0.91 g/cm 3 . Pure isotactic h-PP has a melting temperature of 176°C. However, the 39 melting temperature of commercial h-PP materials is around 160-165°C with melting 40 beginning around 140°C, which is much higher than PE. The linear polymer chains of r-PP 41 contain copolymers such as ethylene, propylene and butene in a random manner. This 42 structure reduces crystallinity and thus improves the optical clarity. This is the main 43 commercial advantage of r-PP over h-PP. In addition, the wide availability of nucleating 44 agents, known as "clarifiers," further reduce the "haze" by nucleating small spherulites 45 on the surface, thereby reducing surface irregularities. (Duca and Moore, 1996) Another 46 commercial advantage of r-PP over h-PP is its lower melting point. This facilitates, for 47 example, heat-sealing of films. 48 49 Lastly, the heco-PP is a block copolymer made up of h-PP phases and, usually, ethylene-50 propylene rubber (EPR) phases. This combination leads to superior impact strength at 51 low temperatures. The "toughness" of heco-PP is due to many small crazes formed by 52 rubber particles that absorb and, therefore, interrupt the propagation of fracture in the 53 PP matrix. (Duca and Moore, 1996) 54 55 Due to the variety of PP formulations mentioned above along with their variety of 56 applications in food packaging, a wide range of the diffusion behaviour is observed in PP. 57 In this paper, the migration behaviour of polymer additives in seventeen industrial PP 58 samples is described. The seventeen PP samples cover all the major types of PP used in 59 food packaging. In order to cover a broad range of M r -values, the diffusion of small 60 The xylene-soluble component is the amount of material which remains dissolved at 20°C 94 after being refluxed in xylene. The analysis of xylene-soluble component provides 95 information about the amorphous, non-crystalline and atactic portion of a h-PP and r-PP 96 sample. For heco-PP samples, the amount of dissolved material indicates the rubber 97 content. Briefly, 500 ml of distilled xylene are introduced into a 3 -necked round-98 bottom flask, which is equipped with stirrer, reflux condenser and thermometer. After 99 heating the flask with the xylene to about 100°C for about 15 min, 5 g of PP are added to 100 the hot agitated solvent. Under vigorous agitation, the temperature is raised to the 101 boiling point of the xylene (ca. 135°C). To dissolve the PP sample completely the flask is 102 heated for an additional 60 min. Then, the heating mantle is removed and, after 2-3 min, 103 is replaced by an ice-water bath. Under vigorous agitation, the solution cools down to 5°C 104 within 20 min while the bulk of the PP sample precipitates as gel. Then, the ice-water 105 bath is replaced by a bath containing 25°C ~ 30°C water. This brings contents of the flask 106 to 20°C within 30 min. The mixture is then filtered at 20°C through a fluted paper filter 107 (ca. 10 min). 108 109The filtrate is subjected to the following analysis in duplicate: 110 Exactly 100.0 ml of the filtrate are pipetted into an aluminium dish, previously weighed 111 to the nearest 0.1 mg (after dried to a constant mass). Under a slight vacuum at 110°C, 112 most of the xylene is removed in two hours. The weight of the dish stabilizes after two 113 additional hours under a full vacuum (< 100 mbar). The aluminium dish is allowed to cool 114 at room temperature for one hour in a desiccator before its weight is measured with an 115 analytical balance. 116 117

Determination of additive migration 118

The migration of I-1010, I-168 and I-P-EPQ into 95% ethanol at 40°C has been monitored 119 after 2, 4, 10 and 20 days and also at 80°C after 3, 6, 9 and 24 hours (Brandsch et al. 120 2002). 95% ethanol makes an excellent alternative fatty food simulant for this system due 121 to its high solubility for these additives and its negligible interaction with PP. 122 123 PP sample sheets with thickness of ca. 1 mm were fully immersed into 95% ethanol. After 124 the desired contact time, an aliquot of the migration solution was injected into a high 125 From the kinetic results obtained at both 40°C and 80°C for each antioxidant, the 138 corresponding diffusion coefficient, D P, has been calculated by curve fitting with the 139 FABES software MIGRATEST © Lite [START_REF] Brandsch | Migration modelling as a 387 tool for quality assurance of food packaging[END_REF]. 140 141 Determination of limonene uptake and diffusion 142 (R)-(+)-Limonene (Aldrich, 97%) has been chosen as a migrant mainly for its molecular 143 weight (136 g/mol), which is lower than most polymer additives. Therefore, limonene 144 migration may be close to the "worst-case migration" based on the current understanding 145 of how molecular weight of a migrant influences its diffusion in polyolefins. In addition, 146 limonene can be identified easily by smell, is readily available, and is a common 147 constituent of some foods. These properties make it easier to work with limonene. 148 149

The Hiden Analytical's (Warrington, UK) Intelligent Gravimetric Analyzer (IGA) is used to 150 monitor both absorption and desorption of limonene into and out of polymer samples. 151

The IGA can monitor changes in the mass of a polymer sample to 0.1 microgram. This 152 instrument is similar to modern research grade thermogravimetric analyzers (TGA) with 153 one major difference -experiments are conducted at low pressures to minimize any 154 interference due to sorption of water and other contaminants in the atmosphere. In 155 addition, the absence of air near the polymer sample minimizes potential errors 156 associated with an air barrier [START_REF] Crank | Diffusion in Polymer[END_REF]. 157 Briefly, a polymer disk is loaded onto a sample holder and placed in the IGA system. 159 Then, the system is pumped down to ca. 10 -9 atm with a turbomolecular pump backed up 160 with a diaphragm pump. After the sample weight, system pressure and temperature 161 stabilize, limonene vapor is introduced into the system. The IGA system uses 162 automatically controlled inlet and outlet valves to maintain the limonene pressure within 163 a specified range. For this work, limonene pressure was set at 2.0 mbar. During 164 desorption runs, the system is evacuated to facilitate the removal of limonene from the 165 polymer surface. 166 167 Typical sorption runs on PP samples, owing to their thickness of more than 1 mm, took 168 more than 14 days for an absorption and desorption cycle at 40°C. Such lengthy analysis 169 runs not only reduced productivity but also increased the effect of normally negligible 170 errors such as a slow leak in the system. In order to circumvent these problems, PP 171 samples have been heat-pressed to ca. 0.2mm thickness. The heat-pressing was 172 performed at 165ºC and 5000 psi for 1 min. By keeping the temperature close to the PP 173 melting point and limiting the heat exposure, this procedure intends to minimize change 174 in film morphology such as crystallization. Densities of original PP samples and heat-175 pressed PP films showed good agreement. Sorption runs on these samples are completed 176 in about 5 days. 177 178 Limonene uptake in heat-pressed PP films are tabulated in the third column in Table 1. 179 Limonene diffusion coefficients in heat pressed PP films are tabulated in the last column 180 in Table 2. Each diffusion coefficient was determined from the slope of sorption curve as 181 described in [START_REF] Limm | Diffusion of Limonene in 413 Polyethylene[END_REF] The diffusion coefficients for I-168 and I-P-EPQ at 40°C and 80°C are listed in Table 3 197 along with the corresponding apparent activation energies, E D. The apparent activation 198 energies in Table 2 and3 An additional point should be mentioned in this connection. Whereas the 17 PP samples 296 investigated in this work are all commercial products, many of the PP samples mentioned 297 in previous publications were compounded with additives at laboratory scales which are 298 likely to differ in morphological characteristics. That means, for example, that diffusion 299 coefficients in latter samples could be higher than those in commercial samples under 300 identical experimental conditions. 301

302

The measurement of diffusion coefficients for migrants of very different relative 303 molecular masses, such as I-1010 with M r = 1178 and limonene with M r = 136, allows 304 another comparison of molecular transport behaviour between the h-PP and heco-PP. 305

Helpful tools for this comparison are theoretical models for estimation of the diffusion 306 coefficients. Generally, there have been two approaches for estimating diffusion 307 coefficients for additives in food packaging systems, one by Limm and Hollifield 1996, and 308 the other by Baner and Piringer (1996) and [START_REF] Brandsch | Migration modelling as a 387 tool for quality assurance of food packaging[END_REF] In the approach by 309 [START_REF] Limm | Modeling of additive diffusion in polyolefins[END_REF], the diffusion coefficients for the three additives I-1010, I-168, 310 I-P-EPQ and limonene can be estimated in PP at 40°C with the equation ( 1), 311 312

D P = D 0 exp (α ▪ M r 1/2 -K ▪ M r 1/3 / T) (cm²/s). 313
(1) 314 315 With K = 1335.7; α = 0.597 and lnD 0 = -2.10 for PP. 316 317 With these values an upper limit can be calculated for all types of PP-samples. The upper 318 limit can highly over estimate the experimental values. From the PP-samples investigated 319 in this study, only the estimated diffusion value for limonene is practically identical to 320 the highest experimentally measured value for test sample 17 heco-PP (Table 2), with D P 321 = 3.10 ▪ 10 -8 . These results strongly suggest that very different diffusion values can result 322 among PP-products. Additionally, high limonene diffusion coefficients in PP samples are 323 likely due to high uptake of limonene in PP samples (see Table 1). Limonene is known to 324 interact mildly with polyolefins so that its diffusion coefficient increases as limonene 325 concentration in the polymer increases [START_REF] Mohney | Permeability and solubility of d-limonene vapor in cereal package liners[END_REF]. We have also observed high 326 limonene diffusion coefficients in our polyethylene works due to high limonene uptake 327 [START_REF] Limm | Diffusion of Limonene in 413 Polyethylene[END_REF]. 328 Due to the big difference in the migration behaviour of different types of PP-matrices, a 331 subdivision of the 17 samples is necessary, where a lower, pre-exponential factor, lnD 0 = -332 5.0 is used for the h-PP in Eq. ( 1). In this way, a better estimate of the diffusion 333 coefficients for h-PP is possible. The calculated D P -values from Table 4 are also upper 334 limits for h-PP, but much closer to the experimental values. Polypropylene is a typical 335 polymer in which different phases exhibit different diffusion properties and, therefore, a 336 subdivision into corresponding groups is highly recommended. 337 338 (Table 4) 339 340

The diffusion coefficients in PP can also be predicted using the second approach by using 341 equation ( 2) [START_REF] Brandsch | Migration modelling as a 387 tool for quality assurance of food packaging[END_REF]: 342 D P = 10 4 exp ( A P -0.1351 M r 2/3 + 0.003 M r -10454/T) (cm 2 /s) (2) 344 with 345

A P = A P ' -τ /T
(3) 346 347 where A P represents a polymer specific parameter which may be a function of the 348 temperature T. 349

From the diffusion coefficients of the three antioxidants measured at 40°C in the 17 350 samples, the corresponding A P -values have been calculated with equation (2). These 351 values are tabulated in Table 5. A graphical presentation of all 20 A P -values for the h-PP 352 and r-PP samples ( As mentioned above, one can correlate the diffusion behaviour and the XL amounts for 362 the homophasic h-PP and r-PP, but not for the heterophasic heco-PP. 363 364 A major objective of modelling diffusion in plastics is to develop an additional tool for the 365 quality assurance of food contact materials. Modelling of potential migration is already 366 used by the US Food and Drug Administration (FDA) and in the European Union (EU). To 367 ensure that a proposed migration model offers the safety margin required by the 368 consumer protection laws, upper limits of diffusion in each plastics food contact material 369 must be calculated. Different approaches can be used to achieve this objective. One such 370 procedure has been recently described [START_REF] Begley | Evaluation of migration models that 383 might be used in support of regulations for food-contact plastics[END_REF]. As shown in equation ( 2

  , M r = 136) as well as the migration of large additives, up to M r = 1178 61 (IRGANOX 1010), have been measured at different temperatures. 6263The aim of this paper is to correlate migration properties with the structure of PP. 64In addition, the diffusion data and the percentages of xylene-soluble fractions have been 65 correlated in order to find a short cut which enables one to predict the migration 66 behaviour of a PP-sample by testing its "isotactic index" with xylene. 67 68 Materials and Methods 69 Seventeen PP samples were obtained from a leading European PP producer, which could 70 be classified into 3 categories: 71 72 samples 1 through 8 are monophasic homopolymers (h-PP); 73 samples 9, 10 and 11 are monophasic random copolymers (r-PP); and 74 samples 12 through 17 are heterophasic copolymers (heco-PP). 75 76 These samples are a representative selection of products actually used as food contact 77 materials. The following polymer additives were present in the PP samples: 78 79 IRGANOX 1010 (I-1010) (CAS 6683-19-8) at initial concentration of ca. 500 mg/kg, 80 IRGAFOS 168 (I-168) (CAS No. 31570-04-4) and/or IRGAFOS P-EPQ (I-P-EPQ) (CAS No. 81 36613-773) at initial concentrations of 300~400 mg/kg. 82 83 In addition, slip-agents and different nucleating agents were included in some of the 84 samples. None of the 17 samples was synthesized via metallocene catalysis. The 85 percentages of the xylene-soluble portions in all samples have been measured. These 86 percentages represent the sums of extractable additives and the xylene-soluble oligomers 87 in the PP. The percentages of xylene-soluble compounds (XL %) and the total 88 percentages of additives are listed in

  initial concentration, C P , O , of additives, a PP sample was dissolved in 129 toluene under reflux and then precipitated with ethanol. After filtering out the polymer, 130 the solution was analysed with an HPLC. 131 132 For a given antioxidant (I-168 and I-P-EPQ) the sum of the active additive and the 133 oxidised substance was used for the evaluation of the results. Quantification was 134 performed by external calibration. The calibration curve was based on linear regression 135 analysis of standards. 136 137

  are significantly higher than the activation energy of 100 kJ/mol 199 used in the diffusion model for PP. , one can make the following observations. First, the D P -values for the 204 h-PP samples and those for r-PP samples are significantly different. The D P -values for r-PP 205 are in all cases at least one order of magnitude higher than those for h-PP. These results 206 are in agreement with O'Brien and Cooper's finding (2001) that migration from r-PP is 207 much higher than that from h-PP. The D P -values for heco-PP are generally somewhat 208 lower but more comparable to the values for r-PP. The degree of crystallinity and the 209 structure of the crystallites, as evidenced by the xylene-extractable fractions, seem to be 210 responsible for this large difference in the D P -values. As will be shown below, a 211 correlation equation can be established between the diffusivity and the XL-values in the 212 series of h-PP and r-PP. 213 214 Second, we narrow our focus to the I-1010 and limonene results in eight h-PP samples in 215 Table 2. The samples 6 and 7, with the lowest XL-values, the highest densities and 216 melting temperatures, have the smallest D P -values at 40°C for the antioxidants and for 217 limonene. Sample 6 is the most nucleated among the 7 h-PP samples. 218 219 From these results, an increase in the diffusion coefficient values with the increase in XL 220 amounts is indicated. The concentration of nucleating agents in PP samples seems to have 221 an effect on the diffusion coefficient values, which is relatively small for I-1010 in h-PP 222 but larger for limonene. 223 224 Third, the influence of the crystalline structure on the diffusion in h-PP is most 225 pronounced for limonene. Its diffusion coefficient in the nucleated sample 8 remains 226 corresponding values in the non-nucleated samples 3 to 5, despite 227 the comparable XL-values. To account for this data, we refer to an additional result 228 obtained in our laboratory with an h-PP produced from the same company but with a 229 metallocene catalyst. Despite a very low XL amount in this sample, relatively high 230 diffusion coefficients, comparable with the non-nucleated h-PP samples, have been 231 measured with the antioxidants. An imperfect isotactic structure resulting from the 232 metallocene catalysis seems to be responsible for this behaviour. 233 234 Fourth, the melting temperatures are significantly lower in the 3 r-PP samples compared 235 with the h-PP`s. The XL-amounts in the r-PP's are highest in the series of the 11 236 homophasic samples. This behaviour correlates well with the diffusivity of all additives 237 and limonene. 238 239 Fifth, no correlation has been observed between the XL-amounts in the 6 heco-PP 240 samples and the corresponding diffusion coefficients of the antioxidants. But an unusually 241 high relative diffusivity has been found for limonene. The small non-polar limonene 242 molecules are mainly diffusing through the rubbery phase in the heterophasic samples 243 and in the atactic phase of the r-PP due to their good solubility in these phases. With the 244 more polar antioxidants no such extreme preference for the rubbery and atactic phase is 245 observed. The limonene absorption values were also higher for heco-PP samples than for 246 the h-PP samples. Still, limonene diffusion is slowest in the strongly nucleated sample 15 247 among all heco-PP samples. 248 249 Sixth, the 15 apparent activation energies of the three additives I-1010, I-168 and I-P-EPQ 250 are in the range of 139 < E D < 216 kJ/mol for h-PP with a mean value at E D = 190 kJ/mol. 251 The 5 values for the random-PP are between 151 < E D < 188 with a mean value at E D = 176 252 kJ mol, and for the 6 heterophasic heco-PP samples the range is 144 < E D < 188 with a 253 mean value E D = 164 kJ/mol. Despite the scatter in these results, the mean value of E D = 254 164 kJ/mol for heco-PP is significantly lower than the mean E D = 190 kJ/mol for h-(1995) determined the diffusion coefficients of I-1010 based on its 258 migration from h-PP into corn oil and water between 50 °C and 135 °C, taking into 259 account the role of mixing in the liquid phase. From their data for h-PP/corn oil system 260

  ), the 371 A P -values are polymer specific parameters along with the lnD 0 -values in equation (1). For 372 example, upper limit values of A P ' = 13.1 and τ = 1577 for equation (3) have been 373 proposed for polypropylene. With these values, upper limits for the diffusion coefficients 374 of additives in PP can be calculated with equation (2), where a mean activation energy of 375 diffusion, E D = 100 kJ/mol is assumed.The results obtained in this work clearly indicate 376 that polypropylene can be further subdivided into h-PP, r-PP and heco-PP and that there 377 is a correlation between XL-values and diffusion and migration values. The XL-values 378 appears to be a precise indicator in the variation of diffusion within the groups of PP. 379

  Fig.1

Table 1

 1

	.

  Table 2 lists the diffusion coefficients D P of I-1010 at 40°C and 80°C and the apparent 193 activation energies obtained from these results. Also included are diffusion coefficients of 194 limonene at 40°C for eleven PP samples. 195 196

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	183 184 185 186	(Table 2) Results	O n l y
	187	Table 1 lists the xylene-soluble amounts, the total amount of additives in the 17
	188	investigated samples as % (w/w) and the melting temperatures. Also tabulated are the
	189	densities of eleven PP samples and the equilibrium limonene concentrations in them at
	190	the limonene pressure of 2 mbar at 40°C. The values in the last column represent the
	191	concentrations of the nucleating agents in % (w/w).
		7

Table 5 )

 5 as a function of the xylene-extractable amounts, XL from 353

Table 1 ,

 1 is shown in Figure1. A linear regression of the A p values results in equation (4) 354

	355	with a correlation coefficient of 0.93:
	356
	357	(Table 5)
	358	(Figure 1)
	359
		A P = -0.5 + 1.2x XL

Table 1 .

 1 Xylene-soluble (XL) and total additive amounts in the 17 PP-samples 433 investigated; densities and limonene equilibrium absorption at 2.0 mbar for 11 samples. 434 [X = Melting points of isotactic-PP phases in all heco-PP's are 165°C. Melting points of 435

	436	rubbery phases are much lower.]
	437
	438	Table 2.	Diffusion coefficients and activation energies measured for I-1010 at 40°C
	439	and 80°C and diffusion coefficients measured for limonene at 40 °C.
	440
	441	Table 3.	Diffusion coefficients and activation energies measured for I-168 and I-P-
	442	EPQ at 40°C and 80°C.
	443
	444	Table 4.	Diffusion coefficients calculated with Eq. (1) with an adjusted pre-
	445	exponential factor lnD 0 = -5.0 for monophasic homopolymers, h-PP
	446
	447	Table 5.	Polymer specific A P -Values for the 17 investigated PP-samples obtained with
	448	I-1010, I-168 and IP-EPQ at 40 °C.

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Table 1 .

 1

	Sample	XL	Additives	ρ P	Limonene	Tm	Comments
		%	%	g/cm³	absorption	°C
					%
	1 h-PP	3.0 1.00			163
	2 h-PP	3.0 1.00			163 Nucleated (0.35) 1
	3 h-PP	3.0 0.20	0.896 3.02	163
	4 h-PP	3.0 0.19	0.892 3.08	163
	5 h-PP	3.0 0.19	0.899 2.94	163
	6 h-PP	1.0 0.90	0.906 2.55	165 strong nucleated (0.7)
	7 h-PP	1.5 0.67	0.904 2.73	165 Nucleated (0.4)
	8 h-PP	3.0 0.87	0.903 2.86	163 Nucleated (0.22)
	9 r-PP	3.5 0.67			150 Nucleated (0.2)
	10 r-PP	6.0 0.20	0.890 4.58	139
	11 r-PP	4.5 0.66	0.897 4.09	150 Nucleated (0.2)
	12 heco-PP 4.7 2.10			X	strong nucleated (1.7)
	13 heco-PP 11.0 1.09			X	Nucleated (0.35)
	14 heco-PP 17.0 0.55			X	Nucleated (0.35)
	15 heco-PP 12.0 2.10	0.906 3.70	X	strong nucleated (1.7)
	16 heco-PP 22.5 0.55	0.894 5.79	X	Nucleated (0.35)
	17 heco-PP 30.0 0.70	0.883 6.45	X	Nucleated (0.5)
	1. Values represent the concentrations of the nucleating agents in % (w/w).

Table 2 .

 2

	Sample	D P -10 14	D P -10 11	E D	D P -10 10
		cm 2 /s	cm 2 /s	KJ/mol	cm 2 /s
		40 °C	80 °C		40 °C
		IRGANOX 1010	IRGANOX 1010		Limonene
	1 h-PP	0.27	1.1	191
	2 h-PP	0.70	0.30	139
	3 h-PP	0.32	1.4	193	12.1
	4 h-PP	0.39	2.9	205	7.24
	5 h-PP	0.42	4.7	216	11.7
	6 h-PP	0.13	0.45	187	0.95
	7 h-PP	0.10	0.39	190	1.95
	8 h-PP	1.5	2.3	168	2.92
	9 r-PP	7.2	22	184
	10 r-PP	51	98	174	44.9
	11 r-PP	13	46	188	39
	12 heco-PP 0.91	3.3	188
	13 heco-PP 1.1	1.8	170
	14 heco-PP 2.0	1.5	152
	15 heco-PP 3.8	6.5	171	35.0
	16 heco-PP 38	28	152	183
	17 heco-PP 4.0	6.8	171	310

Table 3 .

 3

	Sample	D P -10 13	D P -10 10	E D
		cm 2 /s	cm 2 /s	KJ/mol
		40 °C	80 °C
		IRGAFOS 168	IRGAFOS 168
	1 h-PP	1.9	2.6	166
	3 h-PP	0.43	1.7	190
	6 h-PP	0.11	0.94	208
	7 h-PP	0.16	0.85	197
	10 r-PP	34	24	151
	12 heco-PP 1.7	2.1	164
	13 heco-PP 2.2	2.9	165
	14 heco-PP 1.4	1.8	164
	15 heco-PP 3.9	3.2	154
	16 heco-PP 13	6.9	144
	17 heco-PP 2.0	3.4	171
	Sample	D P -10 15	D P -10 11	E D
		cm 2 /s	cm 2 /s	KJ/mol
		40 °C	80 °C
		IRGAFOS P-EPQ	IRGAFOS P-EPQ
	4 h-PP	2.1	1.6	205
	5 h-PP	3.0	2.5	207
	8 h-PP	5.0	1.3	181

Table 4 .

 4

			Food Additives and Contaminants	Page 20 of 22
	Migrant	D P (cm²/s) T( °C)
	IRGANOX 1010 2.4 -10 -11 80
	IRGAFOS 168	1.6 -10 -10 80
	IRGAFOS P-EPQ 3.5 -10 -11 80
	Limonene	2.1-10 -9	40

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Table 5 .

 5

	Sample	IRGANOX 1010 IRGAFOS 168 IRGAFOS P-EPQ
	1 h-PP	2.2	3.1
	2 h-PP	3.1
	3 h-PP	2.3	1.6
	4 h-PP	2.5	1.1
	5 h-PP	2.6	1.5
	6 h-PP	1.4	0.2
	7 h-PP	1.2	0.6

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Zweifel H., 1998, Stabilization of Polymeric Materials, Springer


Celeste De Matos

Isabel M Costa

Angelina Lopes

Simões Pena

Rui Ferreira

Salvador M Cardoso

Celeste M Lino
email: cmlino@ci.uc.pt

A Pena

Estimated intake of sweeteners acesulfame-K and aspartame from soft drinks, soft drinks based on mineral waters and nectars in a scholar group of Portuguese teenagers

Keywords: Estimated intake, sweeteners, acesulfame-K, aspartame, soft drinks, soft drinks based on mineral waters, nectars, Portuguese teenagers

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

Food additives play a vital role in the modern food industry, and are generally used for maintaining food quality characteristics as well as ensuring food safety. Among them, artificial sweeteners are widely used throughout the world. For many year artificial sweeteners have been commonly used in the food, beverage, confectionary and pharmaceutical industries because of their sweet taste with fewer calories [START_REF] Zhu | Separation and simultaneous determination of four artificial sweeteners in food and beverages by ion chromatography[END_REF]. The use of sweeteners is ofsignificance due to increase in obesity problems in developed countries, the general growing awareness of health implications connected to a high calorie diet, to the worldwide increase in the number of people suffering from diabetes, and also to reduce tooth decay [START_REF] Leth | Estimated intake of intense sweeteners from nonalcoholic beverages in Denmark[END_REF].

Acesulfame-K and aspartame are used in about 100 countries [START_REF] Zhu | Separation and simultaneous determination of four artificial sweeteners in food and beverages by ion chromatography[END_REF]. In Portugal, the most common artificial sweeteners used by the food industry are aspartame, acesulfame-K, and sodium cyclamate. Nowadays, in this country, sucralose begins to be introduced in some food items, and the use of saccharin is decreasing.

Scrutiny of the labels from soft drinks, soft drinks based on mineral water, and nectars shows that aspartame and acesulfame-K are those preferentially incorporated.

The acceptable daily intake (ADI) values established are 9 and 40 mg/kg b.w./day for acesulfame-K and aspartame, respectively (SCF, 1985;SCF, 2002). Recently, in an update review of the intake estimations, [START_REF] Renwick | The intake of intense sweeteners -an update review[END_REF] showed that few data are available for the population in general, and for teenagers, only two studies from Italy are reported. As far as we know, this is the first study of sweeteners intake for Portuguese populations, namely adolescents, which is a group with high consumption. The main objective of this study was to check for the presence and concentration of acesulfame-k and aspartame, and verify if below maximum permitted levels in light soft drinks, soft drinks based on mineral waters, and nectars. The data were subsequently used to estimate intake of the sweeteners for a group of Portuguese teenage students aged between 13 and 15.

Materials and methods

Studied population

One public high school was selected in the city of Coimbra (central zone of Portugal). In the school year 2006-2007, students were randomly selected (''Random sample'') (n=65). All students, aged between 13 and 15 years old, 50.8% males and 49.2% females, were asked to fill in a questionnaire related to the duration and frequency of intake of sugar-free soft drinks and nectars. Students were also asked to record their own personal data, such as age, sex, rural/urban residence area and education level of parents. Permission was obtained from those responsible for their education.

The mean body weight was 56.5 kg ranging from 36.5 to 106.1 kg. The Body Mass Index was calculated, and it was found that 62% of individuals had normal weight, 23% had low weight, and 15% had excess weight or obesity.

Chemical analysis

Soft drinks were filtered through 0.45 µm membrane filter and ultrasonicated, before HPLC analysis. Nectars were submitted to centrifugation at 3400g for 15 min, filtration through membrane filter and ultrasonication. The two sweeteners were separated using reverse phase liquid chromatography (Pump model 305 from Gilson, France, an injection valve with one 20 µL loop model 7125 Rheodyne, Cotati, Califórnia, USA) with an Hichrom C18 column (5 µm, 250 x 4.6 mm) and a buffered mobile phase (KH 2 PO 4 0.02M / ACN (90:10) / phosphoric acid), at 1 mL/min, which pH was rigorously controlled at 4.2-4.3 for an adequate resolution between aspartame and benzoic acid, since this preservative is present in almost analysed samples. Detection was performed with an UV detector, model 116 from Gilson, France, at 220 nm.

External standard method was used for quantification. Integration was performed with an integrator; model SP4290 from Spectra-Physics.

Recovery studies

Recoveries were determined by spiking one soft drink free for the two sweeteners, in triplicate, with known amounts of acesulfame-K and aspartame at final concentrations between 35 and 350 mg/L for the first, and 35 and 600 mg/L for the second sweetener.

Results

The methodology was shown to have adequate linearity, sensitivity, accuracy and precision. Correlation coefficients (r 2 ) were 0.9906 for acesulfame-K and 0.9973 for aspartame, both between 0.025 µg and 0.4 µg injected. Limits of quantification of acesulfame-k and aspartame were 1.7 mg/L and 3.5 mg/L, respectively. Recovery values were between 91% for acesulfame-K at fortification levels of 350 mg/L and 102% for aspartame at spiked levels of 35 mg/L. Intra-day repeatabilities (% RSD) ranged between 1.0% for aspartame at 35 mg/L and 7.9% for acesulfame-K at 35 mg/L. The inter-day repeatabilities ranged between 7.3 and 11.5% for aspartame at 600 mg/L and acesulfame-K at 100 mg/L, respectively (Table I). Table II shows the number of the samples, the range, and the mean of the sweeteners in the different types of analysed beverages. Acesulfame-K was detected in 18 (72%) of the 25 soft drinks analysed, with levels ranging between 7.8 mg/L e 166 mg/L, and a mean concentration of 72 mg/L. Twenty three samples (92%) contained aspartame at II).

When the data on sweeteners soft drinks were analyzed according the flavour, the cola drinks (n=5) had the highest mean levels for both sweeteners, 98 and 103 mg/L for acesulfame-K and aspartame, respectively (Table III). In respect of soft drinks based on mineral waters, aspartame was found in 8 samples (62%), between 19 and 154 mg/L. The mean concentration value was 82 mg/L. Acesulfame-k was detected in ten samples (77%), at levels ranging between 3.7 and 182 mg/L, with a mean level of 48 mg/L (Table II). In five of these ten samples, faulty declarations were found as this sweetener was not declared on the ingredient labels. Those samples represent four Portuguese brands. One of them did not specify the presence of any sweetener, but indicated "without addition of sugar", and four samples only referred to the presence of aspartame.

All samples of nectars showed the presence of acesulfame-K, with levels between 35 and 356 mg/L, being the mean concentration 128 mg/L. Aspartame was detected in 8 (80%) samples, at levels between 39 mg/L and 129 mg/L. The mean concentration was 73 mg/L.

According Portuguese legislation (Decreto-Lei nº 394/98 de 10 de Dezembro de 1998), the maximum permitted level for acesulfame-K in soft drinks and in nectars is 350 mg/L. This level was exceeded in one sample of nectar, with a concentration of 356 mg/L. For aspartame, the maximum permitted level for the same kind of products, 600 mg/L, was not exceeded. The estimated daily intake (EDI) for each sweetener by the teenager population was calculated by the following equation EDI = (∑c) (CN -1 D -1 K -1 ) (Table IV), where ∑c is the sum of sweetener concentration in the analyzed samples related to the questionnaire (mg/L), C is the mean annual intake estimated per teenager, N is the total number of analysed samples, D is the number of days in a year, and K is the mean body weight. The estimated intakes of acesulfame-K and aspartame for the average intake, by the consumers were below the acceptable daily intakes (ADIs). For acesulfame-K the EDI was 0.7 mg/kg b.w./day for soft drinks, 0.2 mg/kg b.w./day for soft drinks based on mineral waters, and 0.5 mg/kg b.w./day for nectars, representing 8.0%, 2.2%, and 5.8% of the ADI, respectively (Table IV). Similar situation was observed for aspartame. The EDI for soft drinks was 1.1 mg/kg b.w./day, representing only 2.9% of the ADI. For soft drinks based on mineral waters 0.3mg/kg b.w./day representing 0.7% of the ADI. In respect to nectars, the EDI was 0.2 mg/kg b.w./day, what represents 0.5 % of the ADI (Table IV).

Three of the well-known international trade mark soft drinks, being two cola type drinks, were responsible for the maximum daily intake of the sweeteners by the studied population, assuming the maximum consumption of the soft drinks. One of them was responsible for the maximum intake of aspartame, with 4.5 mg/kg body weight, and occupying the second place for the acesulfame intake of 1.3 mg/kg body weight. The international trade mark beverage contributed the maximum intake for acesulfame of 1.6 mg/kg body weight. Another soft drink gave an aspartame intake of 2.8 mg/kg body weight. The maximum daily intake for both sweeteners given by nectars was lower than that for soft drinks. The EDI of the teenager population was higher than the Portuguese population in general. For the latter, the EDIs of acesulfame-K were 0.18 mg/kg b.w./day and 0.057 mg/kg b.w./day when soft drinks and nectars were consumed, respectively, representing 2.0 and 0.6% of the ADI. For aspartame, the EDI values are 0.3 mg/kg b.w./day and 0.026 mg/kg b.w./day for soft drinks and nectars respectively, which means 0.7% and 0.07 % from the ADIs (Table V).

Discussion

The comparison of data on the levels in this study with other countries is difficult, as result of scarcity of data. In what concerns the presence of aspartame in soft drinks, [START_REF] Prodolliet | Determination of aspartame and its major decomposition products in food[END_REF] found levels of this sweetener of 153 mg/L and 286 mg/L in 2 diet cola drinks in Switzerland. In the same kind of drinks, in Barcelona, the levels of aspartame were 130 mg/L and 676 mg/L [START_REF] Suárez | Analisis de aditivos en bebidas refrescantes[END_REF]. A Danish study reported 159 mg/L and 56 mg/L for soft drinks with (n=20) and without (n=43) carbon dioxide, respectively [START_REF] Leth | Estimated intake of intense sweeteners from nonalcoholic beverages in Denmark[END_REF]). The same sweetener was found by [START_REF] Tyler | Liquid chromatographic determination of sodium saccharin, caffeine, aspartame, and sodium benzoate in cola beverages[END_REF], in USA, in two diet cola drinks with 97 mg/L and 92 mg/mL. In USA, [START_REF] Walker | Analysis of beverages by capillary electrophoresis[END_REF] detected aspartame in two soft drinks, one of diet coca with 206 mg, and another of diet lemon carbonated soft drink with 185 mg, by scan of 355 mL, which corresponds to 580 mg/L and 521 mg/L, respectively. In China, [START_REF] Zhu | Separation and simultaneous determination of four artificial sweeteners in food and beverages by ion chromatography[END_REF] found levels of 7234.6 and 2826.3 mg/L in 2 samples of cola soft drinks. The values in different countries are higher than those found in the samples analysed in the present study, except for soft drinks without carbon dioxide from Denmark.

As far as we know, only one study of nectar samples has been previously reported. In this study, aspartame was found in one exotic fruit nectar at concentrations of 2416 [START_REF] Prodolliet | Determination of aspartame and its major decomposition products in food[END_REF]. One drink of fruit juice, in China, had levels of aspartame and acessulfame-K of 317.2 and 4.5 mg/L, respectively [START_REF] Zhu | Separation and simultaneous determination of four artificial sweeteners in food and beverages by ion chromatography[END_REF]. The aspartame level is excessively high when compared with the concentrations found in identical samples involved in the present study, ranging in this case between 39 mg/L and 129 mg/L.

Concerning acesulfame-K levels, [START_REF] Suárez | Analisis de aditivos en bebidas refrescantes[END_REF] reported its presence in two cola drinks at 59 mg/L and 146 mg/L. Recently, in Denmark, a study of light soft drinks with (n=21) and without (n=60) carbon dioxide showed mean levels of acesulfame-K of 129 mg/L and 62 mg/L, respectively [START_REF] Leth | Estimated intake of intense sweeteners from nonalcoholic beverages in Denmark[END_REF]. Also in China, [START_REF] Zhu | Separation and simultaneous determination of four artificial sweeteners in food and beverages by ion chromatography[END_REF] found levels of acesulfame-K of 1105.4 and 1475.2 mg/L, in 2 cola drinks. The levels found in Portuguese study, 56 mg/L e 137 mg/L, are between those reported by [START_REF] Suárez | Analisis de aditivos en bebidas refrescantes[END_REF], are higher than those found by [START_REF] Leth | Estimated intake of intense sweeteners from nonalcoholic beverages in Denmark[END_REF], and lower than those referred by [START_REF] Zhu | Separation and simultaneous determination of four artificial sweeteners in food and beverages by ion chromatography[END_REF].

The results of the present study are consistent with the percentage of ADI for acesulfame and aspartame reported for other countries. The lowest percentage of ADI for both sweeteners of 0.1%, was found in Italy for a population aged between 13 and 19 years old [START_REF] Leclercq | Intake of saccharin, aspartame, acesulfame-k and cyclamate in Italian teenagers. Present levels and projections[END_REF]. Studies carried out in another teenager population, from Italy, had ADI percentages of 0.2 and 0.3% for aspartame and acesulfame-k, respectively [START_REF] Arcella | Dietary estimated intake of intense sweeteners by Italian teenagers. Present levels and projections derived from the INRAN-RM-2001food survey[END_REF]). The highest values for acesulfame-K, 9%, were found in individuals aged between 3 and 74, in United Kingdom [START_REF] Wilson | Urinary monitoring of saccharin, and acesulfame-k as biomarkers of exposure to these additives[END_REF]. For the aspartame, the highest values were found in one Canadian population, aged between 2 and 6 years, with 10% of the ADI [START_REF] Devitt | Assessment of intakes of artificial sweeteners with type I diabetes mellitus[END_REF]. It should be stressed that youngest population group aged between 1.5 -4.5 years across different areas of the United Kingdom (Food Standards Agency UK. 2003), and 2-6 years in Toronto, Canada [START_REF] Devitt | Assessment of intakes of artificial sweeteners with type I diabetes mellitus[END_REF], had some of the highest percentages of the ADI for aspartame and acesulfame-K. In the UK those values are 6% and 8% for acesulfame and aspartame, respectively, and in Canada represented 4% and 10% for the same sweeteners. The first European conference to review the safety of aspartame, held in Paris in 2006, showed that the intakes are about 10% of the acceptable daily intake, even by high consumers of aspartame [START_REF] Renwick | First European Conference on aspartame: Food and Chemical Toxicology 42: Putting safety and benefits into perspective. Synopsis of presentations and conclusions[END_REF].

Conclusions

The analytical methodology is well-suited for quality control, providing adequate linearity, sensitivity, accuracy and precision for the determination of both sweeteners in soft drinks and nectars. Moreover, the speed of analysis and simple sample preparation allows for its use in a quality control laboratory. In this study it was demonstrated that nectars showed 100% frequency of detection and the highest mean concentration of 128 mg/L for acesulfame-K, and soft drinks contained aspartame in 92% of the samples with mean levels of 89 mg/L. The highest estimated daily intakes of both sweeteners for the teenage population was for soft drinks, giving 0.7 and 1.1 mg/kg b.w./day, being far below their respective ADI-values. It was also shown that EDIs of both sweeteners by the general Portuguese population is lower than the teenager population. It can be concluded that, with the current observed consumption patterns and occurrence levels, the risk of an excessive intake of intense sweeteners by Portuguese teenagers is low. a EDI was calculated using the equation EDI = (∑c) (CN -1 D -1 K -1 ), where ∑c is the sum of sweetener concentration in the analyzed samples related to the questionnaire (mg/L), C is the mean annual intake estimated per teenager (288 L for soft drinks, 111.3 L for soft drinks based on mineral water, and 84 L for nectars), N is the total number of analysed samples, D is the number of days in a year, and K is the mean body weight, which was considered 56.5 Kg (mean of body weight of the studied population).

b ADIs proposed by SCF (1985).

  The consumption data for the Portuguese population in general were obtained through the annual intake estimated per Portuguese inhabitant in 2006, according the ANIRSF, of Soft Drink and Fruit Juice Industry. The estimated mean body weight was 60 kg.The drink surveyA total of 48 samples were chosen in accordance with products available for sale at the canteen of the school and the consumption of the teenagers. Of these samples, 38 were soft drinks comprising 25 light soft drinks and 13 soft drinks based on mineral waters (a new group of cooled drinks recently introduced in the Portuguese market) and 10 were light nectars. Samples were collected in the school canteen, and in supermarkets between December 2006 and January 2007.

Figure

  Figure I shows the chromatograms of a standard of acesulfame-K and aspartame

  /L and 339 mg/L. The mean concentration was 89 mg/L (Table

  Figure I. Chromatograms of: a) standard of acesulfame-K (rt= 4.58) and aspartame

  Estimated daily intake (EDI) a of acesulfame-K and aspartame by Portuguese population in general through soft drinks, , and nectars and respective comparison with acceptable daily intake (ADI) a EDI was calculated using the equation EDI = (∑c) (CN -1 D -1 K -1 ), where ∑c is the sum of sweetener concentration in the analyzed samples related to the questionnaire (mg/L), C is the annual intake estimated per Portuguese inhabitant in 2006 (77.6 L for soft drinks, and 9.8 L for nectars according the ANIRSF, 2007), N is the total number of analyzed samples, D is the number of days in a year, and K is the mean body weight, which was considered 60 Kg (mean of body weight of the Portuguese population).b ADIs proposed bySCF (1985

Table I .

 I Recovery, intra and inter-day repeatability (% RSD) for soft drinks spiked at

	different levels of acesulfame-K and aspartame
		Fortification			Intra-day	Inter-day
	Sweeteners	level (mg/L) Exactitude*	repeatability	repeatability
	Acesulfame-K F o r	35 100		92 94	7.9 1.1	8.2 11.5
		350 P		91	5.3	9.7
	Aspartame	35 e 100 e r	102 85	1.0 1.7	8.5 9.9
	*n=3		600	89 R	2.8	7.3
				e
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						y

Table II .

 II Frequency, range, and mean concentration 1 of the sweeteners in the different types of analysed beverages.

			Food Additives and Contaminants			Page 18 of 20
	Type of sample	Analysed		Acesulfame-K			Aspartame
		samples
				(mg/L)			(mg/L)
	Soft drinks Soft drinks based F o 25 13 r	Positive samples 18 10	Range 7.8 -166 3.7 -182	Mean 72 1 48 1	Positive samples 23 8	Range 8.8 -339 19 -154	Mean 89 1 82 1
	on mineral waters Nectars 1 Mean concentration for samples with the sweeteneer 10 10 35 -356 P e e r	128	8	39 -129	73 1
			R
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						y

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Table III .

 III Mean levels (mg/L) of acesulfame-K and aspartame in light soft drinks according to the flavor.

	Samples	Acesulfame-K	Aspartame
	Orange (n=3)	-	70
	Cola (n=5)	98	103
	Lemon (n=2) Pineapple (n=3) Soy (n=2) F o r	70 51 57	87 52 49
	P
	e
	e r
	R
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			y

Table IV .

 IV Estimated daily intake (EDI) a of acesulfame-K and aspartame by scholar teenagers through soft drinks, soft drinks based on mineral water, and nectars and respective comparison with acceptable daily intake (ADI)

			Soft drinks		Soft drinks based on	Nectars
					mineral water
	Sweeteners	ADI b	EDI	ADI	EDI	ADI (%)	EDI	ADI
		(mg/kg	(mg/kg	(%)	(mg/kg		(mg/kg	(%)
		b.w./day)	b.w./day)		b.w./day)		b.w./day)
	Acesulfame-K	9	0.7	8.0	0.2	2.2	0.5	5.8
	Aspartame	40	1.1	2.9	0.3	0.7	0.2	0.5

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