Since the different dioxin crises, the EC had and will established regulations concerning the presence of dioxin and dioxin-like compounds in food and feedingstuffs. The ground of the present work lies in the difficulty of the fish industry to analyse more and more samples, what is both time and money consuming.
In that way, GfA is looking for a fast and cheap simplified pre-test, cautioned by the EC, to indicate the dioxin level of a batch of samples, results which must be further confirmed by the classical HRGC/HRMS method, if they were potentially exceeding the EC limit values.
All samples related to seafish and seafish products were thus collected from the database of GfA, in order to estimate the WHO-TEQ values (the PCDD/F-, the PCB- and the total TEQs) by using the equations delivered by the linear adjustments of the correlations of some congeners with the TEQ values. The contributions of all congeners to the 3 TEQ values were then evaluated and the correlation factors of all congeners with the 3 TEQ values calculated, what reveals us the most interesting congeners. The combination of these findings leads to the designation of the congeners 2,3,7,8- TCDF, 2,3,7,8-TCDD, 2,3,4,7,8-PeCDF, 1,2,3,7,8-PeCDD and PCB 126 as the most correlated and greater contributors to the TEQ values, these one being consequently selected for the next step of the work.
After this phase, the use of an exploratory data analysis system, like the software Ein*Sight, permits to confirm and to improve the equations previously established by displaying groups of samples that are not apparent from a visual examination and by calculating again more precise equations inside these new groups. Unfortunately, no grouping of the samples was feasible with the parameters we possess and the potential outliers pointed out by the software differ from those provided by the Grubbs' test. That's why these outliers were not removed and the equations of the regression lines conserved, allowing to broad the scale of the method's validity.
The bring into play of the software having not modified the correlation factors, another way to increase them was to correlate the sum of the chosen congeners, what will surely enhance the results and therefore the precision of the method. So different sums of congeners were evaluated, the choice of these sums being based on the former conclusions and on the analytical opportunities offered by a GC-MS system. As waiting, the sums improve the correlation factors previously estimated and we finally opt for:
• the PCB 126 for the prediction of the WHO-PCB-TEQ;
• the sum of 4 congeners (2,3,7,8- TCDF, 2,3,7,8-TCDD, 2,3,4,7,8-PeCDF and 1,2,3,7,8-PeCDD) for the prediction of the WHO-PCDD/F-TEQ;
• the total WHO-TEQ predicted (sum of the WHO-PCDD/F-TEQ predicted with the sum 4 and of the WHO-PCB-TEQ predicted with the PCB 126) for the prediction of the total WHO-TEQ.
The penultimate stage was after all the grouping of the different matrices in less specific data sets so as to spread out the scale of application of the method. The purpose was to group all the samples in only 3, then one data set and to see if the correlation factors were still able to predict correctly the TEQ values. Finally, the gathering in only one data set seems to be possible, the coefficient factors remaining high enough to predict the TEQ values with a correct precision.
Last but not least, so as to complete the work, the uncertainties on the calculated values were evaluated thanks to the interval confidence estimation of the estimated value issued from the linear adjustment with the least squares method. The intervals of the method's validity were then deduced from these results and from the values of the data set.
[...] This entails among other continuous performance checks by an internal quality management and a regular participation in external interlaboratory comparisons (see Appendix 3). This certification is valid until July 2008. Its quality assurance also meets the requirements of the DIN EN ISO 9002 norm. For instance, GfA is recognized for emission/immission measurements and for the trace analysis of highly toxic compounds in all the Länder of the Federal German Republic, according to the law of air protection, and like independent institute for the measurement of sampling of dangerous substances in air and on working places by the German institute AKMP Dioxins and dioxin-like compounds General introduction Dioxin terms may be confusing and some aspects may cause difficulties. [...]
[...] The 2 major useful kinds of graph types are: the 3-dimensional scatter plot, which shows the repartition of the points and indicates the existence of groups within the matrix (see Appendix the dendrogram, which displays the results generated by the HCA method. Dendrograms provide useful information by illustrating how related samples can be grouped as a cluster, as well as help you to spot anomalous samples, which may be outliers (see Appendix 25) Exploratory data analysis Exploratory data analysis is the computation and the graphical display of patterns of association in multivariate data sets. [...]
[...] Thus both absolute weights and TEQs must be given per body weight or per unit of fat, preferably per kilogram, but often per gram. Note also that non-standard units, like ppm, ppb and ppt, are more and more employed Properties of dioxins Chemical properties Dioxins are compounds of planar structure of which positions 1 to 9 are occupied either by a hydrogen atom or by a chlorine atom: they differ from one another primarily by the location and number of chlorine atoms on the molecule. [...]
[...] The toxic potentiality of a congener can be expressed in reference to the most toxic compound, thanks to the concept of toxic equivalent TEF = Toxic potentiality of the individual compound/Toxic potentiality of 2,3,7,8-TCDD It was developed since 1977 to give a toxicological value to a mixture of compounds chemically close and having the same action mechanism, i.e. active on the same receptor. The TEF is frequently re-valued according to the evolution of knowledge (see Appendix 12). The concentration of a congener can be converted into a value of TEQ, equal to the measured concentration multiplied by the TEF of the compound. [...]
[...] These 5 congeners were thus pre-selected for the next steps of the work as the most potential interesting congeners for the prediction of the TEQ values. But the amount of data collected was too important to be easily treated and used to conclude in. That's why the use of a statistical software was a necessity to extract meaningful informations from such large multivariate data sets and to assess the application of such congeners for the prediction of the TEQ values Ein*Sight In order to enhance and to assess the correlation factors established, further work was necessary to treat these data. [...]
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