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Wurz, A., and R. Willmund. 1997. Identi cation of transgenic glyphosate-resistant soybeans. In Food Produced by Means of Genetic Engineering, 2nd status report, edited by G. A. Schreiber and K. W. B g. Berlin: Bundesinstitut f r gesundheitlichen Verbraucherschutz und Veterin rmedizin (BgVV). Wurz, A., H. R ggeberg, P. Broadmann, H. U. Waiblinger, and K. Pietsch. 1998. Deuts Lebensm Rund 94:159 161. Wurz, A., A. Bluth, P. Zeltz, C. Pfeifer, and R. Willmund. 1999. Food Control 10:385 389. Xu, J., H. Miao, H. Wu, W. Huang, R. Tang, M. Qiu, J. Wen, S. Zhu, and Y. Li. 2006. Biosens Bioelectron 22:71 77. Xu, J., S. Zhu, H. Miao, W. Huang, M. Qiu, Y. Huang, X. Fu, and Y. Li. 2007. Journal of Agricultural Food Chemistry 55:5575 5579. Yang, L., A. Pan, J. Jia, J. Ding, J. Chen, H. Cheng, C. Zhang, and D. Zhang. 2005. Journal of Agricultural Food Chemistry 53:183 190. Yates, K. (ed.). 1999. Detection Methods for Novel Foods Derived from Genetically Modi ed Organisms. Brussels: ILSI-Europe. Zeitler, R., K. Pietsch, and H. Waiblinger. 2002. European Food Research and Technology 214:346 351. el, J., M. Mazzara, C. Savini, S. Cordeil, M. Camloh, D. tebih, K. Cankar, K. Gruden, D. Morisset, and G. Van den Eede. 2008. Food Analysis Methods 1:61 72. Zimmermann, A., R. Zehner, and D. Mebs. 1998a. Fleischwirts 78:530 533. Zimmermann, A., J. L thy, and U. Pauli. 1998b. Z. Lebensm Unters Forsch A 207:81 90. Zimmermann, A., W. Hemmer, M. Liniger, J. L thy, and U. Pauli. 1998c. Lebensm Wiss u Technol 31:664 667. Zimmermann, A., J. L thy, and U. Pauli. 2000. Lebens Wiss u Technol 33:210 216.

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It is already possible to follow a parcel from sending to reception and to know its location26 by Internet access The technique currently used is based on RFID In the near future, if there is enough demand, it will be possible to know any object s location Projects are currently dealing with dangerous goods, but an extension to all sorts of objects is easy to imagine, if not easy to implement Still another way to classify relies on the number of frequency bands that are being used in the receiver and whether it is implementing a differential approach or not (although autonomous receivers with EGNOS or WAAS are differential) As described in 3, two frequency bands are common to both GPS and Galileo (E5a/L5 and L1).

con rmed minus the proportion violated Values can range from 10 (all predictions met) to 10 (all predictions violated), with 0 indicating a random t of the data to the model It is a descriptive index of model-data t The second is a signi cance test for CI based on a randomization test strategy (for details see Hubert & Arabie, 1987, or Tracey, 2000) Essentially, the test determines the probability of obtaining the given model t in comparison to all possible permutations of the rows and columns of the matrix Table 167 shows the results of the circular model tests of the two PGI correlation matrices (Terence Tracey kindly performed these analyses for us using RANDALL (Tracey, 1997), a program he designed speci cally to do these kinds of tests of the circular order model.

) Consistent with the previous results reported, the analyses provided strong support for the circular structure of the PGI As can be seen, CIs were close to the maximum values of 1 (almost all order predictions con rmed); based on the randomization tests, in both instances, the chance probabilities for the obtained model t were signi cantly small (ps = 0004) Circular Order Versus Covariance Modeling Circular order and covariance modeling are likely to agree when data are a good t to the equally-spaced circumplex ideal However, we note that research has yet to directly compare the two approaches, especially with respect to how they handle quasi-circumplex data and data for which the circumplex model is misspeci ed Tracey (2000) describes some of the advantages and disadvantages of the two approaches to con rmatory analysis The interested reader is referred there.