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Articles

Optimal definition of the limit of detection (LOD) in detecting genetically modified grains from heterogeneous grain lots

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Pages 36-53 | Accepted 15 Jun 2017, Published online: 10 Jul 2017

References

  • Bliss, C. I., & Owen, A. R. G. (1958). Negative binomial distributions with a common k. Biometrika, 45, 37–58.10.1093/biomet/45.1-2.37
  • Corless, R. M., Gonnet, G. H., Hare, D. E. G., Jeffrey, D. J., & Knuth, D. E. (1996). On the Lambert W function. Advances in Computational Mathematics, 5, 329–359.10.1007/BF02124750
  • Cox, D. R., & Snell, E. J. (1989). Analysis of binary data (2nd ed.). Boca Raton: Chapman & Hall/CRC.
  • Deming, W. E. (1950). Some theory of sampling. Toronto: General Publishing.
  • Emslie, K. R., Whaites, L., Griffiths, K. R., & Murby, E. J. (2007). Sampling plan and test protocol for the semiquantitative detection of genetically modified canola (Brassica napus) seed in bulk canola seed. Journal of Agricultural and Food Chemistry, 55, 4414–4421.10.1021/jf070267i
  • Esbensen, K. H., Friis-Petersen, H. H., Petersen, L., Holm-Nielsen, J. B., & Mortensen, P. P. (2007). Representative process sampling – in practice: Variographic analysis and estimation of total sampling errors (TSE). Chemometrics and Intelligent Laboratory Systems, 88, 41–59.10.1016/j.chemolab.2006.09.011
  • Esbensen, K. H., Paoletti, C., & Minkkinen, P. (2012a). Representative sampling of large kernel lots I. Theory of sampling and variographic analysis. Trends in Analytical Chemistry, 32, 154–164.10.1016/j.trac.2011.09.008
  • Esbensen, K. H., Paoletti, C., & Minkkinen, P. (2012b). Representative sampling of large kernel lots III. General considerations on sampling heterogeneous foods. Trends in Analytical Chemistry, 32, 178–184.10.1016/j.trac.2011.12.002
  • European Commission. (2004). Commission recommendation of 4 October 2004 on technical guidance for sampling and detection of genetically modified organisms and material produced from genetically modified organisms as or in products in the context of Regulation (EC) No. 1830/2003. Retrieved from http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2004:348:0018:0026:EN:PDF
  • Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503–513.
  • Fisher, R. A. (1973). Statistical methods and scientific inference (3rd ed.). New York, NY: Hafner Press.
  • Hilbeck, A., Binimelis, R., Defarge, N., Steinbrecher, R., Székács, A., Wickson, F., … Novotny, E. (2015). No scientific consensus on GMO safety. Environmental Sciences Europe, 27, 1.10.1186/s12302-014-0034-1
  • Holst-Jensen, A. (2007). Sampling, detection, identification and quantification of genetically modified organisms (GMOs). In Y. Pico (Ed.), Food toxicants analysis. Techniques, strategies and developments (231–268). Amsterdam. Elsevier.
  • Horwitz, W. (1982). Evaluation of analytical methods used for regulation of foods and drugs. Analytical Chemistry, 54, 67A–76A.10.1021/ac00238a765
  • Hubbard, R., & Bayarri, M. J. (2003). Confusion over measures of evidence (p’s) versus errors (α’s) in classical statistical testing. The American Statistician, 57, 171–178.10.1198/0003130031856
  • IPPC. (2008). Methodologies for sampling of consignments (ISPM No. 31). Rome: International Plant Protection Convention, FAO.
  • ISO. (1997). ISO 11843–1: Capability of detection – Part 1: Terms and definitions. Genève: International Organization for Standardization.
  • ISO. (1999). ISO 2859–1: Sampling procedures for inspection by attributes – part 1: Sampling schemes indexed by acceptance quality limit (AQL) for lot-by-lot inspection. Genève: International Organization for Standardization.
  • ISO. (2005). ISO 13528: Statistical methods for use in proficiency testing by interlaboratory comparisons. Genève: International Organization for Standardization.
  • ISO. (2006). ISO 24276: Foodstuffs – methods of analysis for the detection of genetically modified organisms and derived products – general requirements and definitions. Genève: International Organization for Standardization.
  • ISO. (2009). ISO 24333: Cereals and cereal products – sampling. Genève: International Organization for Standardization.
  • Japanese Industrial Standards Committee. (1956). JIS Z 9002: Single sampling inspection plans having desired operation characteristics. Part 1. Sampling by attributes. Tokyo: Japanese Standards Association. (in Japanese).
  • Johnson, N. L., Kotz, S., & Kemp, A. W. (2005). Univariate discrete distrbutions (3rd ed.). New York, NY: Wiley.10.1002/0471715816
  • Jovanovic, B. D., & Levy, P. S. (1997). A look at the rule of three. American Statistician, 51, 137–139.
  • Kodama, T., Kasahara, M., Minegishi, Y., Futo, S., Sawada, C., Watai, M., … Hino, A (2011). Qualitative PCR method for roundup ready soybean: Interlaboratory study. Journal of AOAC International, 94, 224–231.
  • Liu, F., & Cui, L. (2013). A design of attributes single sampling plans for three-class products. Quality Technology & Quantitative Management, 10, 369–387.10.1080/16843703.2013.11673421
  • Macarthur, R., & von Holst, C. (2012). A protocol for the validation of qualitative methods of detection. Analytical Methods, 4, 2744–2754.10.1039/c2ay05719 k
  • Macarthur, R., Murray, A. W., Allnutt, T. R., Deppe, C., Hird, H. J., Kerins, G. M., … Hugo, S. (2007). Model for tuning GMO detection in seed and grain. Nature Biotechnology, 25, 169–170.10.1038/nbt0207-169
  • Mano, J., Harada, M., Takabatake, R., Furui, S., Kitta, K., Nakamura, K., … Futo, S. (2012). Comprehensive GMO detection using real-time PCR array: Single-laboratory validation. Journal of AOAC International, 95, 508–516.10.5740/jaoacint.11-388
  • McCulloch, C. E., Searle, S. R., & Neuhans, J. M. (2008). Generalized, linear, and mixed models (2nd ed.). Hoboken, NJ: Wiley.
  • Ministry of Agriculture Forestry and Fisheries. (2007). Operating procedure for the inspection about genetically modified organisms. Annex 1: Detection procedure of GM corn (CBH351) in maize seeds for planting. (in Japanese). Retrieved from www.nias.affrc.go.jp/gmo/doc/notification_maff_190405.pdf
  • Ministry of Health Labor and Welfare. (2001). Inspection method of food using genetically modified organisms, Notification No. 110, Annex. Tokyo: Japanese Ministry of Health, Labor and Welfare. (in Japanese).
  • Minkkinen, P., Esbensen, K. H., & Paoletti, C. (2012). Representative sampling of large kernel lots II. Application to soybean sampling for GMO control. Trends in Analytical Chemistry, 32, 165–177.10.1016/j.trac.2011.12.001
  • Neyman, J., & Pearson, E. S. (1928). On the use and interpretation of certain test criteria for purposes of statistical inference: Part I. Biometrika, 20, 175–240.
  • Onori, R., & De Giacomo, M. (2010). Seeds: Transgenes and genetic modification (GM). In D. R. Heldman, D. G. Hoover, & M. B. Wheeler (Eds.), Encyclopedia of biotechnology in agriculture and food (587–590). New York, NY: Taylor & Francis. 10.1081/E-EBAF
  • Paoletti, C., Donatelli, M., Kay, S., & Van Den Eede, G. (2003). Simulating kernel lot sampling: The effect of heterogeneity on the detection of GMO contaminations. Seed Science and Technology, 31, 629–638.10.15258/sst
  • Paoletti, C., Heissenberger, A., Mazzara, M., Larcher, S., Grazioli, E., Corbisier, P., … Moran, G. (2006). Kernel lot distribution assessment (KeLDA): A study on the distribution of GMO in large soybean shipments. European Food Research and Technology, 224, 129–139.10.1007/s00217-006-0299-8
  • Shrivastava, A., & Gupta, V. B. (2011). Methods for the determination of limit of detection and limit of quantitation of the analytical methods. Chronicles of Young Scientists, 2, 21–25.10.4103/2229-5186.79345
  • Taylor, L. R. (1961). Aggregation, variance and the mean. Nature, 189, 732–735.10.1038/189732a0
  • Taylor, L. R. (1984). Assessing and interpreting the spatial distribution of insect populations. Annual Review of Entomology, 29, 321–357.10.1146/annurev.en.29.010184.001541
  • USDA Federal Grain Inspection Service. (2001). GIPSA directive 9181.1 (2-26-01), Testing for StarLink™ cornlateral flow test strip method. Washington, DC: USDA.
  • Wehling, P., LaBudde, R. A., Brunelle, S. L., & Nelson, M. T. (2011). Probability of detection (POD) as a statistical model for the validation of qualitative methods. Journal of AOAC International, 94, 335–347.
  • Yamamura, K. (2000). Colony expansion model for describing the spatial distribution of populations. Population Ecology, 42, 161–169.10.1007/PL00011995
  • Yamamura, K., & Ishimoto, M. (2009). Optimal sample size for composite sampling with subsampling, when estimating the proportion of pecky rice grains in a field. Journal of Agricultural, Biological, and Environmental Statistics, 14, 135–153.10.1198/jabes.2009.0009
  • Yamamura, K., & Sugimoto, T. (1995). Estimation of the pest prevention ability of the import plant quarantine in Japan. Biometrics, 51, 482–490.10.2307/2532936
  • Yamamura, K., Katsumata, H., Yoshioka, J., Yuda, T., & Kasugai, K. (2016). Sampling inspection to prevent the invasion of alien pests: Statistical theory of import plant quarantine systems in Japan. Population Ecology, 58, 63–80.10.1007/s10144-015-0521-2
  • Zeger, S. L., Liang, K.-Y., & Albert, P. S. (1988). Models for longitudinal data: A generalized estimating equation approach. Biometrics, 44, 1049–1060.10.2307/2531734

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