Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/89513
標題: 即時定量 PCR 檢測基因改造玉米及大豆之量測不確定度評估
Evaluation of measurement uncertainty in real-time PCR for quantitative testing of genetically modified maize and soybean
作者: Yi-Ting Zhang
張翊庭
關鍵字: NO;無
引用: 工業技術研究院。2001。量測不確定度評估─理論與實務。新竹。工業技術研究院。 牛惠之、郭華仁、滕沛倫、彭英泰、陳詩欣。2005。基因改造產品─發展、爭議、管理與規範。初版。台北:行政院農業委員會動植物防疫檢疫局。 李孟昭。2010。基因改造作物定量分析之量測不確定度評估。碩士論文。台中,國立中興大學農藝學系研究所。 范明仁、陳述、林俊義、蔡奇助、楊藹華、王仕賢、林學詩。2005。基因轉殖植物檢測技術開發體系之建立。出自'基因轉殖植物之生物安全評估與檢測專刊',林俊義、石信德、張有明、王強生主編。pp. 105-128。台中:行政院農業委員會農業試驗所。 衛生福利部食品藥物管理署。2014。含基因改造原料食品標示之專家會 議 討 論 結 果 。 http://www.fda.gov.tw/TC/newsContent.aspx?id=11073&chk=71ff456d-c464-45aa-b334-2173886bdd8c#.U401uvmSzAR. Accessed June 3, 2014. Ambrus, A. 2004. Reliability of measurements of pesticide residues in food. Accredit. Qual. Assur. 9:288-304. Analytical Methods Committee. 1995. Uncertainty of measurement:implications of its use in analytical science. Analyst 120:2303-2308. Anklam, E., F. Gadani, P. Heinze, H. Pijnenburg, and G. Van dan Eede. 2002. Analytical methods for detection and determination of genetically modified organisms in agricultural crops and plant-derived food products. Eur. Food Res. Technol. 214:3-26. Bergmans, B., F. Idczak, P. Maete, J. Nicolas, and S. Petitjean. 2008. Setting up a decision rule from estimated uncertainty: emission limit value for PCDD and PCDF incineration plants in Wallonia, Belgium. Accredit. Qual. Assur. 13:639-644. Bruggemann, L. and R. Wennrich. 2002. Evaluation of measurement uncertainty for analytical procedures using a linear calibration function. Accredit. Qual. Assur. 7:269-273. Burns, M. and H. Valdivia. 2007. A procedural approach for the identification of sources of uncertainty associated with GM quantification and real-time quantitative PCR measurements. Eur. Food Res. Technol. 226:7-18. Del Gaudio, S., A. Cirillo, G. Di Bernarod, U. Galderisi, and M. Cipollaro. 2012. Verification of real-time PCR methods for qualitative and quantitative testing of genetically modified organisms. J. Food Qual. 35:442-447. Ellison, S.L.R. and A. Williams. 2007. Use of uncertainty information in compliance assessment. EURACHEM, St. Gallen, Switzerland. Ellison, S.L.R. and A. Williams. 2012. Quantifying uncertainty in analytical measurement. EURACHEM, St. Gallen, Switzerland. ENGL. 2008. Definition of minimum performance requirements for analytical methods of GMO testing. JRC, Ispra, Italy. Gachet, E., G.G. Martin, F. Vigneau, and G. Meyer. 1999. Detection of genetically modified organisms (GMOs) by PCR: a brief review of methodologies available. Trends Food Sci. Technol. 9:380-388. Griffiths, K.R., D.G. Burke, and K.R. Emslie. 2011. Quantitative polymerase chain reaction: a framework for improving the quality of results and estimating uncertainty of measurement. Analytical Methods 3:2201-2211. Gruè re, G.P. and S.R. Rao. 2007. A review of international labeling policies of genetically modified food to evaluate India's proposed rule. J. Agrobiotechnol. Manage. Econ. 10:51-64. Heid, C.A., J. Stevens, K.J. Livak, and P.M. Williams. 1996. Real-time quantitative PCR. Genome Res. 6:986-994. Hernández, M., T. Esteve, S. Prat, and M. Pla. 2004. Development of real-time PCR systems on SYBR Green I, Amplifluor and TaqMan technologies for specific quantitative detection of the transgenic maize event GA21. Journal of Cereal Science 39:99-107. Holst-Jensen, A., S.B. R?nning, A. L?vseth, and K.G. Berdal. 2003. PCR technology for screening and quantification of genetically modified organisms (GMOs). Anal. Bioanal. Chem. 375:985-993. Islam, M.D., M.S. Turcu, and A. Cannavan. 2008. Comparison of methods for the estimation of measurement uncertainty for an analytical method for sulphonamides. Food Addit. Contam. 25:1439-1450. ISO. 1995. Guide 98-3:2008: uncertainty of measurement—part 3: guide to the expression of uncertainty in measurement (GUM:1995). ISO, Geneva, Switzerland. ISO. 2002. Accuracy (trueness and precision) of measurement methods and results ─ Part 2: Basic method for the determination of repeatability and reproducibility of standard measurement method (ISO 5725-2). ISO, Geneva, Switzerland. James, C. 2014. Global status of commercialized biotech/GM crops: 2013. ISAAA Brief 46. ISAAA, Ithaca, NY. Jasbeer, K., F.M. Ghazali, Y.K. Cheah, and R. Son. 2008. Application of DNA and immunoassay analytical methods for GMO testing in agricultural crops and plant-derived products. ASEAN food j. 15:1-25. JCGM. 2012. Evaluation of measurement data – the role of measurement uncertainty in conformity assessment. JCGM, Geneva, Switzerland. Kodama, T., Y. Kurosaw, K. Kitta, and S. Naito. 2010. Tendency for interlaboratory precision in the GMO analysis method based on real-time PCR. J. AOAC Int. 93:734-749. Love, J.L., P. Scholes, B. Gilpin, M. Savill, S. Lin, and L. Samuel. 2006. Evaluation of uncertainty in quantitative real-time PCR. J. Microbiol. Methods 67:349-356. Lyn, J.A., M.H. Ramsey, A.P. Damant, and R. Wood. 2007. Empirical versus modeling approaches to the estimation of measurement uncertainty caused by primary sampling. Analyst 131:1231-1237. Macarthur, R., M. Feinberg, and Y. Bertheau. 2010. Construction of measurement uncertainty profiles for quantitative analysis of genetically modified organisms based on interlaboratory validation data. J. AOAC Int. 93:1046-1056. Magnusson, B., T. Näykki, H. Hovind, and M. Krysell. 2003. Handbook for calculation of measurement uncertainty in environmental laboratories(Nordtest Technical Report 537). Nordtest, Oslo, Norway. Marcó , A., R. Companyó , R. Rubio, M. Pueyo, and E. Vilalta. 2007. Comparison of 'bottom-up' and 'top-down' strategies for the estimation of the uncertainty in organic elemental analysis. Microchim. Acta 159:387-393. Maroto, A., R. Boqueè, J. Riu, and F.X. Rius. 1999. Evaluating uncertainty in routine analysis. Trends Anal. Chem. 18:577-584. Meyer, V.R. 2007. Measurement uncertainty. J. Chromatogr. A 1158:15-24. Montgomery, D.C. 2009. Design and analysis of experiments. 7th ed. Wiley, New York. Priel, M. 2009. From GUM to alternative methods for measurement uncertainty evaluation. Accredit. Qual. Assur. 14:235-241. Ramessar, K., T. Capell, R.M. Twyman, H. Quemada, and P. Christou. 2008. Trace and traceability─a call for regulatory harmony. Nat.Biotechnol. 26:975-978. Ramsey, M.H. and S.L.R. Ellison. 2007. Measurement uncertainty arising from sampling: A guide to methods and approaches. EURACHEM, St. Gallen, Switzerland. Rodomonte, A.L., A. Montinaro, and M. Bartolomei. 2006. Uncertainty evaluation in the chloroquine phosphate potentiometric titration: Application of three different approaches. J. Pharm. Biomed. Anal. 42:56-63. Spagnoli, M., F. Pintus, M. Zamperlini, F. Magalini, E. Zago, E. Benetti, M. Delledomme, and P.S. Cocconcelli. 2008. Application of measurement uncertainty to GMO analysis in real-time PCR. Global Conference on GMO Analysis. pp62. JRC, Brussel, Belgium. Song, J., S. Lei, Y. Liu, D. Wang, Q. Yin, F. Zhang, W. Liu, and L. Chang. 2011. Uncertainty in measuring construct-specific fragments of genetically modified maize MON863 by real time quantitative PCR. Agricultural Science & Technology – Hunan 12:1777-1780. Trapmann, S., M. Burns, H. Broll, R. Macarthur, R. Wood, and J. Zel. 2009. Guidance document on measurement uncertainty for GMO testing laboratories. 2nd ed. Luxembourg: Official Publications of the European, Luxembourg. Viljoen, C.D., B.K. Dajee, and G.M. Botha. 2006. Detection of GMO in food products in South Africa: Implication of labeling. Afr. J. Biotecgnol. 5: 73-82. Yong, B., D. Wang, J. Song, S. Lei, Q. Yin, F. Zhang, W. Liu, and L. Chang. 2013. Estimation of measurement uncertainty resulting from the regression equation in quantitative detection of genetically modified rice (Bt gene). Journal of Sichuan Normal University 36:284-288. Zel , J., K. Gruden, K. Canker, D. Stebih , and A. Blejec. 2007. Calculation of measurement uncertainty in quantitative analysis of genetically modified organisms using intermediate precision-a practical approach. J. AOAC Int. 90:582-586.
摘要: 
現今,由於全球基因改造生物(Genetically Modified Organisms,GMOs)持續的發展,人們逐漸重視其對生態、國際貿易與食品安全的影響。為確保民眾在基改作物、傳統栽培作物和有機作物間,有知道且自由選擇的權利,各國便制定基改生物相關之法規閾值,為了因應相關之管理規定,基改作物定量分析相當重要。對於一個給定樣本重複量測其基改作物含量,無論在同一實驗室內或不同實驗室間,可預期的是無法測得相同的值。量測不確定度為描述量測結果變動範圍的一個參數,若定量結果附上其不確定度便更能正確解釋其結果。目前有數種估計量測不確定度的方法,一種是先估計試驗中每個步驟各別不確定度,再組合成最終量測不確定度的 bottom-up 策略,另一個為top-down 策略,是由試驗所得數據直接估計其量測不確定度。

本研究以 top-down 及 bottom-up 評估策略,估計定量分析基因改造大豆 DP-305423 與玉米 NK603 品項之認證參考物質的量測不確定度。以 top-down 策略所得量測不確定度結果介於 6.26%與 24.71%之間;bottom-up 策略所得量測不確定度結果為 24.75%與 31.99%。並且發現主要不確定度來源為樣本製備 約占總變異的 72.12 至 94.73%。,在實行上各策略有其優、缺點,以 top-down 策略來說,可以試驗的數據估計量測不確定度,而不必再額外設計試驗獲得不確定度,bottom-up 策略則是能成功考慮量測過程中各個影響因子。最後本研究以符合性評估(conformity assessment)解釋量測不確定度如何影響判斷定量結果是否符合規定,提供給決策者判定樣本是否符合規範的依據。

Nowadays there is a persistent development on Genetically Modified Organisms (GMOs) globally. Human beings pay more and more attention to the events' impacts to environmental effects, global trade and food safety. To ensure that human beings have the right to be informed and free choice for GM crops, conventional crops, or organic crops, the thresholds of the regulation about GMOs have been defined in many countries. In order to meet the requirements of the regulation, the quantitative analysis of GMOs has seen increasingly important, especially using real-time PCR technology. Whether in the same laboratory or different ones, it can be expected that repeated measurements of the GMO content of a given sample can't be the same. Measurement uncertainty is a parameter which describes the possible fluctuations of the measurement. If the quantitative result attached its uncertainty, the result of a measurement could be properly interpreted. Up to now, several approaches to estimate uncertainty exist. One of them is the bottom-up approach that calculates all the specific factors influencing the final result. The other approach is the top-down which estimates the uncertainty through the obtained data from sample analyses.

In this study, the measurement uncertainties in quantitative analysis of GM soybean DP-305423 event and GM maize NK603 event were estimated through the top-down approach and the bottom-up approach respectively. The measurement uncertainties were between 6.26% and 24.71% by the top-down approach. However, the measurement uncertainties were 24.75% and 31.99% by the bottom-up approach. The results indicated that the values of measurement uncertainty were consistent and much measurement uncertainties may be attributed to the sampling procedure, taking approximately about 72.12 to 94.73%. All approaches presented different advantages and disadvantages in their performance. The top-down approach estimated uncertainty using validation data, rather than performing additional experiments to obtain uncertainty. The bottom-up approach successfully considered all significant factors of measurement process. Finally, conformity assessment was used to explain how the measurement uncertainty affected the judgment of the quantitative result in our study.
URI: http://hdl.handle.net/11455/89513
其他識別: U0005-0606201513504500
Rights: 同意授權瀏覽/列印電子全文服務,2018-07-15起公開。
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