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|標題:||Evaluation of measurement uncertainty in real-time PCR for quantitative testing of genetically modified maize and soybean
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
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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.
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