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PCR

PCR

Real Time RT-PCR

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

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Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

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Measuring Digital PCR Quality: Performance Parameters and Their Optimization.

A Lievens¹, S Jacchia¹, D Kagkli¹

¹Molecular Biology and Genomics Unit, European Commission - Joint Research Centre, Institute for Health and Consumer Protection, 21027 Ispra (VA), Italy.

|May 6, 2016

Summary

This summary is machine-generated.

Digital PCR (polymerase chain reaction) offers absolute quantification for food analysis. This study introduces statistical methods to ensure assay robustness and accurate quantification in routine food and feed testing.

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Area of Science:

Food Science
Molecular Biology
Analytical Chemistry

Background:

Digital PCR (dPCR) is increasingly used for DNA-based food analysis due to its absolute quantification capabilities.
Applications include determining food composition, detecting GMO content, and ensuring labeling compliance.
Standardized methods for assessing dPCR assay performance in this field are lacking.

Purpose of the Study:

To introduce statistically-based parameters for evaluating the robustness of dPCR assays in food analysis.
To present criteria for post-run evaluation of quantification accuracy.
To assess the utility of Poisson confidence intervals and propose an alternative for variability assessment.

Main Methods:

Development and presentation of three statistical parameters for assay robustness evaluation.
Definition of post-run criteria for assessing quantification accuracy.
Comparative analysis of Poisson confidence intervals versus an alternative strategy for capturing analytical variability.

Main Results:

The proposed statistical parameters provide a straightforward method for users to assess dPCR assay robustness.
Post-run evaluation criteria aid in verifying the accuracy of quantification results.
An alternative strategy to Poisson confidence intervals may better represent the overall variability in the analytical chain.

Conclusions:

Implementing these statistical principles enhances the reliability of dPCR assays for food and feed analysis.
The developed criteria support routine, accurate, and robust DNA-based testing in the food industry.
Further investigation into alternative statistical approaches can improve the precision of dPCR quantification.