Soybean GMO Quantification Prediction Using PCR Screening Data

European Union GMO testing data supports a statistical framework for predicting the quantifiability of genetically modified soybean events. The dataset includes quantification cycle (Cq) values from screening elements like P35S, T-nos, CP4 epsps, and the lectin reference gene. Davide La Rocca authored this proof-of-concept study, which was last updated in April 2026.

Use Cases

• Predict soybean GM event quantifiability from ΔCq values of screening elements P35S, T-nos, CP4 epsps, and lectin.
• Optimize laboratory testing workflow efficiency by identifying samples likely to yield unquantifiable GM content from screening Cq values.
• Validate statistical models for GMO detection using real-life and spiked sample data referenced in the study.
• Assess the risk of false positives in GMO screening phases based on the relationship between Cq values and subsequent quantification success.

Strengths

• Dataset is based on a defined statistical framework for GMO testing workflow optimization.
• Includes data from both real-life and spiked samples for in-house verification of the approach.
• Published under a permissive CC-BY-4.0 license for open reuse.

Limitations

• Dataset size is only 64.3 KB, indicating very limited scope and likely a small number of observations.
• Specific row count, column names, and sample data are unavailable, hindering direct analysis.
• Geographic focus is primarily on the European Union regulatory context, which may limit generalizability.

Provenance

Source

Davide La Rocca via the figshare platform.

Collection Method

Data generated from PCR analysis of soybean samples, both real-life and spiked, as part of a proof-of-concept study.

Freshness

Last updated in April 2026, indicating the data is current relative to its publication date.

Geography

European Union (context of the regulatory framework and testing).