# Development of a Gold Nanoparticle-Based Amplification-Free Nanobiosensor for Rapid DNA Detection Supported by Machine Learning

## TL;DR

A new gold nanoparticle-based sensor with machine learning can detect GM soybean DNA quickly and without amplification, making it ideal for on-site testing.

## Contribution

A label-free, amplification-free nanobiosensor integrated with machine learning for rapid and accurate GMO detection.

## Key findings

- The nanobiosensor achieved a detection limit of approximately 2.5 ng μL−1 for the Cry1Ac gene.
- The system demonstrated over 90% classification accuracy using a Support Vector Machine algorithm.
- The method eliminates the need for PCR or enzymatic amplification, reducing assay time and complexity.

## Abstract

The global expansion of genetically modified (GM) crop cultivation has increased the demand for analytical platforms that can provide rapid, reliable, and cost-effective detection of GM-derived ingredients to support traceability, regulatory compliance, and accurate labeling. Conventional molecular assays such as polymerase chain reaction (PCR) and isothermal amplification are highly sensitive and specific but depend on sophisticated instrumentation and trained personnel, limiting their applicability in field settings. Here, we present a label-free and amplification-free nanobiosensor based on citrate-capped gold nanoparticles (AuNPs) for the direct colorimetric detection of the Cry1Ac gene associated with the MON87701 soybean event, without the use of polymerase chain reaction (PCR) or any enzymatic nucleic acid amplification step. The assay relies on the localized surface plasmon resonance (LSPR) of AuNPs, which induces a red-to-purple color transition upon hybridization between complementary DNA strands. Critical reaction parameters, including NaCl concentration, AuNP size, and ionic strength, were optimized to enable selective and reproducible aggregation. Integration with a Support Vector Machine (SVM) algorithm enabled automated spectral classification and semi-quantitative discrimination of GM content levels. The optimized AuNP–SVM system achieved high sensitivity (limit of detection ≈ 2.5 ng μL−1, depending on nanoparticle batch), strong specificity toward Cry1Ac-positive sequences, and reproducible classification accuracies exceeding 90%. By eliminating enzymatic amplification steps, the proposed platform significantly reduces assay time, operational complexity, and instrumentation requirements, making it suitable for rapid on-site GMO screening.