Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis.

Area of Science:

• Agricultural Science
• Biotechnology
• Analytical Chemistry

Background:

• Environmental risks associated with gene flow from genetically engineered organisms necessitate reliable detection methods.
• Accurate, rapid, and cost-effective techniques are needed to monitor genetically modified (GM) organisms in crops and derived products.

Purpose of the Study:

• To develop and validate a non-destructive method for identifying GM maize kernels.
• To assess the efficacy of hyperspectral imaging combined with chemometric analysis for GM maize detection.

Main Methods:

• Hyperspectral imaging in the near-infrared (NIR) range (874.41-1733.91 nm) was employed on GM and non-GM maize kernels.
• Chemometric methods, including Principal Component Analysis (PCA), Support Vector Machine (SVM), and Partial Least Squares Discriminant Analysis (PLS-DA), were used for data analysis.
• Competitive Adaptive Reweighted Sampling (CARS) was utilized to select optimal wavelengths for classification models.

Main Results:

• Hyperspectral imaging combined with chemometric analysis effectively differentiated GM maize kernels from non-GM counterparts.
• Classification models achieved nearly 100% accuracy in both calculation and prediction.
• Reduced models using 54 selected wavelengths demonstrated rapid classification suitable for online applications.
• GM maize kernels were successfully visualized on prediction maps by analyzing individual pixel features.

Conclusions:

• Hyperspectral imaging and chemometric data analysis offer a promising, non-destructive technique for identifying GM maize kernels.
• This method overcomes limitations of traditional analytical techniques, such as complex sampling procedures.
• The developed approach provides an accurate and efficient tool for monitoring GM maize in agricultural and food production systems.