Raman spectroscopy for discriminating transgenic corns

Abstract

Discrimination of genetically modified organisms is increasingly required by legislation and consumers worldwide. Currently the most commonly used detection methods for identification of transgenic crops are high cost, destructive and time-consuming, so not suitable for fast and extensive application. Raman is a noninvasive and nondestructive spectroscopic technique capable of extracting sample fingerprints. In this paper, Raman spectroscopy and chemometric tools were evaluated for discrimination of transgenic corn. Different spectral preprocessing as well as algorithms for variables selection were evaluated to fit a classifier model based on linear discriminant analysis (LDA). Results showed spectral bands assigned to carbohydrates and carotenoids responsible for classes discrimination. The best classifier achieved 87.5 % of predictive accuracy. These results suggest that genetic differences between evaluated classes are also expressed in their chemical composition, which could be detected using Raman spectroscopy. The developed method is clean, fast and can contribute for establishing normative about genetically modified foods.