Estimation of the percentage of transgenic Bt maize in maize flour mixtures using perfusion and monolithic reversed-phase high-performance liquid chromatography and chemometric tools

Abstract

The estimation of the percentage of transgenic Bt maize in maize flour mixtures has been achieved in this work by high-performance liquid chromatography using perfusion and monolithic columns and chemometric analysis. Principal component analysis allowed a preliminary study of the data structure. Then, linear discriminant analysis was used to develop decision rules to classify samples in the established categories (percentages of transgenic Bt maize). Finally, linear regression (LR) and multivariate regression models (namely, principal component analysis regression (PCR), partial least squares regression (PLS-1), and multiple linear regression (MLR)) were assayed for the prediction of the percentages of transgenic Bt maize present in a maize flour mixture. Using the relative areas of the protein peaks, MLR provided the best models and was able to predict the percentage of transgenic Bt maize in flour mixtures with an error of ±5.3%, ±2.3%, and ±3.8% in the predictions of Aristis Bt, DKC6575, and PR33P67, respectively.