Genetic Algorithm Applied to the Selection of Conditions for the Simultaneous Quantification of Three-Food Colorants Using a Hand Scanner

Abstract

A low-cost and reliable method employing a hand scanner for simultaneous colorimetric quantification of food colorant mixtures including amaranth, brilliant blue, and tartrazine is presented. Compared to a spectrophotometer, a hand scanner is inexpensive, available in most work offices, and easier to operate by non-skilled users. The appropriate instrumental conditions for measuring were selected using a genetic algorithm (GA) coupled with partial least square (PLS) regression. Using the conditions selected by GA, PLS and multiple linear regression (MLR) were compared, and similar results for the two methods were obtained. Under the selected conditions for each of the colorants, artificial neural network (ANN) including three layers of nodes and a Levenberg-Marquardt learning rule was employed, which improved the results. The concentration ranges for the three colorants in the multivariate calibration models were 0.00–5.31 mmol L−1 for amaranth, 0.00–1.85 mmol L−1 for brilliant blue, and 0.00–21.57 mmol L−1 for tartrazine. The minimum estimated relative standard error percentages (RSE%) for prediction of analytes in synthetic samples, using ANN with optimized parameters, were 16.8% for amaranth, 4.8% for brilliant blue, and 5.6% for tartrazine. A number of commercial food products were analyzed satisfactorily with the proposed method.