Development and analytical validation of a simple multivariate calibration method using digital scanner images for sunset yellow determination in soft beverages.
This paper proposed a novel methodology for the quantification of an artificial dye, sunset yellow (SY), in soft beverages, using image analysis (RGB histograms) and partial least squares regression. The developed method presented many advantages if compared with alternative methodologies, such as HPLC and UV/VIS spectrophotometry. It was faster, did not require sample pretreatment steps or any kind of solvents and reagents, and used a low cost equipment, a commercial flatbed scanner. This method was able to quantify SY in isotonic drinks and orange sodas, in the range of 7.8-39.7 mg L(-1), with relative prediction errors lower than 10%. A multivariate validation was also performed according to the Brazilian and international guidelines. Linearity, accuracy, sensitivity, bias, prediction uncertainty and a recently proposed tool, the β-expectation tolerance intervals, were estimated. The application of digital images in food analysis is very promising, opening the possibility for automation.
['Botelho|Bruno G|BG|', 'de Assis|Luciana P|LP|', 'Sena|Marcelo M|MM|']
D001391:Azo Compounds D001628:Beverages D001938:Brazil D002138:Calibration D016018:Least-Squares Analysis
Image analysis RGB histograms Food dyes PLS Quality control Soft drinks