Multivariate curve resolution of spectrophotometric data for the determination of artificial food colors

In the analysis of food additives, past emphasis was put on the development of chromatographic techniques to separate target components from a complex matrix. Especially in the case of artificial food colors, direct spectrophotometric measurement was seen to lack in specificity due to a high spectral overlap between different components. Multivariate curve resolution (MCR) may be used to overcome this limitation. MCR is able to (i) extract from a complex spectral feature the number of involved components, (ii) attribute the resulting spectra to chemical compounds, and (iii) quantify the individual spectral contributions with or without a priori knowledge. We have evaluated MCR for the routine analysis of yellow and blue food colors in absinthe spirits. Using calibration standards, we were able to show that MCR equally performs as compared to partial least-squares regression but with much improved chemical information contained in the predicted spectra. MCR was then applied to an authentic collective of different absinthes. As confirmed by reference analytics, the food colors were correctly assigned with a sensitivity of 0.93 and a specificity of 0.85. Besides the artificial colors, the algorithm detected a further component in some samples that could be assigned to natural coloring from chlorophyll.