Quantitative impurity profiling by principal component analysis of high-performance liquid chromatography–diode array detection data

2006 ◽  
Vol 1108 (1) ◽  
pp. 50-67 ◽  
Author(s):  
Kent Wiberg
2020 ◽  
Vol 103 (3) ◽  
pp. 779-783
Author(s):  
Özlem Aksu Dönmez ◽  
Şule Dinç-Zor ◽  
Bürge Aşçı ◽  
Abdürrezzak E Bozdoğan

Abstract Background In many countries, the levels of synthetic food additives causing harm to humans have been determined and their use has been controlled by legal regulations. Sensitive, accurate and low-cost analysis methods are required for food additive determination. Objective In this study, a fast high performance liquid chromatography-diode array detection (HPLC-DAD) analytical methodology for quantification of sodium benzoate, potassium sorbate, ponceau 4R, and carmoisine in a beverage was proposed. Methods Partial least squares (PLS) and principal component regression (PCR) multivariate calibration methods applied to chromatograms with overlapped peaks were used to establish a green and smart method with short isocratic elution. A series of synthetic solutions including different concentrations of analytes were used to test the prediction ability of the developed methods. Conclusions The average recoveries for all target analytes were in the range of 98.27–101.37% with average relative prediction errors of less than 3%. The proposed chemometrics-assisted HPLC-DAD methods were implemented to a beverage successfully. Analysis results from sodium benzoate, potassium sorbate, ponceau 4R, and carmoisine in a beverage by PLS-2 and PCR were statistically compared with conventional HPLC. Highlights The HPLC methods coupled with the PLS-2 and PCR algorithm could provide a simple, quick and accurate strategy for simultaneous determination of sodium benzoate, potassium sorbate, ponceau 4R, and carmoisine in a beverage sample.


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