UHPLC-HRMS (orbitrap) fingerprinting in the classification and authentication of cranberry-based natural products and pharmaceuticals using multivariate calibration methods

Abstract

UHPLC-HRMS (orbitrap) fingerprinting in negative and positive H-ESI modes was applied to the characterization, classification and authentication of cranberry-based natural and pharmaceutical products. HRMS data in full scan mode (m/z 100–1500) at a resolution of 70 000 full-width at half maximum were recorded and processed with MSConvert software to obtain a profile of peak intensities as a function of m/z values and retention times. A threshold peak filter of absolute intensity (10⁵ counts) was applied to reduce data complexity. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) revealed patterns able to discriminate the analyzed samples according to the fruit of origin (cranberry, grape, blueberry and raspberry). Discrimination among cranberry-based natural and cranberry-based pharmaceutical preparations was also achieved. Both UHPLC-HRMS fingerprints in negative and positive H-ESI modes and the data fusion of both acquisition modes proved to be good chemical descriptors to perform cranberry extract authentication. Validation of the proposed methodology showed a prediction rate of 100% of the samples. Obtained data were further treated by partial least squares (PLS) regression to identify frauds and quantify the percentage of adulterant fruits in cranberry-fruit extracts, achieving prediction errors in the range 0.17–3.86%.