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 mode 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 was recorded and processed with MSConvert software to obtain a profile of peak intensities in 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, as well as the data fusion of both acquisition modes, showed to be good chemical descriptors to address cranberry extracts authentication. Validation of the proposed methodology showed a prediction rate of 100% of the samples. Obtained data was 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%