Abstract
Fuzzy chromatography mass spectrometric (FCMS) fingerprinting methods in combination with principal component analysis (PCA) and soft independent modeling by class analogy (SIMCA) were developed for phenolic profiling and differentiation of American cranberry cultivars. Two FCMS fingerprinting methods, ion mobility fuzzy chromatography mass spectrometric (imFCMS) and conventional FCMS, are compared in the study. PCA and SIMCA successfully differentiated the cultivars with both methods. The six cultivars used formed three distinct groups in the PCA score plots, and each group contained one wild selection cultivar and its genetically related hybrid(s). Compared with FCMS fingerprinting, imFCMS fingerprinting provided better intra-cultivar sample clustering and inter-cultivar sample separation in PCA and superior sample classification sensitivity in SIMCA. Ultra-performance liquid chromatography–high-resolution mass spectrometry (UPLC–HRMS) analysis was conducted on selected samples to profile the phenolic difference between the cultivars. Compound identification using UPLC–HRMS revealed that flavonoid compounds, including flavonol glycosides, proanthocyanidins and anthocyanins, are major components that contribute to the phenolic profile variation among cultivars.
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