Unsaturated fatty-acid based HPLC fingerprints in combination with quantitative analysis of multi-components by single-marker for the classification of Rana chensinensis ovum

A classification method of nutritional food Rana chensinensis ovum (RCO) was established on the basis of unsaturated fatty-acid (UFA) profiling and high-performance liquid chromatography (HPLC) fingerprints in combination with quantitative analysis of multi-components by single-marker (QAMS). The 19 batches of RCO samples were collected from the main breeding forests in northeast China. HPLC analytical method of UFAs in RCO was established by optimizing chromatographic conditions. The RCO samples from different forest regions were used to establish two kinds of HPLC fingerprints, which matched chromatographic peaks with the peak area ratio >0.5% and 7 identified UFAs. A total of 11 common peaks were obtained, of which 7 peaks were identified as eicosapentaenoic acid (EPA), α-linolenic acid (ALA), docosahexaenoic acid (DHA), arachidonic acid (ARA), docosapentaenoic acid (DPA), linoleic acid (LA) and oleic acid (OA). RCO samples were classified into three groups by hierarchical cluster analysis (HCA) and principal component analysis (PCA). Partial least squares discriminant analysis (PLS-DA) predicted differential components that may affect grade classification. The QAMS method established here showed good robustness and feasibility. ALA was designated as an internal reference, and the relative correction factors (RCFs) of the other six UFAs were calculated. Compared with the external standard method (ESM), there was no significant difference (P > 0.05, RE% between ±5.00% and cos θ > 0.9999) in the quantitative analysis of UFAs in RCO by QAMS, but the newly established method is more economical and timesaving. This work provides a comprehensive evaluation method for controlling the quality of RCO.