Rapid GC-FID-based analysis of fatty acid content, nutritional indices, and quality assessment of Rana chensinensis ovum

Abstract

To assess the nutritional value of Rana chensinensis ovum (RCO), fatty acid fingerprinting using gas chromatography (GC) in conjunction with quantitative analysis of multiple components using a single marker (QAMS) was applied. Through analysis of the standard fingerprint of thirteen RCO samples from Northeast China, eleven common peaks were identified, including palmitic acid (C16:0), palmitoleic acid (C16:1), stearic acid (C18:0), oleic acid (OA, C18:1n9c), linoleic acid (LA, C18:2n6), α-linolenic acid (ALA, C18:3n3), paullinic acid (C20:1), eicosadienoic acid (C20:2), arachidonic acid (ARA, C20:4n6), eicosapentaenoic acid (EPA, C20:5n3) and docosahexaenoic acid (DHA, C22:6n3). In QAMS, methyl oleate served as the internal reference, and relative correction factors were calculated for the remaining ten components. Compared with internal standard method, this QAMS method is feasible (RSD < 4%, p > 0.05, cos θ > 0.9999) and is more advantageous in terms of speed and cost-effectiveness. The RCO samples were categorized into four groups using hierarchical cluster analysis (HCA) and principal component analysis (PCA). Additionally, partial least squares-discriminant analysis (PLS-DA) was used to identify four important categorical variables: ALA, C16:0, LA, and ARA. In this work, a useful framework for quality control is provided by the effective application of GC fingerprinting and QAMS in the qualitative and quantitative evaluation of RCO.