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<p>To assess the nutritional value of <i>Rana chensinensis</i> 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 &lt; 4%, <i>p</i> &gt; 0.05, cos θ &gt; 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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年2月12日

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  • Miao Chen,
  • Yuezhen Wang,
  • Yue Zhou,
  • Kexin Zhang,
  • Shihan Wang,
  • Changli Zhang,
  • Min Gao,
  • Zhihan Wang,
  • Yongsheng Wang

摘要

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.