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基于脂肪酸谱和风味特征结合分类算法鉴别两个本地品种鸡蛋

Discriminating Eggs from Two Local Breeds Based on Fatty Acid Profile and Flavor Characteristics Combined with Classification Algorithms.

作者信息

Dong Xiao-Guang, Gao Li-Bing, Zhang Hai-Jun, Wang Jing, Qiu Kai, Qi Guang-Hai, Wu Shu-Geng

机构信息

Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, China.

出版信息

Food Sci Anim Resour. 2021 Nov;41(6):936-949. doi: 10.5851/kosfa.2021.e47. Epub 2021 Nov 1.

Abstract

This study discriminated fatty acid profile and flavor characteristics of Beijing You Chicken (BYC) as a precious local breed and Dwarf Beijing You Chicken (DBYC) eggs. Fatty acid profile and flavor characteristics were analyzed to identify differences between BYC and DBYC eggs. Four classification algorithms were used to build classification models. Arachidic acid, oleic acid (OA), eicosatrienoic acid, docosapentaenoic acid (DPA), hexadecenoic acid, monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), unsaturated fatty acids (UFA) and 35 volatile compounds had significant differences in fatty acids and volatile compounds by gas chromatography-mass spectrometry (GC-MS) (p<0.05). For fatty acid data, k-nearest neighbor (KNN) and support vector machine (SVM) got 91.7% classification accuracy. SPME-GC-MS data failed in classification models. For electronic nose data, classification accuracy of KNN, linear discriminant analysis (LDA), SVM and decision tree was all 100%. The overall results indicated that BYC and DBYC eggs could be discriminated based on electronic nose with suitable classification algorithms. This research compared the differentiation of the fatty acid profile and volatile compounds of various egg yolks. The results could be applied to evaluate egg nutrition and distinguish avian eggs.

摘要

本研究对作为珍贵地方品种的北京油鸡(BYC)和矮小型北京油鸡(DBYC)鸡蛋的脂肪酸谱和风味特征进行了区分。分析了脂肪酸谱和风味特征,以确定BYC和DBYC鸡蛋之间的差异。使用四种分类算法构建分类模型。通过气相色谱 - 质谱联用仪(GC - MS)分析发现,花生酸、油酸(OA)、二十碳三烯酸、二十二碳五烯酸(DPA)、十六碳烯酸、单不饱和脂肪酸(MUFA)、多不饱和脂肪酸(PUFA)、不饱和脂肪酸(UFA)和35种挥发性化合物在脂肪酸和挥发性化合物方面存在显著差异(p<0.05)。对于脂肪酸数据,k近邻(KNN)和支持向量机(SVM)的分类准确率达到91.7%。固相微萃取 - 气相色谱 - 质谱联用(SPME - GC - MS)数据在分类模型中效果不佳。对于电子鼻数据,KNN、线性判别分析(LDA)、SVM和决策树的分类准确率均为100%。总体结果表明,基于电子鼻和合适的分类算法可以区分BYC和DBYC鸡蛋。本研究比较了各种蛋黄脂肪酸谱和挥发性化合物的差异。研究结果可应用于评估鸡蛋营养和区分禽蛋。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98b2/8564318/de1445005f3d/kosfa-41-6-936-g1.jpg

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