食品科学 ›› 2023, Vol. 44 ›› Issue (10): 368-376.doi: 10.7506/spkx1002-6630-20220802-013

• 安全检测 • 上一篇    

基于GC-IMS结合化学计量学鉴别大鲵油掺伪不同比例花生油挥发性有机物特征

金文刚,刘俊霞,孙海燕,何琳琳,裴金金,程虎,姜鹏飞   

  1. (1.陕西理工大学生物科学与工程学院,陕西省资源生物重点实验室,陕西 汉中 723001;2.陕西理工大学 陕南秦巴山区生物资源综合开发协同创新中心,陕西 汉中 723001;3.汉中市龙头山水产养殖开发有限公司,陕西 汉中 723001;4.大连工业大学食品学院,国家海洋食品工程技术研究中心,辽宁 大连 116034)
  • 出版日期:2023-05-25 发布日期:2023-06-02
  • 基金资助:
    国家留学基金委访问学者项目(202008610071);陕西省科技厅项目(2015SZS-15-01); 秦巴生物资源综合利用协同创新中心项目(QBXT-18-4);陕西理工大学重点科研项目(SLG2106)

Characterization of Volatile Organic Compounds of Giant Salamander (Andrias davidianus) Oil Adulterated with Different Amounts of Peanut Oil by Gas Chromatography-Ion Mobility Spectrometry Combined with Chemometrics

JIN Wengang, LIU Junxia, SUN Haiyan, HE Linlin, PEI Jinjin, CHENG Hu, JIANG Pengfei   

  1. (1. Shaanxi Key Laboratory Bio-resources, School of Biological Science and Engineering, Shaanxi University of Technology, Hanzhong 723001, China; 2. Collaborative Innovation Center for Comprehensive Development of Bio-Resources in Qinba Mountain Area of Southern Shaanxi, Shaanxi University of Technology, Hanzhong 723001, China; 3. Hanzhong Dragon Mountain Aquaculture Development Co. Ltd., Hanzhong 723001, China; 4. National Engineering Research Center for Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China)
  • Online:2023-05-25 Published:2023-06-02

摘要: 采用气相色谱-离子迁移色谱(gas chromatography-ion mobility spectroscopy,GC-IMS)结合化学计量学对不同花生油掺伪量(0%、5%、10%、20%、30%、100%)的大鲵油中挥发性成分进行探究。结果表明,不同掺伪量大鲵油GC-IMS图谱中共鉴定出41 种挥发性化合物,其中醛类21 种、酮类6 种、醇类4 种、杂环类4 种、酯类3 种、含硫类2 种、酸类1 种。随着掺伪量的增加,醛类、杂环类、酸类和酯类化合物含量增加,而酮类、醇类和含硫类化合物含量减少。建立了挥发性成分与实际掺伪量偏最小二乘回归模型,校正集和验证集相关系数R2值分别为0.992 4和0.988 2,表明建立的掺伪量预测模型较为可靠。主成分分析表明,前2 个主成分累计贡献率为94.3%,说明不同掺伪量大鲵油可通过挥发性成分实现较好区分。通过偏最小二乘判别分析并结合变量投影重要度(variable importance for the projection,VIP)筛选出13 种差异挥发性化合物(VIP>1),其中醛类7 种、酮类3 种、醇类1 种、含硫类1 种和及酯类1 种。主成分和聚类分析结果表明这些差异挥发性成分也可用于不同掺伪量大鲵油的区分。该研究为无损、快速鉴别大鲵油真实性提供了参考方法。

关键词: 大鲵油;掺伪;花生油;挥发性成分;气相色谱-离子迁移色谱;化学计量学

Abstract: The volatile components in giant salamander oil adulterated with different amounts of peanut oil (0%, 5%, 10%, 20%, 30%, and 100%) were studied by gas chromatography-ion mobility spectroscopy (GC-IMS) combined with chemometrics. The results showed that a total of 41 volatile compounds were identified in all samples, including 21 aldehydes, 6 ketones, 4 alcohols, 4 heterocyclic compounds, 3 esters, 2 sulfur-containing compounds and 1 acid. With increasing adulteration level, the contents of aldehydes, heterocycles, acids and esters increased, while the contents of ketones, alcohols and sulfur compounds decreased. A partial least squares regression (PLSR) model between volatile components and adulteration level was established. The correlation coefficient (R2) values for the calibration and verification sets were 0.992 4 and 0.988 2, respectively, indicating that the reliability of the model. Principal component analysis (PCA) showed that the cumulative contribution rate of the first two principal components (PC) was 94.3%, indicating that the different adulteration levels could be well distinguished by volatile components. Thirteen differential volatile compounds with variable importance for the projection (VIP) scores greater than one, including seven aldehydes, three ketones, one alcohol, one sulfur compound and one ester, were selected by partial least squares-discriminant analysis (PLS-DA). PCA and cluster analysis showed that these differential volatile components could also be used to distinguish the different adulterated salamander oil samples. This study can provide technical support for the nondestructive rapid identification of adulterated giant salamander oil.

Key words: giant salamander oil; adulteration; peanut oil; volatile components; gas chromatography-ion mobility spectroscopy; chemometrics

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