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首页> 外文期刊>RSC Advances >Qualitative and quantitative analysis of fatty acid profiles of Chinese pecans (Carya cathayensis) during storage using an electronic nose combined with chemometric methods
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Qualitative and quantitative analysis of fatty acid profiles of Chinese pecans (Carya cathayensis) during storage using an electronic nose combined with chemometric methods

机译：用电子鼻与化学计量方法相结合储存过程中山核桃（Carya Cathayensis）脂肪酸谱的定性和定量分析

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摘要

Chinese pecans (Carya cathayensis) continuously deteriorate during storage because of their high fatty acid contents. In this study, an electronic nose (E-nose) was introduced to characterize Chinese pecans with different storage times. Chemometric methods (principal component analysis (PCA), partial least squares regression (PLSR), and back propagation neural networks (BPNNs)) were employed to analyze E-nose data. For qualitative analysis, PCA could visualize the discrimination between different pecans based on the E-nose data. For quantitative analysis, the results indicated that BPNN models performed better both in predicting storage times and fatty acid contents than the PLSR models. In addition, a multi-target BPNN regression model was built to simultaneously predict the contents of the six main fatty acids, and the results (R-2 > 0.95 in calibration sets and R-2 > 0.88 in validation sets) were satisfactory. This study provides a potentially viable method for determining the storage times and fatty acid profiles of nut products.
机译：中国山核桃（Carya Cathayensis）由于其高脂肪酸内容物而在储存期间不断恶化。在这项研究中，引入了一种电子鼻子（E-鼻子），以表征具有不同存储时间的中国山核桃。采用化学计量方法（主成分分析（PCA），偏最小二乘回归（PLSR）和反向传播神经网络（BPNNS））来分析电子鼻数据。对于定性分析，PCA可以根据电子鼻数据可视化不同磷油之间的歧视。为了定量分析，结果表明BPNN模型在预测存储时间和脂肪酸内容中的比PLSR模型更好。此外，建立了多目标BPNN回归模型以同时预测六个主要脂肪酸的内容，结果（校准组中的r-2> 0.95和验证集中的R-2> 0.88）是令人满意的。该研究提供了一种用于确定螺母产品的储存时间和脂肪酸型材的潜在可行的方法。

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  • 来源
    《RSC Advances》 |2017年第73期|共11页
  • 作者

    Jiang Shui; Wang Jun; Sun Yubing;

  • 作者单位

    Zhejiang Univ Dept Biosyst Engn 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China;

    Zhejiang Univ Dept Biosyst Engn 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China;

    Zhejiang Univ Dept Biosyst Engn 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 化学;
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