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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：电子鼻结合化学计量学方法对山核桃（山核桃）脂肪酸在贮藏过程中的定性和定量分析

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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 (R2 > 0.95 in calibration sets and R2 > 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 ）由于其高脂肪酸含量而在储存过程中持续恶化。在这项研究中，引入了电子鼻（E-nose）来表征具有不同储存时间的中国山核桃。化学计量学方法（主要成分分析（PCA），偏最小二乘回归（PLSR）和反向传播神经网络（BPNNs））用于分析电子鼻数据。为了进行定性分析，PCA可以根据电子鼻数据可视化不同山核桃之间的区别。对于定量分析，结果表明，BPNN模型在预测存储时间和脂肪酸含量方面均优于PLSR模型。此外，建立了多目标BPNN回归模型以同时预测六种主要脂肪酸的含量，并得出结果（ R 2 R 2

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