Rapid identification of edible oil species using supervised support vector machine based on low-field nuclear magnetic resonance relaxation features
Rapid identification of edible oil species using supervised support vector machine based on low-field nuclear magnetic resonance relaxation features
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DOI：
10.1016/j.foodchem.2018.12.031
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发表时间：
2019-05-15
期刊：
影响因子：
8.8
通讯作者：
Nie, Shengdong
中科院分区：
文献类型：
--
作者：
Hou, Xuewen;Wang, Guangli;Nie, Shengdong
为了快速识别食用油的植物来源，提出了一种基于低场核磁共振和松弛特征的监督支持向量机识别食用油的新方法。采集了11种食用油的低场核磁共振信号，从食用油的横向弛豫衰减曲线中提取了5个特征，并利用支持向量机对食用油进行了识别。应用并讨论了两种支持向量机分类策略。在设计支持向量机模型的二叉树结构之前，通过主成分分析确定各食用油的相对位置，分类效果较好，分类正确率为99.04%。在不同的数据集上验证了该方法的良好鲁棒性。这几乎是一种实时的方法，整个过程只需要S 144次。
Aimed to rapidly identify the edible oils according to their botanical origin, a novel method was proposed using supervised support vector machine based on low-field nuclear magnetic resonance and relaxation features. The low-field (LF) nuclear magnetic resonance (NMR) signals of 11 types of edible oils were acquired, and 5 features were extracted from the transverse relaxation decay curves and modeled using support vector machines (SVM) for the identification of edible oils. Two SVM classification strategies have been applied and discussed. Good performance can be achieved when the relative position of each edible oil has been determined by PCA before the designing of binary tree structure of SVM model, and the classification accuracy is 99.04%. The good robustness of this method has been verify at different data sets. It is almost a real time method, and the entire process takes only 144 s.