Rapid identification of edible oil species using supervised support vector machine based on low-field nuclear magnetic resonance relaxation features
X Hou, G Wang, G Su, X Wang, S Nie - Food chemistry, 2019 - Elsevier
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 …
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 …
[引用][C] Rapid identification of edible oil species using supervised support vector machine based on low-field nuclear magnetic resonance relaxation features.
HXW Hou XueWen, WGL Wang GuangLi… - 2019 - cabidigitallibrary.org
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 …
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 …
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