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Authentication of Pure Camellia Oil by Using Near Infrared Spectroscopy and Pattern Recognition Techniques

文献类型： 外文期刊

作者： Li, Shuifang 2 ; Zhu, Xiangrong 1 ; Zhang, Juhua 1 ; Li, Gaoyang 1 ; Su, Donglin 1 ; Shan, Yang 1 ;

作者机构： 1.Hunan Acad Agr Sci, Hunan Food Test & Anal Ctr, Changsha 410125, Hunan, Peoples R China

2.Cent S Univ Forestry & Technol, Coll Sci, Changsha 410004, Hunan, Peoples R China

关键词： authentication;NIR spectroscopy;pattern recognition;pure camellia oil

期刊名称：JOURNAL OF FOOD SCIENCE （ 影响因子：3.167； 五年影响因子：3.376 ）

ISSN： 0022-1147

年卷期： 2012 年 77 卷 4 期

页码：

收录情况： SCI

摘要： Total of 4 pattern recognition methods for the authentication of pure camellia oil applying near infrared (NIR) spectroscopy were evaluated in this study. Total of 115 samples were collected and their authenticities were confirmed by gas chromatography (GC) in according to China Natl. Standard (GB). A preliminary study of NIR spectral data was analyzed by unsupervised methods including principal component analysis (PCA) and hierarchical cluster analysis (HCA). Total of 2 supervised classification techniques based on discriminant analysis (DA) and radical basis function neural network (RBFNN) were utilized to build calibration model and predict unknown samples. In the wavenumber range of 6000 to 5750 cm-1, correct classification rate of both supervised and unsupervised solutions all can reach 98.3% when smoothing, first derivative, and autoscaling were used. The good performance showed that NIR spectroscopy with multivariate calibration models could be successfully used as a rapid, simple, and nondestructive method to discriminate pure camellia oil.

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