共 36 条
FTIR Spectroscopy and Chemometric Class Modeling Techniques for Authentication of Chinese Sesame Oil
被引:41
作者:
Deng, De-Hua
[1
,2
]
Xu, Lu
[1
]
Ye, Zi-Hong
[1
]
Cui, Hai-Feng
[1
]
Cai, Chen-Bo
[3
]
Yu, Xiao-Ping
[1
]
机构:
[1] China Jiliang Univ, Coll Life Sci, Hangzhou 310018, Peoples R China
[2] Anyang Normal Univ, Coll Chem & Chem Engn, Anyang 455002, Peoples R China
[3] Chuxiong Normal Univ, Dept Chem & Life Sci, Chuxiong 675000, Peoples R China
关键词:
Sesame oil;
FTIR;
Class modeling techniques;
Soft independent modeling of class analogy;
Partial least squares class model;
FATTY-ACID-COMPOSITION;
LEAST-SQUARES METHODS;
OXIDATIVE STABILITY;
INDICUM L;
SEED OIL;
MIDINFRARED SPECTROSCOPY;
SPECTRAL ANALYSES;
QUALITY;
IDENTIFICATION;
VALIDATION;
D O I:
10.1007/s11746-011-2004-8
中图分类号:
O69 [应用化学];
学科分类号:
081704 ;
摘要:
This investigation was aimed at developing a rapid analysis method for authentication of Chinese sesame oils by FTIR spectrometry and chemometrics. Ninety-five sesame oil samples were collected from the six main producing areas of China to include most if not all of the significant spectral variations likely to be encountered in future authentic materials. Two class modeling techniques, the soft independent modeling of class analogy (SIMCA) and the partial least squares class model (PLSCM) were investigated and the data preprocessing techniques including smoothing, derivative and standard normal variate (SNV) tests were performed to improve the classification performance. It was demonstrated that SIMCA and PLSCM can detect various adulterated sesame oils doped with 3% or more (w/w) of other cheaper oils, including rapeseed, soybean, palm and peanut oils. First derivative, second derivative and SNV tests significantly enhanced the class models by reducing baseline and background shifts. Smoothing of raw spectra led to inferior identification performance and proved itself to be unsuitable because some of the detailed frequency details were lost during smoothing. The best model performance was obtained with second derivative spectra by SIMCA (sensitivity 0.905 and specificity 0.944) and PLSCM (sensitivity 0.952 and specificity 0.937). Although it is difficult to perform an exhaustive sampling of all types of pure sesame oils and potential adulterations, PLS and SIMCA combined with FTIR spectrometry can detect most of current adulterations of sesame oils on the Chinese market.
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页码:1003 / 1009
页数:7
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