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FTIR Spectroscopy and Chemometric Class Modeling Techniques for Authentication of Chinese Sesame Oil
by
Ye, Zi-Hong
, Yu, Xiao-Ping
, Cai, Chen-Bo
, Xu, Lu
, Deng, De-Hua
, Cui, Hai-Feng
in
Agriculture
/ Biological and medical sciences
/ Biomaterials
/ Biotechnology
/ Chemistry
/ Chemistry and Materials Science
/ chemometrics
/ China
/ Class modeling techniques
/ Fat industries
/ Food industries
/ Food Science
/ Fourier transform infrared spectroscopy
/ FTIR
/ Fundamental and applied biological sciences. Psychology
/ Industrial Chemistry/Chemical Engineering
/ least squares
/ markets
/ Mathematical models
/ model validation
/ Original Paper
/ Partial least squares class model
/ Peanut oil
/ peanuts
/ Product quality
/ rapeseed
/ Sesame oil
/ Soft independent modeling of class analogy
/ Soybeans
/ Spectrometry
/ Spectrum analysis
/ Vegetable oils
2012
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FTIR Spectroscopy and Chemometric Class Modeling Techniques for Authentication of Chinese Sesame Oil
by
Ye, Zi-Hong
, Yu, Xiao-Ping
, Cai, Chen-Bo
, Xu, Lu
, Deng, De-Hua
, Cui, Hai-Feng
in
Agriculture
/ Biological and medical sciences
/ Biomaterials
/ Biotechnology
/ Chemistry
/ Chemistry and Materials Science
/ chemometrics
/ China
/ Class modeling techniques
/ Fat industries
/ Food industries
/ Food Science
/ Fourier transform infrared spectroscopy
/ FTIR
/ Fundamental and applied biological sciences. Psychology
/ Industrial Chemistry/Chemical Engineering
/ least squares
/ markets
/ Mathematical models
/ model validation
/ Original Paper
/ Partial least squares class model
/ Peanut oil
/ peanuts
/ Product quality
/ rapeseed
/ Sesame oil
/ Soft independent modeling of class analogy
/ Soybeans
/ Spectrometry
/ Spectrum analysis
/ Vegetable oils
2012
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FTIR Spectroscopy and Chemometric Class Modeling Techniques for Authentication of Chinese Sesame Oil
by
Ye, Zi-Hong
, Yu, Xiao-Ping
, Cai, Chen-Bo
, Xu, Lu
, Deng, De-Hua
, Cui, Hai-Feng
in
Agriculture
/ Biological and medical sciences
/ Biomaterials
/ Biotechnology
/ Chemistry
/ Chemistry and Materials Science
/ chemometrics
/ China
/ Class modeling techniques
/ Fat industries
/ Food industries
/ Food Science
/ Fourier transform infrared spectroscopy
/ FTIR
/ Fundamental and applied biological sciences. Psychology
/ Industrial Chemistry/Chemical Engineering
/ least squares
/ markets
/ Mathematical models
/ model validation
/ Original Paper
/ Partial least squares class model
/ Peanut oil
/ peanuts
/ Product quality
/ rapeseed
/ Sesame oil
/ Soft independent modeling of class analogy
/ Soybeans
/ Spectrometry
/ Spectrum analysis
/ Vegetable oils
2012
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FTIR Spectroscopy and Chemometric Class Modeling Techniques for Authentication of Chinese Sesame Oil
Journal Article
FTIR Spectroscopy and Chemometric Class Modeling Techniques for Authentication of Chinese Sesame Oil
2012
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Overview
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.
Publisher
Springer-Verlag,Springer,Springer Nature B.V
Subject
/ Biological and medical sciences
/ Chemistry and Materials Science
/ China
/ Fourier transform infrared spectroscopy
/ FTIR
/ Fundamental and applied biological sciences. Psychology
/ Industrial Chemistry/Chemical Engineering
/ markets
/ Partial least squares class model
/ peanuts
/ rapeseed
/ Soft independent modeling of class analogy
/ Soybeans
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