Characterization of Adulterated Milk by Two-Dimensional Infrared Correlation Spectroscopy

Yang, Ran; Zhang, W. Y.; Yang, Yong; Wu, Zhongchen; Dong, Guimei; Du, Yanhong

doi:10.1080/00032719.2014.915406

Cited by 17 publications

(2 citation statements)

References 24 publications

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“…For LS‐SVM model, it is important to choose appropriate model parameters for constructing a stable and accuracy classification model . In present study, a radial basis function (RBF) was selected as kernel function.…”

Section: Resultsmentioning

confidence: 99%

“…Chen et al discriminated successfully the four kinds of vegetable oil in 4800–4500 cm −1 by comparing the difference of correlation peaks information . Our groups developed a 2D correlation spectroscopy combined with multivariate calibration methods for the qualitation and quantification of adulterated food . Our proposed methods not only overcome the subjectivity of the comparison of correlation spectra, but also improve the efficiency of analysis for a large number of samples.…”

Section: Introductionmentioning

confidence: 99%

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Discrimination of sesame oil adulterated with corn oil using information fusion of synchronous and asynchronous two‐dimensional near‐mid infrared spectroscopy

Yang

Dong

Sun

et al. 2017

Euro J Lipid Sci & Tech

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A new and accurate approach for classifying sesame oil adulterated with corn oil using the information fusion of synchronous and asynchronous 2D NIR‐MIR correlation spectroscopy combined with least squares support vector machines (LS‐SVM). N‐way partial least square discriminant analysis (NPLS‐DA) was adopted to extract the principal components of synchronous and asynchronous 2D NIR‐MIR correlation matrices. And the score matrices of synchronous and asynchronous 2D NIR‐MIR correlation spectroscopy were emerged. Both separate and emerged score matrices were input LS‐SVM to construct discriminate models for adulterated sesame oil. The ratios of correct classification were 98.1 and 100% for calibration set and prediction set, respectively, based on information fusion combined with LS‐SVM. Comparison results showed that the LS‐SVM model could provide higher discriminant accuracy for adulterated sesame soil based on the information fusion of synchronous and asynchronous 2D NIR‐MIR correlation spectroscopy than both separate spectra information. Practical applications: The proposed method not only contains NIR and MIR spectral information, but also contains the similar and dissimilar variation spectral information with the perturbation. Thus, the method can provide better discriminate results of adulterated sesame oil in comparison with conventional spectral methods; can also be applied to other food safety detection areas. The information fusion of synchronous and asynchronous 2D NIR‐MIR correlation spectroscopy is proposed for classifying adulterated sesame oil.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%