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Detection of adulterants such as sweeteners materials in honey using near-infrared spectroscopy and chemometrics

文献类型： 外文期刊

作者： Zhu, Xiangrong 1 ; Li, Shuifang 2 ; Shan, Yang 1 ; Zhang, Zhuoyong 3 ; Li, Gaoyang 1 ; Su, Donglin 1 ; Liu, Feng 1 ;

作者机构： 1.Hunan Acad Agr Sci, Hunan Agr Prod Proc Inst, Changsha 410125, Hunan, Peoples R China

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

3.Capital Normal Univ, Dept Chem, Beijing 100048, Peoples R China

关键词： NIR spectroscopy;detection;honey;chemometrics

期刊名称：JOURNAL OF FOOD ENGINEERING （ 影响因子：5.354； 五年影响因子：5.144 ）

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收录情况： SCI

摘要： Near-infrared (NIR) spectroscopy combined with chemometrics methods has been used to detect adulteration of honey samples. The sample set contained 135 spectra of authentic (n-68) and adulterated (n ? 67) honey samples. Spectral data were compressed using wavelet transformation (WT) and principal component analysis (PCA), respectively. In this paper, five classification modeling methods including least square support vector machine (LS-SVM), support vector machine (SVM), back propagation artificial neural network (BP-ANN), linear discriminant analysis (IDA), and K-nearest neighbors (KNN) were adopted to correctly classify pure and adulterated honey samples. WT proved more effective than PCA, as a means for variables selection. Best classification models were achieved with LS-SVM. A total accuracy of 95.1% and the area under the receiver operating characteristic curves (AUC) of 0.952 for test set were obtained by LS-SVM. The results showed that WT-LS-SVM can be as a rapid screening technique for detection of this type of honey adulteration with good accuracy and better generalization.

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