Search

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. [ABSTRACT FROM AUTHOR]

Abstract: 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 (LDA), 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. [Copyright &y& Elsevier]