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ABSTRACT: 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.
Journal of Food Science 03/2012; 77(4):C374-80. · 1.66 Impact Factor
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ABSTRACT: A new method for the analysis of soluble solids content (SSC) in honey by near infrared spectroscopy (NIR) was developed, and moisture was also analyzed. The partial least square regression models of SSC and moisture were built for different pretreatments of the raw spectra in different spectral range. Good predictions were always obtained for all models. The best models of SSC and moisture were obtained by using Norris (3,2) smoothing + first derivative + multiplicative signal correction in total spectral range. The coefficient of determination (R(CV)2) and root mean square error of cross validation (RMSECV), the coefficient of determination (R(p)2) and root mean square error of validation sets (RMSEP) were 0.9986, 0.190, 0.9985 and 0.127 respectively for SSC, while for moisture they were 0.9984, 0.187, 0.9986 and 0.125 respectively. NIR could be used to analyze SSC and moisture in honey. The result of this article was better than that of related documents for moisture.
Guang pu xue yu guang pu fen xi = Guang pu 09/2010; 30(9):2377-80. · 0.84 Impact Factor
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ABSTRACT: Near-infrared spectroscopy (NIRS) combined with least squares support vector machines (LS-SVM) was used to establish a new method for the determination of the hesperidin content in guogongjiu medicinal wine. Firstly, training set was partitioned by Kernard-Stone (KS) algorithm. Secondly, spectral pretreatment methods were discussed in detail, comparing smoothing, rangescaling, autoscaling, first derivative, second derivative, along with those methods combined. Smoothing, first derivative and rangescaling were used for the pretreatment of the NIR spectra of guogongjiu medicinal wine. Thirdly, the effective interval was selected for 8211-8312 and 9712-9808 cm(-1) by synergy interval partial least squares (siPLS). Finally, the model was established by LS-SVM, the root mean square error of cross validation (RMSECV) is 0.001, root mean square error of prediction (RMSEP) is 0.004, and relative deviation of predicting set is less than 5%. It was compared with siPLS, radial basis function neural network (RBF-NN), and SVM, The result shows that the method is rapid, non-destructive, and credible. It is an effective measurement for determining the hesperidin content in guogongjiu medicinal wine.
Guang pu xue yu guang pu fen xi = Guang pu 09/2009; 29(9):2471-4. · 0.84 Impact Factor
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ABSTRACT: A method for the quantification of density of Chinese Fir samples based on visible/near-infrared (vis-NIR) spectrometry and least squares-support vector machine (LS-SVM) was proposed. Sample set partitioning based on joint x-y distances (SPXY) algorithm was used for dividing calibration and prediction samples, it is of value for prediction of property involving complex matrices. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. For comparison, the models were also constructed by Kennard-Stone method, as well as by using the duplex and random sampling methods for subset partitioning. The results revealed that the SPXY algorithm may be an advantageous alternative to the other three strategies. To validate the reliability of LS-SVM, comparisons were made among other modeling methods such as support vector machine (SVM) and partial least squares (PLS) regression. Satisfactory models were built using LS-SVM, with lower prediction errors and superior performance in relation to SVM and PLS. These results showed possibility of building robust models to quantify the density of Chinese Fir using near-infrared spectroscopy and LS-SVM combined SPXY algorithm as a nonlinear multivariate calibration procedure.
Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy 07/2009; 74(2):344-8. · 2.10 Impact Factor
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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.
Journal of Food Engineering.
