Detection of adulterants such as sweeteners materials in honey using near-infrared spectroscopy and chemometrics
暂无分享,去创建一个
Zhuoyong Zhang | Gaoyang Li | Feng Liu | F. Liu | Zhuoyong Zhang | Gao-yang Li | Yang Shan | Shui-fang Li | Dong-lin Su | Yang Shan | Shuifang Li | Xiangrong Zhu | Donglin Su | Xiangrong Zhu | Feng Liu
[1] Johan A. K. Suykens,et al. Least Squares Support Vector Machines , 2002 .
[2] D. Cabrol-Bass,et al. Honey characterization and adulteration detection by pattern recognition applied on HPAEC-PAD profiles. 1. Honey floral species characterization. , 2003, Journal of agricultural and food chemistry.
[3] D. Massart,et al. Application of wavelet transform to extract the relevant component from spectral data for multivariate calibration. , 1997, Analytical chemistry.
[4] Yong He,et al. Discriminating varieties of tea plant based on Vis/NIR spectral characteristics and using artificial neural networks , 2008 .
[5] Keinosuke Fukunaga,et al. Introduction to Statistical Pattern Recognition , 1972 .
[6] J. Irudayaraj,et al. Rapid Determination of Invert Cane Sugar Adulteration in Honey Using FTIR Spectroscopy and Multivariate Analysis , 2003 .
[7] Jordi Coello,et al. NIR calibration in non-linear systems: different PLS approaches and artificial neural networks , 2000 .
[8] Dusan Stulik,et al. Simple encoding of infrared spectra for pattern recognition Part 2. Neural network approach using back-propagation and associative Hopfield memory , 1995 .
[9] Mauro Bacci,et al. Principal component analysis of near-infrared spectra of alteration products in calcareous samples: An application to works of art , 1997 .
[10] Roberto Muñoz,et al. Multivariate calibration model from overlapping voltammetric signals employing wavelet neural networks , 2006 .
[11] Mohamed Cheriet,et al. Model selection for the LS-SVM. Application to handwriting recognition , 2009, Pattern Recognit..
[12] Desire L. Massart,et al. Feature selection in principal component analysis of analytical data , 2002 .
[13] P. Hopke,et al. Comparison of rule-building expert systems with pattern recognition for the classification of analytical data , 1987 .
[14] Davide Bertelli,et al. Classification of Italian honeys by mid-infrared diffuse reflectance spectroscopy (DRIFTS) , 2007 .
[15] Ana I Cabañero,et al. Liquid chromatography coupled to isotope ratio mass spectrometry: a new perspective on honey adulteration detection. , 2006, Journal of agricultural and food chemistry.
[16] M. Grenier-loustalot,et al. Study and validity of 13C stable carbon isotopic ratio analysis by mass spectrometry and 2H site-specific natural isotopic fractionation by nuclear magnetic resonance isotopic measurements to characterize and control the authenticity of honey. , 2007, Analytica chimica acta.
[17] Gerard Downey,et al. Potential of near Infrared Transflectance Spectroscopy to Detect Adulteration of Irish Honey by Beet Invert Syrup and High Fructose Corn Syrup , 2006 .
[18] Daniel Cabrol-Bass,et al. Detection and quantification of honey adulteration via direct incorporation of sugar syrups or bee-feeding: preliminary study using high-performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD) and chemometrics , 2005 .
[19] C. Biliaderis,et al. Composition, thermal and rheological behaviour of selected Greek honeys , 2004 .
[20] T. B. Murphy,et al. A comparison of model-based and regression classification techniques applied to near infrared spectroscopic data in food authentication studies , 2007 .
[21] Nieves Corzo,et al. HPAEC-PAD oligosaccharide analysis to detect adulterations of honey with sugar syrups , 2008 .
[22] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[23] G. Osorio-Revilla,et al. Application of FTIR-HATR spectroscopy and multivariate analysis to the quantification of adulterants in Mexican honeys. , 2009 .
[24] David H. Burns,et al. Parsimonious calibration models for near-infrared spectroscopy using wavelets and scaling functions , 2006 .
[25] F Despagne,et al. Neural networks in multivariate calibration. , 1998, The Analyst.
[26] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[27] Joseph Maria Kumar Irudayaraj,et al. Classification of simple and complex sugar adulterants in honey by mid-infrared spectroscopy , 2002 .
[28] Johan A. K. Suykens,et al. Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.
[29] N. Sbirrazzuoli,et al. Application of DSC as a tool for honey floral species characterization and adulteration detection , 2003 .
[30] Yi-Zeng Liang,et al. A non‐linear mapping‐based generalized backpropagation network for unsupervised learning , 1996 .
[31] Gerard Downey,et al. Application of Fourier transform midinfrared spectroscopy to the discrimination between Irish artisanal honey and such honey adulterated with various sugar syrups. , 2006, Journal of agricultural and food chemistry.
[32] L. A. Stone,et al. Computer Aided Design of Experiments , 1969 .
[33] Gerard Downey,et al. Initial study of honey adulteration by sugar solutions using midinfrared (MIR) spectroscopy and chemometrics. , 2004, Journal of agricultural and food chemistry.
[34] Joseph Maria Kumar Irudayaraj,et al. Detection of inverted beet sugar adulteration of honey by FTIR spectroscopy , 2001 .
[35] David De Jong,et al. Detection of adulteration of commercial honey samples by the 13C/12C isotopic ratio , 2003 .