Sugar Adulteration (sugar + adulteration)

Distribution by Scientific Domains


Selected Abstracts


Rapid Determination of Invert Cane Sugar Adulteration in Honey Using FTIR Spectroscopy and Multivariate Analysis

JOURNAL OF FOOD SCIENCE, Issue 6 2003
J. Irudayaraj
ABSTRACT: Fourier transform infrared spectroscopy with an attenuated total reflection sampling accessory was combined with multivariate analysis to determine the level (1% to 25%, wt/wt) of invert cane sugar adulteration in honey. On the basis of the spectral data compression by principal component analysis and partial least squares, linear discriminant analysis (LDA), and canonical variate analysis (CVA), models were developed and validated. Two types of artificial neural networks were applied: a quick back propagation network (BPN) and a radial basis function network (RBFN). The prediction success rates were better with LDA (93.75% for validation set) and BPN (93.75%) than with CVA (87.50%) and RBFN (81.25%). [source]


Detection of inverted beet sugar adulteration of honey by FTIR spectroscopy

JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, Issue 8 2001
S Sivakesava
Abstract A combination of Fourier transform infrared (FTIR) spectroscopy and multivariate statistics as a screening tool for the determination of beet medium invert sugar adulteration in three different varieties of honey is discussed. Honey samples with different concentrations of beet invert sugar were scanned using the attenuated total reflectance (ATR) accessory of the Bio-Rad FTS-6000 Fourier transform spectrometer. The spectral wavenumber region between 950 and 1500,cm,1 was selected for partial least squares (PLS) regression to develop calibration models for beet invert sugar determination in honey samples. Results from the PLS (first derivative) models were slightly better than those obtained with other calibration models. Predictive models were also developed to classify beet sugar invert in three different varieties of honey samples using discriminant analysis. Spectral data were compressed using the principal component method, and linear discriminant and canonical variate analyses were used to detect the level of beet invert sugar in honey samples. The best predictive model for adulterated honey samples was achieved with canonical variate analysis, which successfully classified 88,94 per cent of the validation set. The present study demonstrated that Fourier transform infrared spectroscopy could be used for rapid detection of beet invert sugar adulteration in different varieties of honey. 2001 Society of Chemical Industry [source]