Application of UV-Visible Absorption Spectroscopy and Principal Components-Back Propagation Artificial Neural Network to Identification of Authentic and Adulterated Honeys

UV-visible(UV-vis) absorption spectroscopy in combination with chemometrics was used to identify the authentic and adulterated honeys.Adulterant solution prepared using D-fructose and D-glucose following the mass ratio of typical of honey composition(1.2∶1.0) was close to the real honey and added to individual honeys at levels of 5%,10%,15% and 20%.Absorption spectra of authentic and adulterated honeys in the wavelength range of 220-750 nm were acquired.The absorbance values of the best-sensitive band(250-400 nm) were selected to build models.The optimal identification model was developed with principal component analysis in combination with back propagation artificial neural network(PCA-BP-ANN).The scores of optimal principal components were used as the input vectors of model.The output results showed that the correct identification rates were 100% for both the calibration and prediction sets and the corresponding root-mean-square errors were 8.523×10-3(RMSEC) and 8.961×10-3(RMSEP),respectively.The study demonstrates that UV-vis absorption spectroscopy based on PCA and BP-ANN can be used as a convenient,rapid and accurate technique for identification of authentic and adulterated honeys