导读：研究紫外-可见吸收光谱技术结合化学计量学方法鉴别真假蜂蜜。

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

OU Wen-Juan1，MENG Yao-Yong* 1，ZHANG Xiao-Yan1，KONG Meng1

1( College of Biophotonics，South China Normal University，Guangzhou 510631)

Abstract 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 rootmean-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

Keywords Honey; Adulteration; Ultraviolet-Visible absorption spectroscopy; Back propagation artificial neural network; Principal components-back propagation artificial neural network

( Received 14 October 2010; accepted 18 January 2011)