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Difference Analysis and Optimization Study for Determination of Fructose and Glucose by Near Infrared Spectroscopy

文献类型： 外文期刊

作者： Tu Zhen-Hua 1 ; Zhu Da-Zhou 2 ; Ji Bao-Ping 1 ; Meng Chao-Ying 3 ; Wang Lin-Ge 1 ; Qing Zhao-Shen 1 ;

作者机构： 1.China Agr Univ, Coll Food Sci & Nutr Engn, Beijing 100083, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

关键词： Honey; Near infrared spectrometry; Fructose; Glucose; Characteristic wavelengths

期刊名称：CHINESE JOURNAL OF ANALYTICAL CHEMISTRY （ 影响因子：1.134； 五年影响因子：0.909 ）

ISSN： 0253-3820

年卷期： 2010 年 38 卷 1 期

页码：

收录情况： SCI

摘要： A total of 101 honey samples that originated from 20 different unifloral honey and other multifloral honey samples were collected from China. FT-NIR spectrometer were applied to determinate the content of fructose and glucose of honey with two different modes: transflectance (800 - 2500 nm, 2 mm optical path length) and transmittance (800 - 1370 nm, 20 mm optical path length). It was found that the prediction accuracy of fructose and glucose had significant difference with the two modes. In order to analyze the reason of this difference, support vector machine (SVM) was used to analyze the non-linear information, and genetic algorithm (CA) was used to analyze the characteristic wavelengths. The result indicated that the detection difference of fructose and glucose was originated from their different characteristic wavelengths. Through the optimization of detection method, it was found that for the determination of glucose, short wavelength and long optical path length should be used, on the other side, the whole wavelength region and short wavelength, with selecting the characteristic wavelength to avoid the disturb of water can also be used. For the determination of fructose, whole wavelength region and short optical path length should be used. Linear regression methods such as PLSR could obtain good results, and non-linear methods such as SVM did not improve the model performance.

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