食品科学 ›› 2009, Vol. 30 ›› Issue (8): 147-150.doi: 10.7506/spkx1002-6630-200908029

• 分析检测 • 上一篇    下一篇

蜂蜜中还原糖组分的近红外光谱应用研究

陈兰珍1,2,薛晓锋1,陈 芳1,赵 静1,叶志华2,*,钟艳萍3   

  1. 1.中国农业科学院蜜蜂研究所 2.中国农业科学院农业质量标准与检测技术研究所 3.中国农业大学食品科学与营养工程学院
  • 收稿日期:2008-04-22 修回日期:2008-07-09 出版日期:2009-04-15 发布日期:2010-12-29
  • 通讯作者: 叶志华2,*, E-mail: zhihuaye@mail.caas.net.cn
  • 基金资助:

    “十一五”国家科技支撑计划项目(2006BAD06B04);中国农业科学院院级基本业务费专项项目

Prediction Analysis of Reducing Sugar Content in Honey Using Fourier Transform Near-infrared Spectroscopy

CHEN Lan-zhen1,2 XUE Xiao-feng1 CHEN Fang1 ZHAO Jing1 YE Zhi-hua2,* ZHONG Yan-ping3   

  1. (1. Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China
    2. Institute of Quality Standard and Testing Technology for Agro-products, Chinese Academy of Agricultural Sciences, Beijing
    100081, China 3. College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China)
  • Received:2008-04-22 Revised:2008-07-09 Online:2009-04-15 Published:2010-12-29
  • Contact: YE Zhi-hua2,* E-mail: zhihuaye@mail.caas.net.cn

摘要:

为探索快速测定还原糖含量的方法,提出了用傅立叶变换近红外光谱技术结合偏最小二乘法(PLS)建立近红外光谱与蜂蜜还原糖含量的数学模型并进行预测。通过光谱扫描还原糖含量在61.3%~75.22%范围的蜂蜜样本,选择11992.1~7494.6cm-1波数范围、二阶导数、及10个因子数进行光谱预处理,偏最小二乘法(PLS)交叉验证。结果表明,模型的校正决定系数(Rcal)、校正均方差(RMSEE)、交叉验证决定系数(RCV)、交叉验证均方差(RMSECV)分别为99.71%、0.27%、98.44%、0.45%。用该模型对验证集样本进行预测并统计分析,表明预测值与测定值无显著差异。因此,用该方法快速准确定量分析大批蜂蜜中的还原糖含量具有重要意义。

关键词: 蜂蜜, 还原糖, 傅立叶变换近红外光谱法, 偏最小二乘法

Abstract:

For exploring a rapid determination of reducing sugars, a correlation model of near-infrared spectrum and reducing sugar content in honey was established and the reducing sugar contents in honey samples were predicted by using Fourier transform near-infrared spectroscopy (FT-NIR) combined with partial least squares (PLS). The spectra of honey sample with reducing sugars content between 61.3% and 75.22% were scanned and the 11992.1 cm-1 to 7494.6 cm-1 spectrum was selected. Through preprocess of second derivative and 10 factors, the model was established by using PLS. The results of cross validation showed that the model of the calibration coefficient of determination (Rcal), root mean standard error of estimation (RMSEE), validation coefficient of determination (RCV), root mean standard error cross validation (RMSECV) are 99.71 %, 0.27 %, 98.44 % and 0.45 %, respectively. The model was used to verify samples and the statistical results showed that there is no significant difference between the predictive and chemical values. Altogether, this method is fast and accurate for quantitative analysis of reducing sugars content in mass honey samples.

Key words: honey, reducing sugars, Fourier transform near-infrared spectroscopy, partial least squares

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