食品科学 ›› 2017, Vol. 38 ›› Issue (22): 205-210.doi: 10.7506/spkx1002-6630-201722031

• 成分分析 • 上一篇    下一篇

甘薯水分和还原糖协同向量NIR快速检测方法

高丽,潘从飞,陈嘉,王勇德,赵国华,   

  1. (1.西南大学食品科学学院,重庆 400715;2.重庆市中药研究院,重庆 400065;3.重庆市特色食品工程技术研究中心,重庆 400715)
  • 出版日期:2017-11-25 发布日期:2017-11-03
  • 基金资助:
    重庆市社会事业与民生保障科技创新专项(cstc2015shms-ztzx80006); 重庆市特色食品工程技术研究中心能力提升项目(cstc2014pt-gc8001)

Rapid Determination of Moisture and Reducing Sugar in Sweet Potato by Near-Infrared Spectroscopy Coupled with Chemometrics

GAO Li, PAN Congfei, CHEN Jia, WANG Yongde, ZHAO Guohua,   

  1. (1. College of Food Science, Southwest University, Chongqing 400715, China; 2. Chongqing Academy of Chinese Materia Medica, Chongqing 400065, China; 3. Chongqing Engineering Research Center for Special Food, Chongqing 400715, China)
  • Online:2017-11-25 Published:2017-11-03

摘要: 利用近红外光谱技术建立新鲜甘薯水分和还原糖含量的预测模型,实现快速检测与分析,为甘薯品质分析和种质资源筛选提供便捷。选用不同品系甘薯样品146?份,109?份作为校正样品,37?份作为验证样品。运用不同光谱预处理方法、协同区间偏最小二乘最优波长选择法以及主成分回归和偏最小二乘法建立甘薯水分和还原糖模型。结果显示,所建甘薯水分(还原糖)最优模型的决定系数、预测均方根误差和标准差比率分别为0.974(0.885),1.154(0.270)和6.334(3.148)。表明2?种模型具有较好的检测性能,近红外光谱模型的预测值与其相应的化学值有较好的相关性,适用于大批量甘薯选育时水分和还原糖含量的快速检测。

关键词: 近红外光谱技术, 甘薯, 水分, 还原糖, 区间协同偏最小二乘法

Abstract: A predictive model for rapid quantification of moisture and reducing sugar in fresh sweet potato was established by near-infrared (NIR) spectroscopy, which could facilitate the quality analysis and germplasm screening of sweet potato. In this study, 146 samples of different lines of sweet potato were selected, out of which 109 samples were used as calibration samples while the rest were used as validation samples. After different spectral pretreatments and optimal wavelength selection by synergy interval partial least squares, a principal component regression model and a partial least squares model were developed for predicting the contents of moisture and reducing sugar in sweet potato, respectively. The results showed that the coefficient of determination, root mean square error of prediction, and standard deviation ratio of the optimal model for moisture and reducing sugar contents were 0.974 and 0.885, 1.154 and 0.270, and 6.334 and 3.148, respectively, indicating that the two models have good prediction performance, and the NIR model predicted values had a good correlation with the corresponding chemical values. Therefore, the predictive model is suitable for rapid quantification of water and reducing sugar in large-scale sweet potato breeding.

Key words: near-infrared spectroscopy, sweet potato, moisture, reducing sugar, synergy interval partial least squares

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