甘薯水分和还原糖协同向量NIR快速检测方法

发布时间：2019-05-19 17:59

【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。
[Abstract]:The prediction model of moisture and reducing sugar content of fresh sweet potato was established by near infrared spectroscopy (NIR), and the rapid detection and analysis were realized, which provided convenience for quality analysis of sweet potato and selection of germplasm resources. 146 sweet potato samples of different strains, 109 samples as correction samples and 37 samples as verification samples were selected. The moisture and reducing sugar models of sweet potato were established by using different spectral pretreatment methods, cooperative interval partial least square optimal wavelength selection method, principal component regression and partial least square method. The results showed that the root mean square error and standard deviation ratio of the optimal model of sweet potato moisture (reducing sugar) were 0.974 (0.885), 1.154 (0.270) and 6.334 (3.148), respectively. The results show that the two models have good detection performance, and the predicted value of NIR model has a good correlation with their corresponding chemical values, which is suitable for rapid detection of moisture and reducing sugar content in mass sweet potato breeding.
【作者单位】：西南大学食品科学学院;重庆市中药研究院;重庆市特色食品工程技术研究中心;
【基金】：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)
【分类号】：O657.33;TS215

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