烟叶中六种成分OSC-PCR定量模型的研究  

Study on the Model of Six Components in Tobacco Using OSC-PCR

作　　者：吴丽君[1] 田旷达[1] 李倩倩[1] 李祖红[2] 邱凯贤[1] 闵顺耕[1] 

机构地区：[1]中国农业大学理学院应用化学系,北京100193 [2]云南省烟草公司曲靖市公司,云南曲靖655000

出　　处：《光谱学与光谱分析》2013年第6期1517-1520,共4页Spectroscopy and Spectral Analysis

基　　金：中国烟草总公司项目(2010YN65)资助

摘　　要：采用近红外光谱技术对烟草中常规化学成分总糖、还原糖、烟碱、总氮、淀粉和挥发性碱进行测定。利用正交信号校正法(OSC)对烟叶的近红外光谱进行预处理,再使用主成分回归方法(PCR)建立烟叶中六种化学成分的定量分析模型,采用蒙特卡洛交互验证作为集成的建模策略优化模型参数,使用外部预测的相对预测性能(RPD)评价模型。结果表明,OSC有效解决了PCR投影方向并非浓度相关性最大方向的问题,同时解决了噪声、基线漂移、杂散光等问题。OSC-PCR建立的模型能够有效检测烟草常规化学成分。该研究方法通过确定化学值的波动范围初步监控烟叶中常规成分的含量,对于烟草品质评价和控制质量稳定性以及烟草香气成分分析具有重要意义。In the present paper, the authors used NIR to determine routine chemical components, namely total sugar, reducing sugar, nicotine, total nitrogen, starch and volatile alkali. Orthogonal signal correction （OSC） was employed as spectral pretreatment and the principal component regression （PCR） models for 6 chemical components were established with Monte Carlo cross- validation modeling strategy. RPD value for each model was calculated to evaluate the methods. The orientation of PCR projection is the largest variance direction and has no relationship with the concentration. OSC can not only get rid of uninformative concentration but also solve the problem of noise, baseline drift and stray light. Compared with conventional PCR, OSC-PCR sustains the accuracy of the predicting model and improves the stability of the model significantly. It proves that NIR coupled with OSC-PCR method can be applied to the determination of routine chemical components, which is of great significance in eval- uation of tobacco quality and analysis of tobacco aroma components.

关 键 词：烟草 正交信号校正 主成分回归 近红外光谱 常规化学成分 

分 类 号：O657.3[理学—分析化学]