烟叶总氮的近红外光谱检测模型研究  

Study on NIR Spectral Detection Model of Tobacco Total Nitrogen Content

作　　者：凌智钢[1] 唐延林[1] 李玉鹏[1] 李涛[1] 魏晓楠[1] 

机构地区：[1]贵州大学物理系,贵阳550025

出　　处：《中国农学通报》2014年第7期42-46,共5页Chinese Agricultural Science Bulletin

基　　金：国家自然科学基金"基于温度和施氮量的水稻品质遥感监测模型研究"(41061039);国家自然科学基金"烤烟理化参数的光谱监测机理与方法研究"(11164004)

摘　　要：探索建立一种有效的烟叶总氮含量近红外光谱检测模型,并寻找1100～2500 nm波段中预测烟叶总氮含量的有效波长。采用多种不同的光谱处理方法,并选择较优的一阶导数光谱处理原始光谱,再用偏最小二乘回归建立模型和Martens不确定性检验方法选择有效波长。基于全部波长建立的模型,训练集r=0.9930,RMSE=0.0490;交叉验证r=0.9708,RMSE=0.0996;预测集r=0.9747,RMSE=0.0884。基于有效波长建立的模型,训练集r=0.9937,RMSE=0.0464;交叉验证r=0.9744,RMSE=0.0938;预测集r=0.9610,RMSE=0.1116,预测值与化学值的绝对误差小于0.227%,相对误差未超过0.1%。表明使用近红外光谱分析技术检测烟叶总氮含量较好,采用Martens不确定性检验方法选择有效波长,并利用有效波长预测烟叶总氮含量是可行的。An effective NIR spectral detection model of tobacco total nitrogen content was explored, and effective wavelengths for the detection of tobacco total nitrogen content between 1100-2500 nm were found. A variety of spectroscopic processing methods were used to deal with the original spectrum. The method of first derivative was better than others. Partial least squares regression（PLS） was established the regression model, effective wavelengths were selected by Martens＇ uncertainty test. Regression model based on all wavelengths had the regression coefficient r=0.9930, RMSE=0.0490 in training set, r=0.9708, RMSE=0.0996 in cross validation set, and r=0.9747, RMSE=0.0884 in prediction set. Regression model based on effective wavelengths had the regression coefficient r=0.9937, RMSE=0.0464 in training set, r=0.9744, RMSE=0.0938 in cross validation set, and r=0.9610, RMSE=0.1116 in prediction set. The absolute error of prediction value and chemical value was less than 0.227%, and the relative error was not more than 0.1%. It showed that using NIR spectroscopy to detect tobacco total nitrogen content was good. It was feasible to select effective wavelengths using Martens＇ uncertainty test and to detect tobacco total nitrogen content using these effective wavelengths.

关 键 词：烟叶 总氮含量 近红外光谱 偏最小二乘回归 有效波长 

分 类 号：S127[农业科学—农业基础科学]