不同烘烤条件下烤烟纤维素的近红外光谱检测模型研究  

Study on NIR Spectral Detection Model of Flue-cured Tobacco Cellulose Content Under Different Baking Conditions

作　　者：魏晓楠[1] 唐延林[1] 方智文[1] 凌智钢[1] 李玉鹏[1] 方世诚 

机构地区：[1]贵州大学物理系,贵阳550025

出　　处：《中国农学通报》2015年第17期65-69,共5页Chinese Agricultural Science Bulletin

基　　金：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);贵州大学创新基金"不同氮;钾营养下烤烟理化参数光谱监测模型研究"(研理工2014068)

摘　　要：为探索不同烘烤条件下烤烟纤维素含量近红外光谱检测模型,采用偏最小二乘回归法(PLS)对不同烘烤条件下的共85个样品,分别基于全部波长建立模型。常规烘烤时,定标集r=0.9949,RMSE=0.1122;交叉验证集r=0.9234,RMSE=0.4636;预测集r=0.8982,RMSE=0.6963。低温烘烤时,定标集r=0.9811,RMSE=0.3279;交叉验证集r=0.9456,RMSE=0.5290;预测集r=0.9938,RMSE=0.1608。高温烘烤时,定标集r=0.9128,RMSE=0.4381;交叉验证集r=0.8215,RMSE=0.6162;预测集r=0.9743,RMSE=0.1986。结果表明,采用偏最小二乘法预测不同烘烤条件下烤烟纤维素含量是可行的。The NIR spectral detection model of flue-cured tobacco cellulose content under different baking conditions was explored. For 85 samples under different baking conditions, partial least squares regression （PLS） was used to establish the regression model based on all wavelengths. Under common baking, the regression coefficient r=0.9949, RMSE=0.1122 in calibration set; r=0.9234, RMSE=0.4636 in cross validation set; r=0.8982, RMSE=0.6963 in prediction set. Under low temperature baking, r=0.9811, RMSE=0.3279 in calibration set; r=0.9456, RMSE=0.5290 in cross validation set; r=0.9938, RMSE=0.1608 in prediction set. Under high temperature baking, r=0.9128, RMSE=0.4381 in calibration set; r=0.8215, RMSE=0.6162 in cross validation set; r=0.9743, RMSE=0.1986 in prediction set. The results showed that using the partial least squares regression to predict the cellulose content of flue-cured tobacco under different baking conditions was feasible.

关 键 词：不同烘烤条件 烤烟 纤维素含量 近红外光谱 偏最小二乘回归 

分 类 号：S127[农业科学—农业基础科学]