不同烘烤条件下烤烟纤维素的近红外光谱检测模型研究

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。

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

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 established the regression model which 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 content of flue-cured tobacco under different baking conditions was feasible.