烟草中淀粉近红外光谱变量的筛选及校正模型的建立

摘要

In order to improve the prediction precision of calibration model, the near infrared spectroscopy(NIR) calibration model for the starch in tobacco was studied. Spectral variables were chosen by means of full spectrum (FS) and variance spectrum (VS), spectral wavelength by means of genetic algorithm (GA), via partial least squares, the calibration models, FS+PLS, VS+PLS and GA+PLS, were established, and the starch contents in 100 flue-cured tobacco samples were predicted. The results showed that: 1) Rc2 and root mean squares error of cross validation (RMSECV) of the three models, FS+PLS (1 557 variables), VS+PLS (781 variables) and GA+PLS (72 variables) were 0.976 4 and 0.433, 0.987 1 and 0.332, 0.988 5 and 0.314, respectively. 2) Comparing with FS+PLS and VS+PLS, the number of variables in GA+PLS was 4.62% and 9.22% of that in FS and VS, its main factor number reduced from 15 to 12, Rc2 increased from 0.976 4 to 0.988 5, and RMSECV decreased from 0.433 to 0.314. 3) The predicted results of starch contents in 100 tobacco samples indicated that, Rp2 and RMSEP of FS+PLS, VS+PLS and GA+PLS models were 0.965 2 and 0.780, 0.984 3 and 0.501, 0.985 3 and 0.496, respectively. The paired T test between the prediction values and the test values was performed, the Sig. value, T value and average relative error (%) were 0.271, 1.107 and 17.48% for FS+PLS, 0.973, 0.034 and 13.13% for VS+PLS, 0.722, 0.357 and 13.12% for GA+PLS, respectively. There were no significant differences between the results predicted by the three methods and the corresponding test values. The RSD values of FS+PLS, VS+PLS and GA+PLS were 10.34%, 6.98% and 4.76%, respectively. The prediction precision of GA+PLS model was better than that of FS+PLS and VS+PLS models. This method provides a reference for improving the precision of prediction model for a complex chemical system.%为提高校正模型的预测精度，以烟草中淀粉近红外光谱（NIR）校正模型为研究对象，分别利用全光谱波段（FS）、方差光谱（VS）筛选光谱变量和遗传算法（GA）筛选光谱波长，结合偏最小二乘法建立校正模型（FS+PLS、VS+PLS和GA+PLS），并对100个初烤烟叶样品进行了预测。结果显示：①FS+PLS（变量数1557个）、VS+PLS（变量数781个）和GA+PLS（变量数72个）3种校正模型的决定系数Rc2、交互验证均方根误差（RMSECV）分别为0.9764、0.433，0.9871、0.332和0.9885、0.314。②与FS+PLS和VS+PLS模型相比，GA+PLS模型的光谱变量数分别减少为FS和VS变量数的4.62%和9.22%，主因子数由15降至12，Rc2明显提高，RMSECV显著降低。③FS+PLS、VS+PLS和GA+PLS模型对100个初烤烟叶样品的预测结果显示，Rp2、预测均方根误差（RMSEP）分别为0.9652、0.780，0.9843、0.501和0.9853、0.496，预测值与其对应的化学检测值之间通过配对T检验，显著性Sig.值、T值和平均相对误差（%）分别为0.271、1.107、17.48%，0.973、0.034、13.13%和0.722、0.357、13.12%，3种方法所建立校正模型的预测值与检测值之间均无显著性差异，模型预测精度（RSD）分别为10.34%、6.98%和4.76%。基于逐步优化光谱信息法建立的GA+PLS校正模型的预测精度优于FS+PLS和VS+PLS模型，该方法对于提高复杂化学体系模型的精度有参考意义。