不同物理形态烟叶样品的模型转移研究

采用近红外光谱仪采集整烟叶、卷烟丝和烟叶粉末光谱数据,并采用流动分析仪测定烟碱含量,利用偏最小二乘法建立烟叶烟碱的光谱预测模型,再通过斜率截距算法(SBC)、分段直接标准化算法(PDS)和典型相关分析算法(CCA)3种模型转移算法,将整烟叶、卷烟丝和烟叶粉末便携式近红外光谱转移到粉末状烟叶傅立叶近红外光谱模型上,比较分析预测均方根误差值(RMSEP)。结果表明:烟叶粉末烟碱近红外光谱预测模型经SBC、PDS和CCA算法模型转移后的RMSEP值分别为0.741 0、0.736 5、0.298 2,卷烟丝的RMSEP值分别为0.725 0、0.513 2、0.222 2,整烟叶的RMSEP值分别为0.712 6、0.446 6、0.333 9,CCA算法模型转移优于SBC与PDS算法。

Calibration transfer on tobacco NIR spectra with different physical states

In order to use a portable near infrared spectrometer preferably and quickly to conduct the real–time on scene test in tobacco production, the measured spectrum was usually moved to the spectral model already established in laboratory by mathematical method to directly conduct the forecast analysis, which saves cost compared with rebuilding a model. In this paper, slope intercept algorithm (SBC), piecewise direct standardization (PDS) and canonical correlation analysis (CCA) were used and compared in transfer of portable near infrared spectrum of the whole leaf, cigarette silk and powder to the powdered tobacco on Fourier near–infrared spectroscopy model, followed by predicting values of root mean square error (RMSEP). The results showed thatRMSEP value of near infrared spectrum of the powder, cigarette silk and whole leaf calibration transferred by SBC algorithm were 0.7365, 0.7988, 0.2986 respectively, by PDS were 0.8892, 0.6409, 0.3007 respectively, by CCA were 0.7165, 0.7613, 0.5369, respectively.RMSEP results after calibration transfer with CCA were better than those with SBC and PDS, and better theRMSEP predicted by rebuilt model using the portable near infrared spectrum directly.