Wülfert F, Kok W T, Smilde A K
Department of Chemical Engineering, Process Analysis & Chemometrics, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands.
Anal Chem. 1998 May 1;70(9):1761-7. doi: 10.1021/ac9709920.
Temperature, pressure, viscosity, and other process variables fluctuate during an industrial process. When vibrational spectra are measured on- or in-line for process analytical and control purposes, the fluctuations influence the shape of the spectra in a nonlinear manner. The influence of these temperature-induced spectral variations on the predictive ability of multivariate calibration model is assessed. Short-wave NIR spectra of ethanol/water/2-propanol mixtures are taken at different temperatures, and different local and global partial least-squares calibration strategies are applied. The resulting prediction errors and sensitivity vectors of a test set are compared. For data with no temperature variation, the local models perform best with high sensitivity but the knowledge of the temperature for prediction measurements cannot aid in the improvement of local model predictions when temperature variation is introduced. The prediction errors of global models are considerably lower when temperature variation is present in the data set but at the expense of sensitivity. To be able to build temperature-stable calibration models with high sensitivity, a way of explicitly modeling the temperature should be found.
在工业过程中，温度、压力、粘度和其他过程变量会发生波动。当为过程分析和控制目的在线或在生产线上测量振动光谱时，这些波动会以非线性方式影响光谱的形状。评估了这些温度引起的光谱变化对多元校准模型预测能力的影响。在不同温度下获取乙醇/水/2-丙醇混合物的短波近红外光谱，并应用不同的局部和全局偏最小二乘校准策略。比较了测试集的预测误差和灵敏度向量。对于无温度变化的数据，局部模型在高灵敏度下表现最佳，但当引入温度变化时，预测测量的温度知识无助于改善局部模型预测。当数据集中存在温度变化时，全局模型的预测误差要低得多，但以灵敏度为代价。为了能够建立具有高灵敏度的温度稳定校准模型，应该找到一种明确模拟温度的方法。
