Wulfert F, Kok WT, Smilde AK
Department of Chemical Engineering, Process Analysis & Chemometrics, University of Amsterdam, The Netherlands.
Anal Chem. 2000 Apr 1;72(7):1639-44. doi: 10.1021/ac9906835.
In process analytical applications it is not always possible to keep the measurement conditions constant. However, fluctuations in external variables such as temperature can have a strong influence on measurement results. For example, nonlinear temperature effects on near-infrared (NIR) spectra may lead to a strongly biased prediction result from multivariate calibration models such as PLS. A new method, called Continuous Piecewise Direct Standardization (CPDS) has been developed for the correction of such external influences. It represents a generalization of the discrete PDS calibration transfer method and is able to adjust for continuous nonlinear influences such as the temperature effects on spectra. It was applied to shortwave NIR spectra of ethanol/water/2-propanol mixtures measured at different temperatures in the range 30-70 degrees C. The method was able to remove, almost completely, the temperature effects on the spectra, and prediction of the mole fractions of the chemical components was close to the results obtained at constant temperature.
在过程分析应用中,并非总能保持测量条件恒定。然而,诸如温度等外部变量的波动会对测量结果产生强烈影响。例如,温度对近红外(NIR)光谱的非线性效应可能导致来自多元校准模型(如PLS)的预测结果出现严重偏差。一种名为连续分段直接标准化(CPDS)的新方法已被开发用于校正此类外部影响。它是离散PDS校准转移方法的推广,能够针对连续的非线性影响进行调整,例如温度对光谱的影响。该方法应用于在30 - 70摄氏度范围内不同温度下测量的乙醇/水/2 - 丙醇混合物的短波近红外光谱。该方法几乎能够完全消除温度对光谱的影响,并且化学成分摩尔分数的预测结果接近在恒定温度下获得的结果。