Chu Xiao-li, Yuan Hong-fu, Wang Yan-bin, Lu Wan-zhen
Research Institute of Petroleum Processing, Beijing 100083, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2004 Jun;24(6):666-71.
There are three approaches to developing robust near infrared calibration models, including spectral pretreatment such as differentiation, Piecewise Multiplicative Scatter Correction (PMSC), Finite Impulse Response (FIR), and Orthogonal Signal Correction (OSC), to remove external variations, selecting wavelengths which are insensitive to external variations, and constructing temperature-hybrid calibration models. In this paper, these three strategies were investigated based on reforming gasoline NIR spectra collected at different temperatures in order to develop robust RON and benzene calibration models against temperature. It has been found that with only spectral pretreatment even OSC method fails to obtain satisfactory results, which could not remove the effects caused by temperature fluctuation. Selecting wavelengths by genetic algorithms and constructing temperature-hybrid calibration models, in which spectra measured at different temperature are combined into one calibration set, are both good approaches to developing robust NIR calibration models against temperature. The latter seems better because it needs no special knowledge and extra software, but thenon-linear effects should be considered in practical applications.
有三种方法来开发稳健的近红外校准模型，包括光谱预处理，如微分、分段乘法散射校正（PMSC）、有限脉冲响应（FIR）和正交信号校正（OSC），以消除外部变化；选择对外部变化不敏感的波长；以及构建温度混合校准模型。本文基于对不同温度下收集的重整汽油近红外光谱进行研究，探究这三种策略，以开发针对温度变化的稳健的研究法辛烷值（RON）和苯校准模型。研究发现，仅通过光谱预处理，即使是OSC方法也无法获得满意的结果，无法消除温度波动带来的影响。通过遗传算法选择波长并构建温度混合校准模型（将不同温度下测量的光谱组合成一个校准集），都是开发针对温度变化的稳健近红外校准模型的好方法。后者似乎更好，因为它不需要特殊知识和额外软件，但在实际应用中应考虑非线性效应。
