近红外稳健分析校正模型的建立(Ⅰ)--样品温度的影响

样品温度对近红外光谱分析模型稳健性有明显影响 ,消除其影响的三类方法包括光谱预处理、波长选择和温度补偿校正集。文章以重整汽油 /辛烷值 /苯含量为研究体系 ,考察了这三类方法对建立稳健分析模型的有效性 ,并详细研究了温度补偿校正集样品数量及性质分布对温度混合模型稳健性的影响规律。结果表明 ,仅通过光谱预处理方法难以消除温度对模型稳健性的影响 ;遗传算法波长选择和温度补偿校正集对消除温度影响是有效的 ,而后者更容易实现 ,但在实际应用中应考虑非线性问题

Developing Robust Near Infrared Calibration Models

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 the non linear effects should be considered in practical applications.