Jeszenszky Eva, Kocsányi László, Barócsi Attila, Richter Péter
Department of Atomic Physics, Budapest University of Technology and Economics, Hungary.
Appl Spectrosc. 2006 Feb;60(2):162-7. doi: 10.1366/000370206776023403.
Determining the thickness of plastic sheets on the basis of near-infrared spectra by building a multivariate calibration model requires a relatively large sample set. In the thickness region, where just a few non-interference-patterned samples are available, it is a waste of information if interference-patterned spectra are excluded. After eliminating the interference pattern from the spectra (filtering), the calibration set can be extended with these filtered spectra. Fourier transformation of an interference-patterned spectrum versus wavenumber leads to a Fourier spectrum as a function of the optical path length containing an easily recognizable interference peak. Unfortunately, this peak coincides with components of the spectral information of absorbance, on which multivariate calibration is based. Hence, replacing the interference peak is a cardinal step of the filtering process. Since the Fourier spectrum versus optical path length function is not known, it has been shown that interpolated data over the remaining Fourier components can be substituted for the missing part of the spectrum. In this paper, a novel method is proposed that uses a linear approximation between the Fourier spectra and the thickness values so that the regression coefficients are calculated on components of all but the interference-patterned Fourier spectra and the corresponding thicknesses, and then the deleted components in the filtered spectrum are replaced. This latter method yields more detailed Fourier spectra. Reducing the disturbing effect of scattering is also discussed. The effectiveness of the filtering was tested on low-density polyethylene sheets. The performance of different calibration models with or without filtering was compared by significance tests on standard error of prediction values. Application of the new Fourier type filtering technique led to significant improvements in the calibration performance.
通过建立多元校准模型基于近红外光谱确定塑料片材的厚度需要相对较大的样本集。在厚度区域,仅有少数无干涉图案的样本可用,如果排除有干涉图案的光谱则是信息的浪费。从光谱中消除干涉图案(滤波)后,校准集可以用这些滤波后的光谱进行扩展。有干涉图案的光谱相对于波数的傅里叶变换会得到一个作为光程长度函数的傅里叶光谱,其中包含一个易于识别的干涉峰。不幸的是,这个峰与多元校准所基于的吸光光谱信息的成分重合。因此,替换干涉峰是滤波过程的关键步骤。由于傅里叶光谱相对于光程长度函数未知,已表明可以用其余傅里叶成分上的插值数据替代光谱中缺失的部分。本文提出了一种新方法,该方法利用傅里叶光谱与厚度值之间的线性近似,以便在除有干涉图案的傅里叶光谱及其相应厚度之外的所有成分上计算回归系数,然后替换滤波光谱中删除的成分。后一种方法产生更详细的傅里叶光谱。还讨论了降低散射干扰效应的问题。在低密度聚乙烯片材上测试了滤波的有效性。通过对预测值标准误差的显著性检验比较了有无滤波的不同校准模型的性能。新的傅里叶型滤波技术的应用导致校准性能有显著提高。