Jeszenszky Eva, Kocsányi Lászlo, Richter Péter
Department of Atomic Physics, Technical University of Budapest, H-1111 Budafoki ut. 8., Budapest, Hungary.
Appl Spectrosc. 2004 Jun;58(6):693-7. doi: 10.1366/000370204872953.
A Fourier type filtering method is proposed for the pretreatment of near-infrared (NIR) spectra of thin (<100 microm) transparent plastic foils before their identification by means of multivariate calibration methods. The interference of multiply reflected beams from the boundary surfaces of the foil causes a disturbing signal component in the spectrum and the identification becomes impossible. The purpose of the filtering is to eliminate the interference pattern from the spectrum. In the Fourier transformed NIR spectrum against the wavenumber there appears a discrete spectral component caused by the interference. This component can be recognized and cut off. After inverse Fourier transformation of such pretreated spectra, absorption peaks are free from interference modulation, so application of multivariate calibration methods is much more effective. With principal component analysis (PCA) on cluster plots, visual distinction between different plastics becomes possible. Correct class membership is provided by use of the Mahalanobis distance.
提出了一种傅里叶型滤波方法,用于在通过多元校准方法识别薄(<100微米)透明塑料薄膜的近红外(NIR)光谱之前进行预处理。来自薄膜边界表面的多次反射光束的干扰会在光谱中产生干扰信号成分,从而无法进行识别。滤波的目的是从光谱中消除干涉图样。在傅里叶变换后的近红外光谱中,相对于波数会出现由干涉引起的离散光谱成分。该成分可以被识别并去除。对经过这种预处理的光谱进行傅里叶逆变换后,吸收峰不受干扰调制的影响,因此多元校准方法的应用更加有效。通过对聚类图进行主成分分析(PCA),可以直观地区分不同的塑料。使用马氏距离可以确定正确的类别归属。