A new near-infrared spectral transformation method for quantitative analysis

Abstract

The existing near-infrared spectrum preprocessing methods are limited in improving the performance of prediction models for quantitative analysis because they are hard to extract furthermore valid information from the complex near-infrared spectra. This paper proposes a new near-infrared spectral transformation method based on the Gaussian-curve-fitting—adaptive full-rank fitting integral (AFFI) transformation, which can automatically adapt to different spectral shapes and fully display the useful information hidden in the waveforms. In this method, the optimal bandwidths of the Gaussian functions are determined by correlation analysis between waveform and bandwidths; the heights of the Gaussian functions are obtained by constructing and solving the curve-fitting equations; the transformation is realized by integrating these Gaussian functions. In the experiments, the variation of the Pearson correlation coefficient between the near-infrared spectral bands and quantitative analysis object is used to demonstrate the effectiveness of the proposed method. Finally, the partial least squares (PLS) method was used to build prediction models of quantitative analysis. The experimental results of two different near-infrared spectral data sets show that the proposed method has obvious advantages with some existing pretreatment and transformation methods.