Abstract In the past two decades, there has been considerable research interest in utilizing infrared techniques for process analysis, monitoring and for the control of broad biological and chemical fields, such as pharmaceutical, food and petroleum industries. The infrared techniques, including Near-infrared and Mid-infrared technique, is widely used in these areas, extracting information with fast, noninvasive and low cost of sample measurement. Multivariate calibration techniques such as Partial Least Squares (PLS) was often used because of the ability of overcome both the dimensionality and the collinear problems of infrared spectral data. However, there are still a number of obstacles with infrared quantitative calibration, such as how to extract useful information and eliminate noise interference concurrently during use. In this sense, the research in this thesis focuses on the quality improvement of the spectra and contributes to improving the robustness of the quantitative analysis model. The content in this thesis includes the following aspects: Scattering correction of the infrared spectra are explored in this paper. a novel algorithm was proposed to find a spectral region automatically that the interesting chemical absorbance and noise are low, that is, finding an interference dominant region (IDR). Based on the definition of IDR, a two-step method was proposed to find the optimal IDR and the corresponding correction parameters estimated from IDR. Finally, the correction was performed to the full spectral range using previously obtained parameters for the calibration set and test set, respectively. Contrary to conventional correction methods, the proposed method used the infromation of reference values to find IDR. In addition, the method can be applied to multi target systems with one IDR suitable for all targeted analytes. Tested on two benchmark data sets of near-infrared transmittance spectra, the performance of the proposed method provided considerable improvement compared with full spectral estimation methods and comparable with other stat-of-art methods. A modified partial least squares algorithm is presented based on a novel weight updating strategy. In original PLS, the estimation of weight vector are often suffered with X-space noise, led to complex model and poor performance. To slove this problem, the slice transform (SLT) technique is introduced to provide a piece-wise linear mapping to the weight vectors. The new weight can ha...
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