Abstract
One of the essential factors influencing the prediction accuracy of multivariate calibration models is the quality of the calibration data. A local regression strategy, together with a wavelength selection approach, is proposed to build the multivariate calibration models based on partial least squares regression. The local algorithm is applied to create a calibration set of spectra similar to the spectrum of an unknown sample; the synthetic degree of grey relation coefficient is used to evaluate the similarity. A wavelength selection method based on simple-to-use interactive self-modeling mixture analysis minimizes the influence of noisy variables, and the most informative variables of the most similar samples are selected to build the multivariate calibration model based on partial least squares regression. To validate the performance of the proposed method, ultraviolet-visible absorbance spectra of mixed solutions of food coloring analytes in a concentration range of 20–200 µg/mL is measured. Experimental results show that the proposed method can not only enhance the prediction accuracy of the calibration model, but also greatly reduce its complexity.
Highlights
IntroductionMultivariate entire-spectrum data analysis is recently becoming a hot topic in analytical chemistry
Multivariate entire-spectrum data analysis is recently becoming a hot topic in analytical chemistry.One of the goals of the multivariate spectral analysis is to construct a calibration model that relates spectral databases to the chemical or physical properties of an analytical sample [1]
Throughout this paper, we proposed to conduct a local strategy based on the similarity criterion named as synthetic degree of grey relation coefficient (S-GRC)
Summary
Multivariate entire-spectrum data analysis is recently becoming a hot topic in analytical chemistry. One of the goals of the multivariate spectral analysis is to construct a calibration model that relates spectral databases to the chemical or physical properties of an analytical sample [1]. It is somewhat difficult to discriminate overlapping peaks [2]. Multivariate calibration methods, like principal components regression (PCR) [3] and partial least squares regression (PLSR) [4], have been extensively used in multivariate spectral analysis. PLSR has been proven to be a very powerful multivariate statistical tool for quantitative analysis because of its ability to solve problems, such as collinearity and band overlaps of the spectral data [5]. It has been shown that PLSR with global samples can yield precision prediction models
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