Abstract
A derivative spectral estimator (DSE) based on singular perturbation technique was designed and a quantitative analysis method based on derivative spectra information space, termed derivative spectra fusion interval partial least squares (DSF-iPLS) modeling was proposed. DSF-iPLS mainly focused on obtaining final fusion model by making full use of derivative spectra information. The glucose spectra dataset with concentrate ranging from 0.04% to 5% and the beer spectra dataset with the original extract concentration ranging from 4.23 to 18.76 degrees P (Plato) were used to evaluate the effectiveness of the proposed quantitative analysis method. The experiment results indicated that DSF-iPLS model for two infrared spectra datasets provided the minimum root mean square error of prediction (RMSEP) and the values were 0.121 and 0.087, respectively. Compared with other single model, DSF-iPLS model based derivative spectra could provide more excellent predictive performance.
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