Rapid detection of various ions in cigarette paper by near-infrared spectroscopy
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Abstract
In order to rapidly analyze contents of various ions in cigarette paper, Fourier transform near-infrared spectroscopy (FT-NIR) and traditional chemical analytical method were employed to collect spectral data and the contents of chemical components (K+, Na+, Ca2+, Mg2+, Cl－, NO3－, SO42－, PO43－, citrate ion, and tartrate ion) of 202 cigarette paper samples. Combined with partial least squares regression (PLSR) and least squares support vector regression (LS-SVR) algorithms, mathematical prediction models for various ions in cigarette paper were established based on the spectral data and chemical values. The results showed that, among the models based on the classical PLSR algorithm for chemical components in cigarette papers, only the models of Mg2+ and Cl－ could meet the needs of rapid detection (their model fitting coefficients were all larger than 0.90, their prediction errors were smaller). Models based on LS-SVR algorithm for chemical components performed better than PLSR models. Among them, the R2 of the test set in the LS-SVR models for K+, Na+, Mg2+, Cl－ and citrate ion were all more than 0.90, while for Ca2+, the R2 of the test set in the LS-SVR model was 0.85, and the prediction error was smaller, which demonstrated that the models for the above-mentioned six ions could be used in the rapid detection for cigarette paper. While for SO42－, although the R2 (R2=0.72) could be accepted, the model could not be used for rapid detection because the error between predicted value and real value was over 25%. For the other three anions, both fitting abilities and prediction errors were unacceptable, therefore the models could not be used for the predictions of contents of NO3－, PO43－ and tartrate ion.
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