[Research of Straw Biomass Based on NIR by Wavelength Selection of IPLS-SPA].

The whole spectrum usually contains a lot of redundant information in the near-infrared spectroscopy model, the presence of redundant information will increase the model resolution time and increase the difficulty of parsing model, Therefore, how to select the characteristic wavelength quickly and effectly is very crucial. In this paper, we combined the algorithm based on SPA (successive projections algorithm ) with IPLS (interval partial least squares ) to selec the characteristic wavelength in the fermentation of wheat straw microbial biomass, A total of 85 samples prepared by measuring microbial biomass using glucosamine method, 68 samples are chosen as calibration set and 17 simples are chosen as verification set. First, the whole spectral region 520 points are segmented modeling according to the interval wavelength point size 10, 20, 30, 40 and 4 450~4 925 cm-1, 9 194~9 993 cm-1 two-band range are selected as the characteristic wavelength band, then pick out the new feature wavelength points by Successive Projections Algorithm band and Genetic Algorithm (GA), comprehensive analysis and comparison the result of model. The experimental results show that the using of IPLS-SPA algorithm to select the combination band 4 450~4 925 cm-1 & 9 194~9 993 cm-1 has the best modeling effect, compared with the modeling of whole spectrum, the wavelength points decrease from 520 to 10, the correction coefficient of determination R2 rised from 0. 884 9 to 0. 945 28, root mean square error (RMSE) dropped from 11. 104 9 to 8. 203 3, although the genetic algorithm model achieved the better accuracy, but the results are instable and have a strong randomness , while IPLS combined SPA method can select characteristic wavelength information stability and accurately, which can improve the model calculation speed and reduce the fitting difficulty of the model, it can be used as a new reference method for band selection. The results show that using near infrared spectroscopy method for straw biomass rapid detection is feasible.

[Characteristic wavelength variable optimization of near-infrared spectroscopy based on Kalman filtering].

Combining classical Kalman filter with NIR analysis technology, a new method of characteristic wavelength variable selection, namely Kalman filtering method, is presented. The principle of Kalman filter for selecting optimal wavelength variable was analyzed. The wavelength selection algorithm was designed and applied to NIR detection of soybean oil acid value. First, the PLS (partial leastsquares) models were established by using different absorption bands of oil. The 4 472-5 000 cm(-1) characteristic band of oil acid value, including 132 wavelengths, was selected preliminarily. Then the Kalman filter was used to select characteristic wavelengths further. The PLS calibration model was established using selected 22 characteristic wavelength variables, the determination coefficient R2 of prediction set and RMSEP (root mean squared error of prediction) are 0.970 8 and 0.125 4 respectively, equivalent to that of 132 wavelengths, however, the number of wavelength variables was reduced to 16.67%. This algorithm is deterministic iteration, without complex parameters setting and randomicity of variable selection, and its physical significance was well defined. The modeling using a few selected characteristic wavelength variables which affected modeling effect heavily, instead of total spectrum, can make the complexity of model decreased, meanwhile the robustness of model improved. The research offered important reference for developing special oil near infrared spectroscopy analysis instruments on next step.