[A wavelength selection approach of near infrared spectra Based on SCARS strategy and its application].

According to the characteristics of near infrared spectral(NIR)data, a new tactic called stability competitive adaptive reweighted sampling (SCARS) is employed to select characteristic wavelength variables of NIR data to build PLS model. This method is based on the stability of variables in PLS model. SCARS algorithm consists of a number of loops. In each loop, the stability of each corresponding variable is computed at first. Then enforced wavelength selection and adaptive reweighted sampling (ARS) is used to select important variables according to the stability of variables. The selected variables are kept as a variable subset and further used in the next loop. After the running of all loops, a number of subsets of variables are obtained and root mean squared error of cross validation (RMSECV) of PLS models is computed. The subset of variables with the lowest RMSECV is considered as the optimal variable subset. Validated by NIR data set of protein fodder solid-state fermentation process, the SCARS-PLS prediction model is better than PLS models based on wavelengths selected by competitive adaptive reweighted sampling (CARS) and Monte Carlo uninformative variable elimination (MC-UVE) methods. As a result, twenty one wavelength variables are selected by SCARS method to build the PLS prediction model with the predicted root mean square error (RMSEP) valued at 0.0543 and correlation coefficient (Rp) 0.9908. The results show that SCARS tactic can efficiently improve the accuracy and stability of NIR wavelength variables selection and optimize the precision of prediction model in solid-state fermentation process. The SCARS method has a certain application value.

[Determination of process variable pH in solid-state fermentation by FT-NIR spectroscopy and extreme learning machine (ELM)].

Fourier transform near-infrared (FT-NIR) spectroscopy was attempted to determine pH, which is one of the key process parameters in solid-state fermentation of crop straws. First, near infrared spectra of 140 solid-state fermented product samples were obtained by near infrared spectroscopy system in the wavelength range of 10 000-4 000 cm(-1), and then the reference measurement results of pH were achieved by pH meter. Thereafter, the extreme learning machine (ELM) was employed to calibrate model. In the calibration model, the optimal number of PCs and the optimal number of hidden-layer nodes of ELM network were determined by the cross-validation. Experimental results showed that the optimal ELM model was achieved with 1040-1 topology construction as follows: R(p) = 0.961 8 and RMSEP = 0.104 4 in the prediction set. The research achievement could provide technological basis for the on-line measurement of the process parameters in solid-state fermentation.