Chemometrics And Near-infrared Spectroscopy For The Study Of The Quality Of Wood And Bamboo

Holo cellulose (including cellulose and hemicellulose) and lignin are the chief constituent oftimber and bamboo wood. They correla te closely to the processing and utilization of timber andbamboo wood. In the paper industry, the content of cellulose correla te closely to the get rate ofpaper pulp and the quality of paper pulp, the content of lignin is basic to a making of steamingand bleaching technology conditions. The measuring of the content of holo cellulose in fibremateria ls follows the nationa l standard 2677.10-1995, the measuring of the content of ligninfollows the nationa l standard 2677.8-1994. The traditiona l analysis methods of the content ofholo cellulose and lignin are complicated and expensive. Therefore, a new fast, efficient, accurateanalysis technology must be developed.Near infrared spectrum is a green analytica l technology. It is fast, non-destructive, easy andaccurate. But near infrared spectrum overlaps seriously and it needs analysis by chemometricsmethods and computer data processing. The character parameter of timber and bamboo wood isresearched by near-infrared reflecta nce spectroscopy and chemometrics methods. The results asfollows:1. Holocellulose content of eucalyptus and bamboo, lignin content of bamboo were predicted bynear-infrared reflecta nce spectroscopy and back propagation artificia l neura l network (BP-ANN).Holocellulose content of 72 eucalyptus samples, lignin content of 54 bamboo samples andholocellulose content of 53 bamboo samples was measured according to wet-chemica l method.In order to improve the signa l-noise ratio and accelera te computation speed of neura l network,the raw spectral data were pretreated by smoothing, compress and scaling. The number of hiddenneurons, learning rate, momentum, and epochs were optimized by using lea ve-n-out crossvalidation approach. The root mea n square error of prediction are 0.57%, 0.88% and 1.40%.These results are satisfactory.2. Via near-infrared reflecta nce spectroscopy combined with radia l basis function (RBF) neura lnetwork, a model for determining holocellulose content of bamboo was established . 53 bamboosamples were used as experimental materia l. The spectral data were pretreated by smoothing,derivative, compress and scaling. A real data set from near-infrared reflecta nce spectroscopy ofbamboo were used to build up models with RBF. The root mea n square error of predicted model is 0.0323. These results demonstrate that the method is precise. It can be used to determinateholocellulose content of bamboo.3. Via near infrared spectroscopy combined with support vector machine (SVM), models fordetermining holocellulose content of eucalyptus and Chinese fir, lignin content of Chinese firwere established . 72 eucalyptus samples, 58 Chinese fir samples and 47 Chinese fir samples wereused as experimental materia l. The spectra of samples were recorded by near infraredspectrometer. The spectral data were pretreated by smoothing, derivative, compress and scaling.The radia l basis function (RBF) was used as the kernel function. A model with support vectormachine was established. The sum of the square of the relative calibration error are 0.01047,0.003626 and 0.007433, the sum of the square of the relative prediction error are 0.005057,0.009576 and 0.001219. These results demonstrate that the method is precise.4. Via near-infrared reflecta nce spectroscopy combined with genera lized regression neura lnetwork (GRNN), a model for determining holocellulose content of eucalyptus was established .72 eucalyptus sample s were used as experimental materia l. The spectral data were pretreated bysmoothing, derivative, compress and scaling. A real data set from near-infrared reflecta ncespectroscopy of eucalyptus were used to build up models with GRNN. The root mea n squareerror of predicted model is 0.0198. These results demonstrate that the method is precise. It can beused to determinate holocellulose content of eucalyptus.