Quantitative Determination Of Fibrous Materials Chemical Composition And Near Infrared Model Construction

Fibrous materials are wildly used in textile, paper making, bioenergy and other related area. Quantitative analyze chemical composition of fibrous materials is essential on raw material evaluation, scientific research and producing process. However, most of the researches use wet chemistry as a standard method to quantitive analyze chemical composition in fibrous materials, wet chemistry method also vary in different area. Recent years, modern analysis methods show high advantages than wet chemistry. This research studied the drawbacks of normal wet chemistry analysis method, and tried to establish a more fast and accurate modified method combining with High Performance Liquid Chromatography (HPLC) and near infrared modeling method (NIR). The main results and conclusions of this research are listed blew:1. Established a relatively accurate wet chemistry analysis method using vacuum dry, Fourier Transform Infrared spectroscopy (FTIR) and High Performance Liquid Chromatography (HPLC).(1) Improve the accuracy in testing extractives using vacuum-dry technique, this mehod can avoid the extractive to be evaporated or oxidezed during oven dry.(2) Studied on thermal degradation kinetics of polysaccharides in fibrous materials by using cyclic test combined with origin software and mathematic curve fitting technique. And validate the equation with HPLC test. Using multivariate orthogonal experiments coupled with thermal degradation kinetics equation to get the best temperature system of wet chemistry on test fibrous materials chemical composition. Finally we found that to oven dry sample at60℃and then extractive samples using water in60℃water bath is the best temperature system to test water solube matter in fibrous materious.(3) Resarch found lignin could be degraded in hot alkali liquor, and the optimized the procedure to reduce the error in testing hemicellulose from10%to1%. Study aslo determined the best testing method for lignin and monosaccharides.2. Optimize the best parameters on collecting near infrared spectrum. Studing the relationship between particle size of sample and spectrum resolution and the quality of spectrum, finally determined the best particle size and spectrum resolution.Using southern pine as the raw material collected the near infrared spectra and then constructed near infrared models on particle size of wood lumber,1/8inch,20mesh,40mesh and80mesh samples, respectively. Results showed that the precision of near infrared model could be improved with the particle size decreasing. There were significantly impovements during particle size changed from wood lumber to1/8inch sample and from40mesh to80mesh sample. Study finally determined that80mesh is the best particle size to collect the near infrared spectrum for near infrared model.Colleted the near infrared spectra and construed the NIR model of80mesh samples under spectrum resolution16cm-1,4cm-1and2cm-1, respectively. Results showed that spectrum resolution of4cm-1was the best parameter to collect NIR spectrum.3. Identified the wavenumber ranges in near infrared spectrum of key chemical composition. Comparing the selected wavenumber range and the whole spectrum range on calibrating the near infrared models quality.Using targeted pretreatment method to remove one chemical composition, then collect near infrared spectrum on raw material and pretreated material. Then identify the wavenumber range of this chemical composition by comparing the NIR spectrum of raw material and pretreated material. By using this method, we finally identify the wavenumber range of chemical composition, which are:water5050-5360cm-1, color element8500-10000cm-1, extractives4000-8500cm-1, lignin5800-6900cm-1, sugars (holocellulose, cellulose and hemicellulose)4000-5100cm-1,6900-8500cm-1.Using southern pine (softwood) as the raw material, quantitatively determine its extractives, lignin and sugars using modified wet chemistry and HPLC method which is proposed by national renewable laboratory (NREL). Then determine the cellulose and hemicellulose content based on the structure of softwood. Then further construted NIR models of the chemical compositon using full wavenumber and selected wavenumber range. Result showed that using selected wavenumber range can significantly improve the pricison of NIR model than using full wavenumber range.4. Research on expand the predictiablity and robust the NIR model.Using southern pine as the raw material, and lignin was determined for NIR model construction. This research tried to expand the lignin content of raw materials by using delignification to decrease the lignin content. The NIR models were established on raw wood samples, pretreated wood samples and all wood samples (combine raw wood sample and pretreated wood sample together). The calibration data and prediction data of pretreated wood lignin model were listed as follows:r square of cross validation0.99, prediction error of cross validation0.72%; r square of prediction0.99, prediction error of prediction0.68%, residual predictive deviation (RPD)12.7. The prediction data of all wood sample lignin model were listed as follows:r square of prediction0.99, prediction error0.6%, RPD value14.34. The results show high predictiablity of lignin model both in prediction precision and prediction range on raw and pretreated wood samples.We also studied the method of robust NIR model. Research found NIR model has low predictiability on predicting sample which chemical composition content beyong the calibration data of NIR model. However, the prediction precision could be improved by adding one of the new bath sample’s information in old NIR model.5. Established NIR models on different species of fibrous materials chemical composition content, including ramie, softwood (southern pine), mixed hardwood (aspen, cotton wood and other species).Quantitative analyzed chemical composition of ramin using modified method in this research. Then NIR models of these chemical compositions were calibrated and validated. Results showed high predictability of NIR models for ramie cellulose, gum. The results of NIR models of cellulose, gum and hemicellulose were r2=0.95,0.91,0.75; RPD=4.54,3.37and2.61, respectively.We also established NIR models for chemical composition on softwood and hardwood, all the r2were over0.9which impled they had high quality to be used for prediction. Study also found that it is much easier to establish a NIR model on pure species than on multispecies samples.