A Study Of NIR Information Extraction And Its Application In Wood Property Analysis

With the rapid development of China's economy and society, and the growing demand for wood, it is necessary to speed up the directed breeding of wood as well as improve the efficiency in its utilization so as to ease the pressure of its supply. It is, therefore, significant to find fast and accurate methods to detect wood properties with regard to the silviculture improvement, genetic modification and effective utilization for China.As a new analytical technique, the NIR spectroscopy can make a fast and accurate nondestructive examination of the physical, mechanical and chemical properties of organic solid, liquid, and powder samples. It combines and integrates the latest findings in modern spectroscopy, computerized information processing, stechiometric data analysis and multivariate correction, and is applied in many fields with its uniqueness. There are still some technical problems in NIR spectroscopy remaining to be solved. One of the problems is how to extract and enhance the effective information in the complicated, overlapping and changing spectrum, so as to provide excellent spectral information to build an NIR composition concentration prediction model.This dissertation has made a systematic study of how to extract spectral signatures from wood NIR and how give a quantitative expression, based on the overall analysis of generation mechanism of NIR. With the Chinese fir and eucalyptus NIR in planted forest as the information source, it makes a quantitative analysis on the method of NIR information extraction, builds Chinese fir density and eucalyptus lignin content prediction model by using the principle component analysis and partial least square method, and compares the influences by different spectral information extraction methods.The dissertation mainly studies:(1) Taking the quadratic sum of the second derivatives of spectra as well as the root-mean-square error as the standard, it compares the influences of average smoothing and fold smoothing of spectral data on the spectral information. For the average smoothing method, when the window widths are 15,17 and 19, it shows sound results in extraction and maintenance of effective spectral information; while for the fold smoothing method, when the optimum window widths are 13,15,17, it also shows sound results in extraction and maintenance of effective spectral information. Both averaging smoothing and fold smoothing methods used in spectral data can remove the measurement noise, and optimize the spectral information.(2) It provides a method for preprocessing the wood NIR information, based on the moving window variance method which measures the fluctuations of data using the variances of spectral data over a local band, so as to identify the bands with large fluctuations and then de-noise these bands. Taking the quadratic sum and root-mean-square error of spectral data as the standard, it analyzes and discusses the processing effects of this method under different windows and different thresholds. When the window size takes 4 to 8, the lower quartile of 0.87-0.9 or 0.94-0.95 of window variance are thresholds, this method produces sound de-noising effects.(3) It studies the de-noising of the first derivative spectrum from wood by using wavelet transform. Taking the signal-to-noise ratio and root-mean-square error as the standards, it compares the four wavelet threshold rules as fixed sqtwolog, partial likelihood estimation (rigrsure), mixd (heursure) and maximum and minimum (minimaxi), and studies the de-noising of derivative spectral data. When spectral information is decomposed under the scale of 4, and the fixed threshold rule is used, the signal-to-noise ratio (SNR) is 10.22, and root-mean-square error (RMSE) is 0.000307, which shows that the de-noising effect is better than that of other methods.(4) It studies the signature extraction of spectral signals by using the wavelet transform modulus maximum. According to the different propagation properties of noise and signal at the maxima of wavelet transform, it carries out wavelet decomposition under scale 8 on the wood NIR signals, and searches the wavelet modulus maxima of signals and noise in the neighboring scales., extracts signals modulus maximum, eliminate noise modulus maximum. After wavelet inverse transform, it rebuilds de-noised signals, which achieves the purposes of extracting spectral signatures, and removing the noise. After decomposition under the scale 4, and de-noising by wavelet modulus maximum, the signal-to-noise ratio (SNR) reaches 15.14, and the root-mean-square error (RMSE) is 0.000953. Wavelet transform modulus maximum can effectively extract the spectral information and remove the spectral noise.(5) It builds the Chinese fir density prediction model by using the principle component regression method and partial least square method. After the comprehensive analysis and assessment of Chinese fir density prediction model built based on the original spectra and the spectral data preprocessed by the first derivative method, the second derivative method, the moving average smoothing method, fold smoothing method, moving window variance method, multivariate scattering correction, data standardization, wavelet threshold method, and wavelet modulus maximum, the correlation coefficients of correction set models by the moving window variance method and wavelet maximum are 0.9391 and 0.9405, and for prediction set, the correlation coefficients are 0.8706 and 0.8756 respectively, which shows better results than that by other preprocessing methods. It carries out 25 point fold smoothing on the first derivative of original spectra, rejects 6 abnormal samples, and selects 10 principle components to further optimize the models, then the correlation coefficient for the correction set model built is 0.9692, and for prediction set, it is 0.8976.(6) It builds the prediction model of eucalyptus lignin content by the partial least square method. After the whole analysis of model of eucalyptus lignin which after predispose.The results shows that the correlation coefficient of correction set models by the moving window variance method is 0.9011, and for prediction set, the correlation coefficient are 0.8414, which shows a better result than that by other preprocessing methods. It carries out 19-point moving average smoothing on the first derivative of original spectra, rejects 4 abnormal samples, and selects 7 principle components to further optimize the models, the correlation coefficient for the correction set model built is 0.9742, and for prediction set, it is 0.8768.