[Study on artificial neural network combined with near infrared spectroscopy for wood species identification].

In the present article, near infrared spectra of 89 wood samples of different geographical provenances and species were measured, and back propagation artificial neural networks(BPANN) and generalized regression neural network (GRNN) were used for modeling of wood species NIRS identifying. Parameters for two neural networks were chosen via analysis of variance, respectively; and networks were trained with optimum parameters. Considering the difference between spectra, spectra with different levels of white noise and different levels of bias were simulated and predicted by using the models built.

[Rapid identification of softwood and hardwood by near infrared spectroscopy of cross-sectional surfaces].

The feasibility of wood identification of softwood and hardwood by near infrared spectroscopy (NIR) coupled with partial least squares discriminant analysis (PLS-DA) was investigated in the present paper. The near infrared spectra (780 - 2 500 nm) were collected from wood cross-section from one softwood species (Chinese fir) and one hardwood species (eucalyptus). The results show that: (1) The identification accuracy of the calibration samples predicted by the model based on NIR coupled the PLS-DA was 100%.

[Application of NIR spectroscopy to estimate of MFA and fiber length of Neosinocalamus a f finis].

Near infrared spectroscopy was applied to rapidly predict microfibril angle (MFA) and fiber length of Neosinocalamus a f finis Keng by using a fiber-optic probe in diffuse reflectance mode. The MFA and fiber length were measured by X-ray diffractometry and optical microscope, respectively. Partial least squares (PLS) was used to build models based on raw and pretreated spectra, including noise spectra and noise combined with orthogonal signal correction (OSC) spectra.

[Study on the wood grading by near infrared spectroscopy].

The present paper discussed wood grading according to modulus of rupture (MOR) by near infrared (NIR) spectroscopy. The calibration model was built between MOR of wood and NIR data in the range of 1 000-1 400 nm with partial least square regression (PLS). The correlation coefficient (r) was 0.

[Discrimination of bamboo fiber and ramie fiber by near infrared spectroscopy].

The research on discrimination of natural bamboo fiber, bamboo pulp fibers and ramie fiber used in textile was demonstrated by near infrared (NIR) spectroscopy. First, the spectra of three kinds of fiber were scanned by NIR spectrometer. Then, the spectral data were pretreated by first derivatives.

[FTIR studies of masson pine wood decayed by brown-rot fungi].

Fourier transform infrared (FTIR) spectroscopy was used to study the chemical changes of masson pine (pinus massoniana lamb.) decayed by the brown-rot fungus Wolfiporia cocos (Schwein.) Ryvarden & Gilbn.

[Study on denoising near infrared spectra of wood based on wavelet transform].

Near infrared (NIR) spectra of wood samples are often confused by a series of noise, which greatly influences accurate analytical result. In order to improve analytical precision, the authors need to pretreat the spectrum data. Derivative can correct baseline and background effects, increasing the resolution ratio of the spectra.

[Rapid modeling method for spectroscopic analysis of chemical components of bamboo].

A rapid modeling method for predicting the chemical components contents of bamboo was presented. The holocellulose contents and lignin contents of 54 samples from three growth years, two longitudinal positions and three radial positions were analyzed according to traditional chemical methods. Eleven samples were selected based on their holocellulose content and lignin content from these 54 samples to cover the range of holocellulose content and lignin content.

[Quantitative prediction of holocellulose, lignin, and microfibril angle of Chinese fir by BP-ANN and NIR spectrometry].

The amount of holocellulose, lignin, and microfibril angle of Chinese fir was predicted by using back-propagation neural network (BP-ANN) combined with near infrared (NIR) spectrometry. First, the data of original spectra were pretreated by Savitzky-Golay smoothing algorithm and the second derivative, then the data of near infrared spectrometry with 171 points were compressed to 86 points by using wavelet transform, and finally, the models were established by using BP-ANN. The models were validated using leave-n-out cross-validation approach, and the influences of the number of hidden neurons, learning rate, momentum, and epochs were discussed in the present paper.

[Study of the Eucalyptus and Poplar by generalized two-dimensional infrared correlation spectroscopy].

Poplar and Eucalyptus were identified fast by Fourier transform infrared spectroscopy (FTIR) combined with two-dimensional correlation spectroscopy (2D) in the present paper. The two kinds of wood were similar to each other in one-dimensional IR spectra but quite different in 2D FTIR spectra. In the range between 800 and 1500 cm(-1), they are similar and three strong auto-peaks were aroused around 1221, 954 and 879 cm(-1) in synchronous spectrum and four weak auto-peaks were aroused around 1470, 1150, 1105 and 1008 cm(-1), respectively.

[Study of thermal perturbation of natural bamboo fiber by two dimensional correlation analysis and Fourier transform infrared spectroscopy].

The Fourier transform infrared spectroscopy (FTIR) combined with generalized two-dimensional correlation analysis was applied to study the mini-heating process of natural bamboo fiber. The absorption peaks of natural bamboo fiber and bamboo in the FTIR spectra were different, which showed the contents of lignin and hemicelluloses of natural bamboo fiber was lower than those of bamboo. The changes in absorption peaks of natural bamboo fiber in the FTIR spectra at different temperatures were inconspicuous during heating up from 50 to 120 degrees C, which showed that there was not oxidation reaction in natural bamboo fiber during the process.

[Rapid determination of Klason lignin content in bamboo by NIR].

Lignin is one of the main components of lignocellulosic materials. The main purpose of wood cooking and bleaching is to remove lignin by chemical agent in paper industry. Whereas the lignin content shows wide variations depending on its tree specie, site condition, part and so on, it is essential to analyze the lignin content of different raw material.

[Influence of sample surface roughness on mathematical model of NIR quantitative analysis of wood density].

Near infrared spectroscopy is widely used as a quantitative method, and the main multivariate techniques consist of regression methods used to build prediction models, however, the accuracy of analysis results will be affected by many factors. In the present paper, the influence of different sample roughness on the mathematical model of NIR quantitative analysis of wood density was studied. The result of experiments showed that if the roughness of predicted samples was consistent with that of calibrated samples, the result was good, otherwise the error would be much higher.

[Determination of holocellulose and lignin content in Chinese fir by near infrared spectroscopy].

The contents of holocellulose and lignin of wood are important determinants of the pulping quality of wood. The determination of holocellulose and lignin contents using traditional chemical methods is a costly and time-consuming process. Near infrared reflectance (NIR) analysis offers a fast, nondestructive testing and low cost alternative for prediction of wood quality.

[Water in wood and its near infrared spectroscopic analysis].

Water is one of the most important characters of wood, so the effect of moisture content on the results must be considered in wood property analysis by NIR spectroscopy. In the present study, the absorbance of wood with various moisture content in NIR spectra was analyzed. Then the moisture contents in wood were predicted by NIR spectroscopy.

[The analysis of wood microfibril angle by near infrared spectroscopy and X-ray diffractometry].

In the present paper, near infrared (NIR) spectroscopy and X-ray diffractometry were used to rapidly predict the microfibril angle (MFA). MFA is one of the most important factors affecting wood properties. Wood resource utilization and tree improvement programs require cost-effective methods for the rapid analysis of thousands of samples.

[Near infrared spectroscopy of wood sections and rapid density prediction].

In the present report, the use of calibrated near infrared (NIR) spectroscopy for rapid prediction of solid wood density is described. NIR spectra were obtained from the three sections (cross, radial and tangential section) of each Chinese fir sample. The authors found that the spectra of the three sections are different and the correlation coefficients between the laboratory-determined density and the NIR-fitted data are different, too.