[Discrimination of wood biological decay by soft independent modeling of class analogy (SIMCA) pattern recognition based on principal component analysis].

Wood, as a biomass materials, tends to be attacked by microorganisms, and its structure could be rapidly destroyed by biological decay. Therefore, it's significant to rapidly and accurately detect or identify biological decay in wood. Recently, extensive research has demonstrated that near infrared spectroscopy (NIR) and soft independent modeling of class analogy (SIMCA) can be used to discriminate or detect a wide variety of food, medicine and agricultural products. The use of NIR coupled with principal component analysis (PCA) and SIMCA pattern recognition to detect wood biological decay was investigated in the present paper. The results showed that NIR spectroscopy coupled with SIMCA pattern recognition could be used to rapidly detect the biological decay in wood. The discrimination accuracy by the SIMCA model based on the training set for the non-decay, white-rot and brown-rot decay samples were 100%, 82. 5% and 100%, respectively; and that for the samples for the test set were 100%, 85% and 100%, respectively. However, some white-rot decay samples were mis-discriminated as brown-rot decay, for which the main reasons might be that the training set does not have enough typical samples, and there's a slight difference between white-rot and brown-rot decay during the early stage of decay.

[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. The aim of this paper is to develop a rapid near infrared (NIR) reflectance spectroscopic method to characterize the Klason lignin content of bamboo. Fifty four samples from three growth years, two positions along the longitudinal directions and three positions along the radial directions within a bamboo pole were prepared. The Klason lignin contents of 54 samples were analyzed according to traditional chemical method, the spectra of these samples were collected by NIR in the range of 350 to 2500 nm, and the relationship between the lignin content and the spectra of these samples was established by multivariate statistical technique. After second derivative pretreatment of raw spectra, the Klason lignin contents of the bamboo samples were quantified using partial least-squares statistical analysis (PLS1) and full cross validation in the range of 1011-1675 nm and 1930-2488 nm. High coefficients of correlation (r) were obtained between the predicted NIR results and those obtained from traditional chemical method. The correlation coefficient of calibration model and prediction model was 0.99 and 0.97, respectively. The standard error of calibration (SEC) and standard error of prediction (SEP) was 0.36% and 0.59%, respectively. It was found that the lignin content in bamboo could be determined rapidly with reasonable accuracy by the NIR method.