[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.
Yang
Zhong
Z
Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing 100091, China. zyang@caf.ac.cn
Jiang
Ze-Hui
ZH
Fei
Ben-Hua
BH
Qin
Dao-Chun
DC
D025341:Principal Component Analysis; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D014934:Wood / Q000737:chemistry* / Q000378:metabolism