SIMCA法判别分析木材生物腐朽的研究

Discrimination of Wood Biological Decay by Soft Independent Modeling of Class Analogy (SIMCA) Pattern Recognition Based on Principal Component Analysis

木材是一种生物质材料,容易受到各种微生物的危害,生物腐朽可以迅速导致木材结构的破坏,因此,对木材生物腐朽的快速、准确地检测或鉴定具有重要意义。近几年来,近红外光谱和SIMCA方法正被用于识别或检测食品、药品和农产品等研究中,因此,本研究尝试利用近红外光谱结合SIMCA方法来检测木材的生物腐朽。研究结果表明,应用近红外光谱和SIMCA方法能有效地判别木材的生物腐朽类型,通过培训集样本建立的基于PCA分析的SIMCA判别模型对未腐朽、白腐和褐腐三种类型样本进行回判,判别准确率分别为100%,82.5%和100%;而对未知腐朽类型的样本(包括未腐朽、白腐和褐腐样本),判别准确率分别为100%,85%和100%;SIMCA方法对未腐朽和褐腐类型的判别准确率均达到100%,但对白腐样本都有错判,造成这种错判的主要原因可能是由于样本包括的信息不够丰富以及腐朽初期白腐和褐腐试样的性质差异太小等。

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.