文献类型：期刊文献

英文题名：Non-Contact Detection of Surface Quality of Knot Defects on Eucalypt Veneers by Near Infrared Spectroscopy Coupled with Soft Independent Modeling of Class Analogy

作者：Yang, Zhong[1] Zhang, Maomao[1] Chen, Ling[1] Lv, Bin[1]

第一作者：杨忠

通信作者：Yang, Z[1]

机构：[1]Chinese Acad Forestry, Res Inst Wood Ind, Beijing 100091, Peoples R China

年份：2015

卷号：10

期号：2

起止页码：3314-3325

外文期刊名：BIORESOURCES

收录：;EI(收录号：20153601249054);Scopus(收录号：2-s2.0-84940850679);WOS:【SCI-EXPANDED(收录号:WOS:000354642000107)】；

基金：The authors are grateful for the support of the China National Natural Science Fund (Grant. No. 31370711).

语种：英文

外文关键词：Non-contact detection; Knot defects; Eucalypt; Veneer; Near infrared spectroscopy; Soft independent modeling of class analogy (SIMCA)

摘要：A knot is a natural defect that degrades the quality of softwood and hardwood veneer. To improve efficiency, the plywood industry needs a rapid, inexpensive method of knot identification that is easy to operate and industrialize. Although a non-contact knot-detection technology based on NIR spectroscopy and soft independent modeling of class analogy (SIMCA) has been successful in detecting softwood knots, it has not yet been explored in eucalypt (hardwood) veneer. This study investigated the interaction between knot size, spectral pretreatment methods, and wavelength range selections on this model's classification accuracy of knots and normal eucalypt wood. The study found that classification results were accurate up to 94.4% for large knot samples (10 to 15 mm in diameter) and up to 100% for knot-free samples. Spectral data for small knots (< 5 mm in diameter) impeded the model's classification accuracy because of confusion between small knots and both large knots and normal wood. Calibration models developed with second-derivative spectra exhibited the highest accuracy, followed by models built with first-derivative spectra, models based on spectra transformed by vector normalization, and the model based on the raw spectroscopy. Wavelength ranges of 1100 to 2500 nm enabled greater classification accuracy than wavelength ranges of 780 to 1100 nm or 780 to 2500 nm.