To implement the discovery of discarded anti-corrosion wood, an automatic sorting robot system was built. Three kinds of commonly used wood were selected as the research object, which uses hyperspectral imaging technology to achieve the identification. In the range of 900–1700 nm (230 bands), the infrared spectra of three kinds of anti-corrosion wood were collected, and then the characteristic information was obtained through the analysis of MATLAB to distinguish them. Among them, three kinds of preservative woods are Scots pine (add CCA treatment), Pseudotsuga menziesii (high-temperature carbonization treatment) and Incense Cedar (pressurized treatment). After the pretreatment by the Savitzky-Golay method, spectral data were conducted by principal component analysis (PCA), and the contribution rate of the first three principal components reached 99.902 %. Besides, through the loading coefficients of the first three principal components that were plotted on the wavelength, we obtained five characteristic wavelengths and corresponding reflectance information, simultaneously; this set up a typical discriminant analysis model. Then, the model was validated by the validation set, and the accuracy rate of the prediction set was 98.89 %. This method can effectively identify and classify three kinds of anti-corrosion wood, which can provide a scientific method and basis for a solid waste sorting system.

为了实现发现废弃防腐木，建立了一个自动分拣机器人系统。选择了三种常用木材作为研究对象，利用高光谱成像技术进行了鉴定。在900-1700 nm（230波段）范围内，收集了三种防腐木材的红外光谱，然后通过MATLAB分析获得了特征信息以区分它们。其中，三种防腐木是苏格兰松木（添加CCA处理），假单胞菌Menziesii（高温碳化处理）和香柏木（加压处理）。通过Savitzky-Golay方法进行预处理后，通过主成分分析（PCA）进行光谱数据分析，前三个主成分的贡献率达到99.902％。此外，通过绘制在波长上的前三个主成分的负载系数，我们同时获得了五个特征波长和相应的反射率信息；这建立了一个典型的判别分析模型。然后，通过验证集对模型进行验证，预测集的准确率为98.89％。该方法可以有效地对三种防腐木进行识别和分类，为固体废物分类系统提供科学的方法和依据。同时; 这建立了一个典型的判别分析模型。然后，通过验证集对模型进行验证，预测集的准确率为98.89％。该方法可以有效地对三种防腐木进行识别和分类，为固体废物分类系统提供科学的方法和依据。同时; 这建立了一个典型的判别分析模型。然后，通过验证集对模型进行验证，预测集的准确率为98.89％。该方法可以有效地对三种防腐木进行识别和分类，为固体废物分类系统提供科学的方法和依据。