光谱学与光谱分析, 2020, 40 (9): 2937, 网络出版: 2020-12-02  

IPSO-BP木材绝干密度近红外光谱预测模型

Prediction Model of Wood Absolute Dry Density by Near-Infrared Spectroscopy Based on IPSO-BP
作者单位
东北林业大学机电工程学院, 黑龙江 哈尔滨 150040
摘要
木材密度决定着木材力学性能, 是木材物理性能的重要指标之一。 近年来, 由于近红外光谱分析具有操作过程简单、 方便、 快速等优势, 已有学者应用近红外光谱数据预测木材密度, 但是, 在实际应用中, 数据集缺乏、 光谱特征欠优、 非线性拟合准确性低等问题还没有得到完全解决, 木材密度预测模型的精度有待于进一步提升。 木材密度中, 木材的绝干密度相对稳定, 测量结果相对精确, 以柞木绝干密度预测为研究对象, 通过采集不同含水率下的近红外光谱信息构建出适合不同含水率条件的绝干密度非线性预测模型。 首先, 选用德国INSION公司的近红外光纤光谱仪, 运用SPEC view 7.1软件对不同含水率的柞木样本采集光谱信息; 然后, 利用SPXY样本划分方法, 按2∶1划分校正集与预测集, 并利用多元散射校正、 二阶导数光谱及S-G平滑方法进行光谱预处理, 以减少散射光和高频噪声的影响; 接着, 运用连续投影算法(SPA)提取有效的波长信息; 最后, 运用一种非线性权重粒子群算法优化的BP网络(IPSO-BPNN)建立不同含水率状态下的近红外光谱与柞木绝干密度之间的关联, 实现柞木绝干密度预测模型的构建。 实验过程中, 加工选取了100个柞木试件样本, 在绝干条件下测量样本试件密度和光谱信息, 并浸泡水中测量出不同含水率对应的光谱信息, 实验结果表明: SPXY保证了校正集样本的均匀分布, 提高了模型泛化能力; MSC、 二阶导数和S-G卷积平滑相结合的方法抑制了原始光谱中噪声高频信号, 同时使得峰值更加突出; SPA从117个光谱数据中优选出16个特征波长, 提高了建模精度; IPSO-BPNN模型较SPA-PLS, BP和PSO-BP具有更高的相关系数, 更小的均方根误差, 柞木绝干密度预测相关系数为0.938, 预测均方根误差为0.012 9, 方法可以对木材密度在线无损测量提供一定的理论基础。
Abstract
Wood density is an important physical property, which determines the mechanical properties of wood. In recent years, as NIR has the advantages of simple, convenient and fast operation, it has already been used in terms of wood density prediction. However, in practical application, the sample sets shortage, spectral characteristics selection and non-linear fitting inaccuracy still not been solved definitely, and the accuracy of wood density prediction model needs to be further improved. Among all the wood density parameters, the absolute dry density of wood is relatively stable, and the measurement results are relatively accurate. In this paper, the prediction of absolute dry density of oak is studied. By collecting spectroscopy information under different moisture content, a non-linear prediction model of absolute drying density suitable for arbitrary moisture content is constructed. The near-infrared optical fiber spectrometer of INSION Company in Germany was selected, and the spectral information of oak samples with different moisture content was collected by SPEC view 7.1 software. Then, the calibration set and prediction set were divided according to 2∶1 using SPXY sample partition method, and multivariate scattering correction, second derivative spectroscopy and S-G smoothing method were used to reduce the influence of scattered light and high-frequency noise; After that, continuous projection algorithm SPA was used to extract effective wavelength information; finally, a BP network (IPSO-BPNN) was used to establish the correlation between near-infrared spectra and oak absolute dry density under different moisture content, which was optimized by a non-linear weighted particle swarm optimization algorithm here. The density and spectral information of 100 samples of oak wood was obtained under absolute drying condition, and the spectral information was collected corresponding to different moisture content. The experimental results show that SPXY guarantees the uniform distribution of calibration samples and improves the generalization ability of the model; Using a combination of MSC, second derivative and S-G convolution can smoothly suppressthe high-frequency noise signal in the original spectrum and make the peak value more prominent;16 characteristic wavelengths were selected by SPA from 117 spectral data. Generally, IPSO-BPNN model has a higher correlation coefficient than SPA-PLS, BP and PSO-BP, own smaller root mean square error. The correlation coefficient of the absolute dry density of oak is 0.938, and the root mean square error is 0.012 9.

于雷, 陈金浩, 李龙飞, 李超, 张怡卓. IPSO-BP木材绝干密度近红外光谱预测模型[J]. 光谱学与光谱分析, 2020, 40(9): 2937. YU Lei, CHEN Jin-hao, LI Long-fei, LI Chao, ZHANG Yi-zhuo. Prediction Model of Wood Absolute Dry Density by Near-Infrared Spectroscopy Based on IPSO-BP[J]. Spectroscopy and Spectral Analysis, 2020, 40(9): 2937.

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