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Determination of Hemicellulose Cellulose and Lignin in Moso Bamboo by Near Infrared Spectroscopy

机译:近红外光谱法测定毛竹中的半纤维素纤维素和木质素

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摘要

The contents of hemicellulose, cellulose and lignin are important for moso bamboo processing in biomass energy industry. The feasibility of using near infrared (NIR) spectroscopy for rapid determination of hemicellulose, cellulose and lignin was investigated in this study. Initially, the linear relationship between bamboo components and their NIR spectroscopy was established. Subsequently, successive projections algorithm (SPA) was used to detect characteristic wavelengths for establishing the convenient models. For hemicellulose, cellulose and lignin, 22, 22 and 20 characteristic wavelengths were obtained, respectively. Nonlinear determination models were subsequently built by an artificial neural network (ANN) and a least-squares support vector machine (LS-SVM) based on characteristic wavelengths. The LS-SVM models for predicting hemicellulose, cellulose and lignin all obtained excellent results with high determination coefficients of 0.921, 0.909 and 0.892 respectively. These results demonstrated that NIR spectroscopy combined with SPA-LS-SVM is a useful, nondestructive tool for the determinations of hemicellulose, cellulose and lignin in moso bamboo.
机译:半纤维素,纤维素和木质素的含量对于生物质能源工业中的毛竹加工非常重要。在这项研究中调查了使用近红外(NIR)光谱法快速测定半纤维素,纤维素和木质素的可行性。最初,建立了竹子成分与其近红外光谱之间的线性关系。随后,使用连续投影算法(SPA)检测特征波长,以建立方便的模型。对于半纤维素,纤维素和木质素,分别获得了22、22和20个特征波长。随后,基于特征波长,通过人工神经网络(ANN)和最小二乘支持向量机(LS-SVM)建立了非线性确定模型。用于预测半纤维素,纤维素和木质素的LS-SVM模型均获得了优异的结果,分别具有0.921、0.909和0.892的高测定系数。这些结果表明,NIR光谱结合SPA-LS-SVM是测定毛竹中半纤维素,纤维素和木质素的有用,无损的工具。

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