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首页> 外文期刊>Journal of Wood Chemistry and Technology >Determination of Quality Parameters in Moist Wood Chips by Near Infrared Spectroscopy Combining PLS-DA and Support Vector Machines
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Determination of Quality Parameters in Moist Wood Chips by Near Infrared Spectroscopy Combining PLS-DA and Support Vector Machines

机译：结合PLS-DA和支持向量机的近红外光谱法测定湿木片中的质量参数

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摘要

In this work, studies are described using near infrared spectroscopy and chemometrics for determination of quality parameters in moist wood chips, such as basic density, lignin content, and extractives. A classification model using partial least squares-discriminant analysis (PLS-DA) was developed to determine the level of moisture in the samples. Then, for each moisture level, a calibration model was built for quality parameter predictions using least squares support vector machines (LS-SVM)., Multivariate calibration was performed for a data set of 92 wood chip samples. The PLS-DA algorithm was able to classify the samples in the correct class with a small error (lower than 6%) and it was possible to develop a LS-SVM model for quality parameter determination for each class of moisture content with only a few samples and with average relative errors comparable to those obtained by conventional analysis.
机译：在这项工作中，使用近红外光谱和化学计量学描述了确定潮湿木片中质量参数（如基本密度，木质素含量和提取物）的研究。建立了使用偏最小二乘判别分析（PLS-DA）的分类模型来确定样品中的水分含量。然后，针对每个湿度水平，使用最小二乘支持向量机（LS-SVM）建立用于质量参数预测的校准模型，并对92个木片样品的数据集进行多元校准。 PLS-DA算法能够以很小的误差（低于6％）将样品分类为正确的类别，并且有可能开发出LS-SVM模型来确定每种水分含量的质量参数，而只需很少样品，平均相对误差可与常规分析相比。

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