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|Type:||Artigo de periódico|
|Title:||Determination of Quality Parameters in Moist Wood Chips by Near Infrared Spectroscopy Combining PLS-DA and Support Vector Machines|
|Abstract:||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 squaresdiscriminant 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.|
|Subject:||Near infrared spectroscopy|
support vector mach- ines
partial least squares
|Editor:||Taylor & Francis Inc|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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