Journal article Open Access

# Prototype of the Near-Infrared Spectroscopy Expert System for Particleboard Identification

Anna Sandak; Jakub Sandak; Dominika Janiszewska; Salim Hiziroglu; Marta Petrillo; Paolo Grossi

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<dc:creator>Anna Sandak</dc:creator>
<dc:creator>Jakub Sandak</dc:creator>
<dc:creator>Dominika Janiszewska</dc:creator>
<dc:creator>Salim Hiziroglu</dc:creator>
<dc:creator>Marta Petrillo</dc:creator>
<dc:creator>Paolo Grossi</dc:creator>
<dc:date>2018-09-30</dc:date>
<dc:description>The overall goal of this work was to develop a prototype expert system assisting quality control and traceability of particleboard panels on the production floor. Four different types of particleboards manufactured at the laboratory scale and in industrial plants were evaluated. The material differed in terms of panel type, composition, and adhesive system. NIR spectroscopy was employed as a pioneer tool for the development of a two-level expert system suitable for classification and traceability of investigated samples. A portable, commercially available NIR spectrometer was used for nondestructive measurements of particleboard panels. Twenty-five batches of particleboards, each containing at least three independent replicas, was used for the original system development and assessment of its performance. Four alternative chemometric methods (PLS-DA, kNN, SIMCA, and SVM) were used for spectroscopic data classification. The models were developed for panel recognition at two levels differing in terms of their generality. In the first stage, four among twenty-four tested combinations resulted in 100% correct classification. Discrimination precision with PLS-DA and SVMC was high (&gt;99%), even without any spectra preprocessing. SNV preprocessed spectra and SVMC algorithm were used at the second stage for panel batch classification. Panels manufactured by two producers were 100% correctly classified, industrial panels produced by different manufacturing plants were classified with 98.9% success, and the experimental panels manufactured in the laboratory were classified with 63.7% success. Implementation of NIR spectroscopy for wood-based product traceability and quality control may have a great impact due to the high versatility of the production and wide range of particleboards utilization.</dc:description>
<dc:identifier>https://zenodo.org/record/1439398</dc:identifier>
<dc:identifier>10.1155/2018/6025163</dc:identifier>
<dc:identifier>oai:zenodo.org:1439398</dc:identifier>
<dc:relation>info:eu-repo/grantAgreement/EC/H2020/739574/</dc:relation>
<dc:relation>url:https://zenodo.org/communities/innorenew</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:title>Prototype of the Near-Infrared Spectroscopy Expert System for Particleboard Identification</dc:title>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>publication-article</dc:type>
</oai_dc:dc>

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