Abstract
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 (>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.
Highlights
Particleboard is a panel product manufactured from lignocellulosic materials, combined with an adhesive system and bonded together under heat and pressure
Functional, and environmental-friendly; they serve as sustainable raw materials for manufacturing particleboards. e type of raw resources used for panels manufacturing, its quality, size of particles, moisture content as well as type and amount of bonding system have significant effect on particleboard properties [12]. e most important quality assessment aspects of manufactured panels are emissions, which mainly depends on the type and amount of resin [1]. e recent trend to reduce the formaldehyde release from manufactured wood products has led to the substitution of ureaformaldehyde (UF) resin with several alternatives [12]
Four different types of particleboards manufactured in the laboratory and in industrial plants were evaluated. e materials differed in terms of panel type, composition, and the adhesive system (ureaformaldehyde (UF) resin, UF resin modified with liquefied wood (LW), or UF resin modified with starch)
Summary
Particleboard is a panel product manufactured from lignocellulosic materials, combined with an adhesive system and bonded together under heat and pressure. Meder et al [19] reported successful implementation of FT-NIR spectroscopy for at-line measurement for quality control of melamine-urea-formaldehyde resin in composite wood-panel production. LDA is a probabilistic method, which assumes that each sample belonging to a particular class follows a multivariate Gaussian distribution It requires the explicit calculation of this probability for the formulation of the classification rule. Soft independent modelling of class analogy (SIMCA) is the most common supervised modelling method, representing class-modelling approach It requires a training data set of samples with a set of attributes and their class membership. Good separation is achieved by the hyperplane that has the largest distance to the nearest training data point of any class [24] It works by obtaining the optimal boundary of two groups in a vector space independent of the probabilistic arrangements of vectors in training set. A two-level expert system is proposed here for identification of particleboard panels with a portable, commercially available NIR spectrometer. e overall goal of this work was to develop a prototype system that might assist quality control and traceability of particleboard panels on the production floor
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