Comparison of classification methods performance for defining the best reuse of waste wood material using NIR spectroscopy
Open accessAbstract
Recycling of post-consumer waste wood material is becoming an increasingly appealing alternative to disposal. However, its huge heterogeneity is calling for an assessment of the material characteristics in order to define the best recycling option and intended reuse. In fact, waste wood comes into a variety of uses/types of wood, along with several levels of contamination, and it can be divided into different categories based on its composition and quality grade. This study provides the measurement of more than a hundred waste wood samples and their characterisation using a hand-held NIR spectrophotometer. Three classification methods, i.e. K-nearest Neighbours (KNN), Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA) and PCA-KNN, have been compared to develop models for the sorting of waste wood in quality categories according to the best-suited reuse. In addition, the classification performance has been investigated as a function of the number of the spectral measurements of the sample and as the average of the spectral measurements. The results showed that PCA-KNN performs better than the other classification methods, especially when the material is ground to 5 cm of particle size and the spectral measurements are averaged across replicates (classification accuracy: 90.9 %). NIR spectroscopy, coupled with chemometrics, turned out to be a promising tool for the real-time sorting of waste wood material, ensuring a more accurate and sustainable waste wood management. Obtaining real-time information about the quality and characteristics of waste wood material translates into a decision of the best recycling option, increasing its recycling potential.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
Fisher, R. (1936) · Annals of Eugenics · doi:10.1111/j.1469-1809.1936.tb02137.x · Cited by 16,063 · see citing works
LIII. On lines and planes of closest fit to systems of points in space
Karl Pearson, F. R. S. (1901) · The London Edinburgh and Dublin Philosophical Magazine and Journal of Science · doi:10.1080/14786440109462720 · Cited by 13,658 · see citing works
An Introduction to Statistical Learning
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106 · see citing works
Pattern Recognition and Neural Networks
Ripley, B. D. (1996) · Cambridge University Press eBooks · doi:10.1017/cbo9780511812651 · Cited by 6,446 · see citing works
Computer Aided Design of Experiments
Kennard, R. W., & Stone, L. A. (1969) · Technometrics · doi:10.1080/00401706.1969.10490666 · Cited by 3,498 · see citing works
Review of the most common pre-processing techniques for near-infrared spectra
Rinnan, Å., van den Berg, F. W. J., & Engelsen, S. B. (2009) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2009.07.007 · Cited by 3,088 · see citing works
Self-citationAn overview of the chemical composition of biomass
Vassilev, S. V., Baxter, D., Andersen, L. K., & Vassileva, C. G. (2010) · Fuel · doi:10.1016/j.fuel.2009.10.022 · Cited by 2,519 · see citing works
An Introduction to Statistical Learning
James, G. M., Witten, D., Hastie, T. J., & Tibshirani, R. J. (2021) · Springer texts in statistics · doi:10.1007/978-1-0716-1418-1 · Cited by 1,790 · see citing works
Nearest neighbor (NN) norms: NN pattern classification techniques
Dasarathy, B. V. (1991) · Medical Entomology and Zoology · Cited by 1,649 · see citing works
Chemical properties of solid biofuels—significance and impact
Obernberger, I., Brunner, T., & Bärnthaler, G. (2006) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2006.06.011 · Cited by 774 · see citing works
A Review of Band Assignments in near Infrared Spectra of Wood and Wood Components
Schwanninger, M., Rodrigues, J. C., & Fackler, K. (2011) · Journal of Near Infrared Spectroscopy · doi:10.1255/jnirs.955 · Cited by 675 · see citing works
A review on automated sorting of source-separated municipal solid waste for recycling
Gundupalli, S. P., Hait, S., & Thakur, A. (2017) · Waste Management · doi:10.1016/j.wasman.2016.09.015 · Cited by 583 · see citing works
Lignocellulose biodegradation: Fundamentals and applications
Malherbe, S., & Cloete, T. E. (2002) · Reviews in Environmental Science and Bio/Technology · doi:10.1023/a:1020858910646 · Cited by 485 · see citing works
The distance function effect on k-nearest neighbor classification for medical datasets
Hu, L., Huang, M., Ke, S., & Tsai, C. (2016) · SpringerPlus · doi:10.1186/s40064-016-2941-7 · Cited by 455 · see citing works
Faraca, G., Boldrin, A., & Astrup, T. (2019) · Waste Management · doi:10.1016/j.wasman.2019.02.005 · Cited by 125 · see citing works
Energy and carbon balances of wood cascade chains
Sathre, R., & Gustavsson, L. (2006) · Resources, Conservation and Recycling · doi:10.1016/j.resconrec.2005.12.008 · Cited by 124 · see citing works
State-of-the-art of waste wood supply chain in Germany and selected European countries
Garcia, C. A., & Hora, G. (2017) · Waste Management · doi:10.1016/j.wasman.2017.09.025 · Cited by 93 · see citing works
Dynamic accounting of greenhouse gas emissions from cascading utilisation of wood waste
Faraca, G., Tonini, D., & Astrup, T. F. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2018.10.136 · Cited by 88 · see citing works
Xiao, W. F., Yang, J., Fang, H., et al. (2019) · Waste Management · doi:10.1016/j.wasman.2019.04.036 · Cited by 84 · see citing works
Different Facets of Lignocellulosic Biomass Including Pectin and Its Perspectives
Shrestha, S., Kognou, A. L. M., Zhang, J., & Qin, W. (2020) · Waste and Biomass Valorization · doi:10.1007/s12649-020-01305-w · Cited by 74 · see citing works
Clustering Challenges in Biological Networks
Butenko, S. I., Chaovalitwongse, W. A., & Pardalos, P. M. (2009) · WORLD SCIENTIFIC eBooks · doi:10.1142/6602 · Cited by 71 · see citing works
Use of NIR for structural characterization of urea–formaldehyde resins
Minopoulou, E., Dessipri, E., Chryssikos, G. D., et al. (2003) · International Journal of Adhesion and Adhesives · doi:10.1016/s0143-7496(03)00089-7 · Cited by 59 · see citing works
Multi-modal sorting in plastic and wood waste streams
Konstantinidis, F. K., Sifnaios, S., Arvanitakis, G., Tsimiklis, G., Mouroutsos, S. G., Amditis, A., & Gasteratos, A. C. (2023) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2023.107244 · Cited by 54 · see citing works
Edo, M., Björn, E., Persson, P.-E., & Jansson, S. (2016) · Waste Management · doi:10.1016/j.wasman.2015.11.048 · Cited by 52 · see citing works
Energy recovery of glued wood waste – A review
Cesprini, E., Resente, G., Causin, V., Urso, T., Cavalli, R., & Zanetti, M. (2020) · Fuel · doi:10.1016/j.fuel.2019.116520 · Cited by 50 · see citing works
Assessment of the coherence of the Swiss waste wood management
C. Bergeron, F. (2014) · Resources, Conservation and Recycling · doi:10.1016/j.resconrec.2014.07.011 · Cited by 44 · see citing works
Hein, P. R. G., Campos, A. C. M., Mendes, R. F., et al. (2010) · European Journal of Wood and Wood Products · doi:10.1007/s00107-010-0471-5 · Cited by 42 · see citing works
Furniture wood wastes: Experimental property characterisation and burning tests
Tatàno, F., Barbadoro, L., Mangani, G., et al. (2009) · Waste Management · doi:10.1016/j.wasman.2009.06.012 · Cited by 36 · see citing works
Huron, M., Oukala, S., Lardière, J., Giraud, N., & Dupont, C. (2017) · Fuel · doi:10.1016/j.fuel.2017.04.025 · Cited by 36 · see citing works
Mancini, M., Rinnan, Å., Pizzi, A., Mengarelli, C., Rossini, G., Duca, D., & Toscano, G. (2018) · Fuel · doi:10.1016/j.fuel.2018.01.008 · Cited by 33 · see citing works
Self-citationLima, M. D. R., Ramalho, F. M. G., Trugilho, P. F., Bufalino, L., Dias Júnior, A. F., Protásio, T. D. P., & Hein, P. R. G. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.05.048 · Cited by 28 · see citing works
Gonçalves, M., Paiva, N. T., Ferra, J. M. M., et al. (2019) · International Journal of Adhesion and Adhesives · doi:10.1016/j.ijadhadh.2019.01.021 · Cited by 27 · see citing works
Waste wood characterization and combustion behaviour in pilot lab scale
Gehrmann, H.-J., Mätzing, H., Nowak, P., Baris, D., Seifert, H., Dupont, C., Defoort, F., Peyrot, M., & Castagno, F. (2020) · Journal of the Energy Institute · doi:10.1016/j.joei.2020.02.001 · Cited by 25 · see citing works
Determination of formaldehyde/urea molar ratio in amino resins by near‐infrared spectroscopy
Henriques, A., Cruz, P., Martins, J. M., et al. (2011) · Journal of Applied Polymer Science · doi:10.1002/app.35128 · Cited by 24 · see citing works
Shahidul, M. I., Malcolm, M. L., Hashmi, M. S. J., & Alhaji, M. H. (2020) · Encyclopedia of Renewable and Sustainable Materials · doi:10.1016/b978-0-12-803581-8.11275-5 · Cited by 23 · see citing works
Mancini, M., & Rinnan, Å. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.05.137 · Cited by 22 · see citing works
Self-citationMancini, M., & Rinnan, Å. (2023) · Analytica Chimica Acta · doi:10.1016/j.aca.2023.341564 · Cited by 22 · see citing works
Self-citationLesar, B., Humar, M., & Hora, G. (2018) · Waste Management · doi:10.1016/j.wasman.2018.08.002 · Cited by 21 · see citing works
Wan, Z., Yang, H. W., Xu, J., et al. (2023) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2023.104920 · Cited by 11 · see citing works
Study of Variability of Waste Wood Samples Collected in a Panel Board Industry
Mancini, M., & Rinnan, Å. (2021) · Frontiers in Chemistry · doi:10.3389/fchem.2021.722090 · Cited by 5 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- Deroubaix, G., 2013. Optimisation of material recycling and energy recovery from waste and demolition wood in different value chains (FCBA). 5th WWNet Symposium (2013) (February) 235066.
- Mantau, U., Saal, U., Prins, K., Steierer, F., Lindner, M., Verkerk, H., Eggers, J., Leek, N., Oldenburger, J., Asikainen, A., 2010. EUwood–real potential for changes in growth and use of EU forests. Final report. Hamburg/Germany, June 2010. 165 p. 10.13140/2.1.3372.0642. · DOI
- Vis, M., Mantau, U., Allen, B., Essel, R., Reichenbach, J., 2016. CASCADES, Study on the optimised cascading use of wood. Vol. no 394/PP/. Publications Office. Doi: 10.2873/827106. · DOI
Cited by
Still being checked.
- Authors
- Mancini, M., Taavitsainen, V.-M., & Rinnan, Å.
- Affiliations
- Marche Polytechnic University (IT)
- Lappeenranta-Lahti University of Technology (FI)
- University of Copenhagen (DK)
- Corresponding author
- Mancini, Manuela
- Venue
- Waste Management
- Publisher
- Elsevier BV
- Published
- 2024-04
- Volume
- 178
- Pages
- 321-330
- ISSN
- 0956-053X (ISSN-L)
- OpenAlex
- W4392438809
- Cited by
- 21
- References
- 40
- Citations by year
- 2026: 9 · 2025: 7 · 2024: 5
- FWCI
- 3.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Waste Management (publishedVersion, cc-by)
- SSRN Electronic Journal (acceptedVersion)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- Università Politecnica delle Marche (Università Politecnica delle Marche) (submittedVersion, cc-by)
- Funders
- Horizon 2020 Framework Programme
- Horizon 2020
- Horizon 2020 Marie Skłodowska-Curie Actions (838560)
- Topics
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Physical Sciences › Environmental Science › Waste Management and Disposal › Municipal Solid Waste Management
- Social Sciences › Arts and Humanities › Archeology › Cultural Heritage Materials Analysis
- Keywords
- wood waste, wood recycling, near-infrared spectroscopy, classification methods, k-nearest neighbors, PCA-LDA, real-time sorting, timber grading, particle size, chemometrics
- MeSH terms
- Waste Products, Wood, Discriminant Analysis, Waste Management, Spectroscopy, Near-Infrared, Recycling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
