‘Tracheid effect’ light scattering response of common commercial species of Malaysia using red laser (650 nm) for grain angle detection, and machine learning based performance prediction

Abstract ’Tracheid effect’, or laser light scattering in wood is an important phenomenon in the study of wood, but not well researched for Malaysian timber species. Sixty (60) common commercial timber species of Malaysia, as defined by the Forest Research Institute Malaysia (FRIM), were tested for ’tracheid effect’ response using 650 nm (red) laser. 5 samples each of 11 heavy hardwood, 15 medium hardwood, 32 light hardwood, and 2 softwood species were tested. Medium density fibreboard (MDF) was used as a reference. The grain angle performance metric, which is the root mean squared error (RMSE) between the observed and actual angles of ellipse generated by the dot laser and imaged by the camera, was determined and tabulated. To predict the grain angle performance for unknown species using colour and density features, two machine learning (ML) classification approaches were tested, namely k-Nearest Neighbour (k-NN), and shallow feed-forward artificial neural network (ANN), as well as one function-fitting ANN. For predicting RMSE $$&lt;5^\circ$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>&lt;</mml:mo> <mml:msup> <mml:mn>5</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> </mml:mrow> </mml:math> , the function-fitting ANN performed the best at 82.7%, while k-NN scored the highest overall performance of 89.3% when predicting RMSE $$&lt;10^\circ$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>&lt;</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> </mml:mrow> </mml:math> . The density of wood did not directly correlate with the grain angle performance, but its inclusion as a feature together with the colour features improved the accuracy of the ML predictions. The colour features related to brightness were dominant features that affected performance. In summary, this study confirmed that wood colour as well as density plays an important role in the ability to determine grain angle by means of the tracheid effect using 650 nm lasers.

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Journal
Wood Science and Technology
Published
2025-05-07
DOI
https://doi.org/10.1007/s00226-025-01653-7
Citations
1
Primary Topic
Spectroscopy and Chemometric Analyses
Type
article
Field-Weighted Citation Impact
0.75

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article

‘Tracheid effect’ light scattering response of common commercial species of Malaysia using red laser (650 nm) for grain angle detection, and machine learning based performance prediction

Zuriani Usop, Jin Xue Soo, Siew‐Cheok Ng, Shigenobu Ogata et al.
1 citations
Wood Science and Technology
Spectroscopy and Chemometric Analyses
0.75
article

‘Tracheid effect’ light scattering response of common commercial species of Malaysia using red laser (650 nm) for grain angle detection, and machine learning based performance prediction

Zuriani Usop, Jin Xue Soo, Siew‐Cheok Ng, Shigenobu Ogata, Mohd ‘Akashah Fauthan, Shaer Jin Liew, Hwa Jen Yap, Chiat Oon Tan
article en
1 citations

Abstract

Abstract ’Tracheid effect’, or laser light scattering in wood is an important phenomenon in the study of wood, but not well researched for Malaysian timber species. Sixty (60) common commercial timber species of Malaysia, as defined by the Forest Research Institute Malaysia (FRIM), were tested for ’tracheid effect’ response using 650 nm (red) laser. 5 samples each of 11 heavy hardwood, 15 medium hardwood, 32 light hardwood, and 2 softwood species were tested. Medium density fibreboard (MDF) was used as a reference. The grain angle performance metric, which is the root mean squared error (RMSE) between the observed and actual angles of ellipse generated by the dot laser and imaged by the camera, was determined and tabulated. To predict the grain angle performance for unknown species using colour and density features, two machine learning (ML) classification approaches were tested, namely k-Nearest Neighbour (k-NN), and shallow feed-forward artificial neural network (ANN), as well as one function-fitting ANN. For predicting RMSE $$&lt;5^\circ$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>&lt;</mml:mo> <mml:msup> <mml:mn>5</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> </mml:mrow> </mml:math> , the function-fitting ANN performed the best at 82.7%, while k-NN scored the highest overall performance of 89.3% when predicting RMSE $$&lt;10^\circ$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>&lt;</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> </mml:mrow> </mml:math> . The density of wood did not directly correlate with the grain angle performance, but its inclusion as a feature together with the colour features improved the accuracy of the ML predictions. The colour features related to brightness were dominant features that affected performance. In summary, this study confirmed that wood colour as well as density plays an important role in the ability to determine grain angle by means of the tracheid effect using 650 nm lasers.

Wood Science and TechnologyVol. 59(4)
Sirim Berhad (MY), University of Malaya (MY), University of Kuala Lumpur (MY), The University of Osaka (JP)
Japan Society for the Promotion of Science
Openalex Percentile: Top 33%
Spectroscopy and Chemometric Analyses
0.75
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