Wet-pocket classification in Abies lasiocarpa using spectroscopy in the visible and near infrared range
Abstract The potential of visible and near infrared (Vis-NIR) spectroscopy to distinguish wet-pockets from normal subalpine fir (Abies lasiocarpa Hook) wood was evaluated. Two specimen classes were used, namely, wood with more than half of the surfaces covered by wet-pockets (WW), and wood completel...
Ausführliche Beschreibung
Autor*in: |
Watanabe, Ken [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2010 |
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Schlagwörter: |
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Anmerkung: |
© Springer-Verlag 2010 |
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Übergeordnetes Werk: |
Enthalten in: Holz als Roh- und Werkstoff - Springer-Verlag, 1937, 70(2010), 1-3 vom: 16. Okt., Seite 61-67 |
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Übergeordnetes Werk: |
volume:70 ; year:2010 ; number:1-3 ; day:16 ; month:10 ; pages:61-67 |
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DOI / URN: |
10.1007/s00107-010-0490-2 |
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Katalog-ID: |
OLC209642710X |
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520 | |a Abstract The potential of visible and near infrared (Vis-NIR) spectroscopy to distinguish wet-pockets from normal subalpine fir (Abies lasiocarpa Hook) wood was evaluated. Two specimen classes were used, namely, wood with more than half of the surfaces covered by wet-pockets (WW), and wood completely free of wet-pockets (NW). A partial least square (PLS) regression model was derived and calibrated to predict moisture content ranging from 0 to 210%, and its usefulness for moisture-based sorting of green lumber was assessed. Samples were sorted into the two classes after Vis-NIR scanning via two models: (1) soft independent modeling of class analogy (SIMCA) and (2) PLS discriminant analysis. The SIMCA model using second derivatives and wavelengths spanning 650 to 1150 nm successfully classified 98% of WW and NW in the green state, while it resulted in misclassification of 96% of the specimens after air-drying. The discriminant PLS model using wavelengths spanning 650–1150 nm, correctly classified WW and NW 96% in the green state and 100% after air-drying, respectively. These results clearly demonstrate the applicability of Vis-NIR spectroscopy to discriminate wet-pockets from normal wood. | ||
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10.1007/s00107-010-0490-2 doi (DE-627)OLC209642710X (DE-He213)s00107-010-0490-2-p DE-627 ger DE-627 rakwb eng 670 VZ 670 VZ 23 ssgn Watanabe, Ken verfasserin aut Wet-pocket classification in Abies lasiocarpa using spectroscopy in the visible and near infrared range 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2010 Abstract The potential of visible and near infrared (Vis-NIR) spectroscopy to distinguish wet-pockets from normal subalpine fir (Abies lasiocarpa Hook) wood was evaluated. Two specimen classes were used, namely, wood with more than half of the surfaces covered by wet-pockets (WW), and wood completely free of wet-pockets (NW). A partial least square (PLS) regression model was derived and calibrated to predict moisture content ranging from 0 to 210%, and its usefulness for moisture-based sorting of green lumber was assessed. Samples were sorted into the two classes after Vis-NIR scanning via two models: (1) soft independent modeling of class analogy (SIMCA) and (2) PLS discriminant analysis. The SIMCA model using second derivatives and wavelengths spanning 650 to 1150 nm successfully classified 98% of WW and NW in the green state, while it resulted in misclassification of 96% of the specimens after air-drying. The discriminant PLS model using wavelengths spanning 650–1150 nm, correctly classified WW and NW 96% in the green state and 100% after air-drying, respectively. These results clearly demonstrate the applicability of Vis-NIR spectroscopy to discriminate wet-pockets from normal wood. Partial Little Square Partial Less Square Discriminant Analysis Green State Normal Wood Partial Less Square Regression Model Mansfield, Shawn D. aut Avramidis, Stavros aut Enthalten in Holz als Roh- und Werkstoff Springer-Verlag, 1937 70(2010), 1-3 vom: 16. Okt., Seite 61-67 (DE-627)129594962 (DE-600)240590-8 (DE-576)015087867 0018-3768 nnns volume:70 year:2010 number:1-3 day:16 month:10 pages:61-67 https://doi.org/10.1007/s00107-010-0490-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OPC-FOR AR 70 2010 1-3 16 10 61-67 |
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10.1007/s00107-010-0490-2 doi (DE-627)OLC209642710X (DE-He213)s00107-010-0490-2-p DE-627 ger DE-627 rakwb eng 670 VZ 670 VZ 23 ssgn Watanabe, Ken verfasserin aut Wet-pocket classification in Abies lasiocarpa using spectroscopy in the visible and near infrared range 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2010 Abstract The potential of visible and near infrared (Vis-NIR) spectroscopy to distinguish wet-pockets from normal subalpine fir (Abies lasiocarpa Hook) wood was evaluated. Two specimen classes were used, namely, wood with more than half of the surfaces covered by wet-pockets (WW), and wood completely free of wet-pockets (NW). A partial least square (PLS) regression model was derived and calibrated to predict moisture content ranging from 0 to 210%, and its usefulness for moisture-based sorting of green lumber was assessed. Samples were sorted into the two classes after Vis-NIR scanning via two models: (1) soft independent modeling of class analogy (SIMCA) and (2) PLS discriminant analysis. The SIMCA model using second derivatives and wavelengths spanning 650 to 1150 nm successfully classified 98% of WW and NW in the green state, while it resulted in misclassification of 96% of the specimens after air-drying. The discriminant PLS model using wavelengths spanning 650–1150 nm, correctly classified WW and NW 96% in the green state and 100% after air-drying, respectively. These results clearly demonstrate the applicability of Vis-NIR spectroscopy to discriminate wet-pockets from normal wood. Partial Little Square Partial Less Square Discriminant Analysis Green State Normal Wood Partial Less Square Regression Model Mansfield, Shawn D. aut Avramidis, Stavros aut Enthalten in Holz als Roh- und Werkstoff Springer-Verlag, 1937 70(2010), 1-3 vom: 16. Okt., Seite 61-67 (DE-627)129594962 (DE-600)240590-8 (DE-576)015087867 0018-3768 nnns volume:70 year:2010 number:1-3 day:16 month:10 pages:61-67 https://doi.org/10.1007/s00107-010-0490-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OPC-FOR AR 70 2010 1-3 16 10 61-67 |
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10.1007/s00107-010-0490-2 doi (DE-627)OLC209642710X (DE-He213)s00107-010-0490-2-p DE-627 ger DE-627 rakwb eng 670 VZ 670 VZ 23 ssgn Watanabe, Ken verfasserin aut Wet-pocket classification in Abies lasiocarpa using spectroscopy in the visible and near infrared range 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2010 Abstract The potential of visible and near infrared (Vis-NIR) spectroscopy to distinguish wet-pockets from normal subalpine fir (Abies lasiocarpa Hook) wood was evaluated. Two specimen classes were used, namely, wood with more than half of the surfaces covered by wet-pockets (WW), and wood completely free of wet-pockets (NW). A partial least square (PLS) regression model was derived and calibrated to predict moisture content ranging from 0 to 210%, and its usefulness for moisture-based sorting of green lumber was assessed. Samples were sorted into the two classes after Vis-NIR scanning via two models: (1) soft independent modeling of class analogy (SIMCA) and (2) PLS discriminant analysis. The SIMCA model using second derivatives and wavelengths spanning 650 to 1150 nm successfully classified 98% of WW and NW in the green state, while it resulted in misclassification of 96% of the specimens after air-drying. The discriminant PLS model using wavelengths spanning 650–1150 nm, correctly classified WW and NW 96% in the green state and 100% after air-drying, respectively. These results clearly demonstrate the applicability of Vis-NIR spectroscopy to discriminate wet-pockets from normal wood. Partial Little Square Partial Less Square Discriminant Analysis Green State Normal Wood Partial Less Square Regression Model Mansfield, Shawn D. aut Avramidis, Stavros aut Enthalten in Holz als Roh- und Werkstoff Springer-Verlag, 1937 70(2010), 1-3 vom: 16. Okt., Seite 61-67 (DE-627)129594962 (DE-600)240590-8 (DE-576)015087867 0018-3768 nnns volume:70 year:2010 number:1-3 day:16 month:10 pages:61-67 https://doi.org/10.1007/s00107-010-0490-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OPC-FOR AR 70 2010 1-3 16 10 61-67 |
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Wet-pocket classification in Abies lasiocarpa using spectroscopy in the visible and near infrared range |
abstract |
Abstract The potential of visible and near infrared (Vis-NIR) spectroscopy to distinguish wet-pockets from normal subalpine fir (Abies lasiocarpa Hook) wood was evaluated. Two specimen classes were used, namely, wood with more than half of the surfaces covered by wet-pockets (WW), and wood completely free of wet-pockets (NW). A partial least square (PLS) regression model was derived and calibrated to predict moisture content ranging from 0 to 210%, and its usefulness for moisture-based sorting of green lumber was assessed. Samples were sorted into the two classes after Vis-NIR scanning via two models: (1) soft independent modeling of class analogy (SIMCA) and (2) PLS discriminant analysis. The SIMCA model using second derivatives and wavelengths spanning 650 to 1150 nm successfully classified 98% of WW and NW in the green state, while it resulted in misclassification of 96% of the specimens after air-drying. The discriminant PLS model using wavelengths spanning 650–1150 nm, correctly classified WW and NW 96% in the green state and 100% after air-drying, respectively. These results clearly demonstrate the applicability of Vis-NIR spectroscopy to discriminate wet-pockets from normal wood. © Springer-Verlag 2010 |
abstractGer |
Abstract The potential of visible and near infrared (Vis-NIR) spectroscopy to distinguish wet-pockets from normal subalpine fir (Abies lasiocarpa Hook) wood was evaluated. Two specimen classes were used, namely, wood with more than half of the surfaces covered by wet-pockets (WW), and wood completely free of wet-pockets (NW). A partial least square (PLS) regression model was derived and calibrated to predict moisture content ranging from 0 to 210%, and its usefulness for moisture-based sorting of green lumber was assessed. Samples were sorted into the two classes after Vis-NIR scanning via two models: (1) soft independent modeling of class analogy (SIMCA) and (2) PLS discriminant analysis. The SIMCA model using second derivatives and wavelengths spanning 650 to 1150 nm successfully classified 98% of WW and NW in the green state, while it resulted in misclassification of 96% of the specimens after air-drying. The discriminant PLS model using wavelengths spanning 650–1150 nm, correctly classified WW and NW 96% in the green state and 100% after air-drying, respectively. These results clearly demonstrate the applicability of Vis-NIR spectroscopy to discriminate wet-pockets from normal wood. © Springer-Verlag 2010 |
abstract_unstemmed |
Abstract The potential of visible and near infrared (Vis-NIR) spectroscopy to distinguish wet-pockets from normal subalpine fir (Abies lasiocarpa Hook) wood was evaluated. Two specimen classes were used, namely, wood with more than half of the surfaces covered by wet-pockets (WW), and wood completely free of wet-pockets (NW). A partial least square (PLS) regression model was derived and calibrated to predict moisture content ranging from 0 to 210%, and its usefulness for moisture-based sorting of green lumber was assessed. Samples were sorted into the two classes after Vis-NIR scanning via two models: (1) soft independent modeling of class analogy (SIMCA) and (2) PLS discriminant analysis. The SIMCA model using second derivatives and wavelengths spanning 650 to 1150 nm successfully classified 98% of WW and NW in the green state, while it resulted in misclassification of 96% of the specimens after air-drying. The discriminant PLS model using wavelengths spanning 650–1150 nm, correctly classified WW and NW 96% in the green state and 100% after air-drying, respectively. These results clearly demonstrate the applicability of Vis-NIR spectroscopy to discriminate wet-pockets from normal wood. © Springer-Verlag 2010 |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OPC-FOR |
container_issue |
1-3 |
title_short |
Wet-pocket classification in Abies lasiocarpa using spectroscopy in the visible and near infrared range |
url |
https://doi.org/10.1007/s00107-010-0490-2 |
remote_bool |
false |
author2 |
Mansfield, Shawn D. Avramidis, Stavros |
author2Str |
Mansfield, Shawn D. Avramidis, Stavros |
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doi_str |
10.1007/s00107-010-0490-2 |
up_date |
2024-07-04T03:30:44.363Z |
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1803617665675165696 |
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