Prediction of basic wood properties for Norway spruce. Interpretation of Near Infrared Spectroscopy data using partial least squares regression
Abstract This work was undertaken to investigate the feasibility of using near infrared spectroscopy (NIR) and partial least squares regression (PLS) as a tool to characterize the basic wood properties of Norway Spruce (Picea abies (L.) Karst.). The wood samples originated from a trial located in th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hauksson, Jón B. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2001 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2001 |
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| Übergeordnetes Werk: |
Enthalten in: Wood science and technology - Springer-Verlag, 1967, 35(2001), 6 vom: Dez., Seite 475-485 |
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| Übergeordnetes Werk: |
volume:35 ; year:2001 ; number:6 ; month:12 ; pages:475-485 |
| Links: |
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| DOI / URN: |
10.1007/s00226-001-0123-3 |
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| Katalog-ID: |
OLC2073067905 |
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| 520 | |a Abstract This work was undertaken to investigate the feasibility of using near infrared spectroscopy (NIR) and partial least squares regression (PLS) as a tool to characterize the basic wood properties of Norway Spruce (Picea abies (L.) Karst.). The wood samples originated from a trial located in the province of Västerbotten in Sweden. In this trial, the effects of birch shelterwoods (Betula pendula Roth) of different densities on growth and yield in Norway spruce understorey were examined. All Norway spruce trees in each shelterwood treatment were divided into three growth rate classes based on diameter at breast height (1.3 m) over bark. Five discs were cut from each tree (i.e. from the root stem, and at 20%, 40%, 60%, and 80% of the total height). The discs from 40% tree height were used (i.e., where the largest variations in annual ring widths and wood density were found). A total of 27 discs were selected. The discs were used for measuring annual ring widths, wood density, average fiber length and the fiber length distributions. Milled wood samples prepared from the discs were used for recording NIR spectra. PLS regression was used to generate prediction models for the wood properties (Y-matrix) and NIR spectra (X-matrix) as well as between the wood properties (Y-matrix) and the fiber length distributions (X-matrix). One set of models was generated using untreated spectra and fiber length distributions. For a second set of models the structure in the X-matrix, which was orthogonal to the matrix described by the wood properties, was eliminated using a soft target rotation technique called orthogonal signal correction (OSC). The PLS model obtained using “raw” untreated NIR spectra and fiber length distributions had a poor modeling power as evidenced by the cumulative $ Q^{2} $ values. For the PLS models based on untreated NIR spectra the cumulative $ Q^{2} $ values ranged from a minimum of 16% (wood density) to a maximum of 46% (no. of annual rings). Orthogonal signal correction of the X-matrix (NIR spectra or fiber length distributions) gave PLS models with a modeling power corresponding to cumulative $ Q^{2} $ values well in excess of 70%. The improvement in predictive ability accomplished by the OSC procedure was verified by placing four of the 27 observations in an external test set and comparing RMSEP values for the test set observations without OSC and with OSC. | ||
| 650 | 4 | |a Partial Little Square | |
| 650 | 4 | |a Wood Density | |
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| 700 | 1 | |a Bergsten, Urban |4 aut | |
| 700 | 1 | |a Sjöström, Michael |4 aut | |
| 700 | 1 | |a Edlund, Ulf |4 aut | |
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10.1007/s00226-001-0123-3 doi (DE-627)OLC2073067905 (DE-He213)s00226-001-0123-3-p DE-627 ger DE-627 rakwb eng 670 VZ 23 ssgn Hauksson, Jón B. verfasserin aut Prediction of basic wood properties for Norway spruce. Interpretation of Near Infrared Spectroscopy data using partial least squares regression 2001 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2001 Abstract This work was undertaken to investigate the feasibility of using near infrared spectroscopy (NIR) and partial least squares regression (PLS) as a tool to characterize the basic wood properties of Norway Spruce (Picea abies (L.) Karst.). The wood samples originated from a trial located in the province of Västerbotten in Sweden. In this trial, the effects of birch shelterwoods (Betula pendula Roth) of different densities on growth and yield in Norway spruce understorey were examined. All Norway spruce trees in each shelterwood treatment were divided into three growth rate classes based on diameter at breast height (1.3 m) over bark. Five discs were cut from each tree (i.e. from the root stem, and at 20%, 40%, 60%, and 80% of the total height). The discs from 40% tree height were used (i.e., where the largest variations in annual ring widths and wood density were found). A total of 27 discs were selected. The discs were used for measuring annual ring widths, wood density, average fiber length and the fiber length distributions. Milled wood samples prepared from the discs were used for recording NIR spectra. PLS regression was used to generate prediction models for the wood properties (Y-matrix) and NIR spectra (X-matrix) as well as between the wood properties (Y-matrix) and the fiber length distributions (X-matrix). One set of models was generated using untreated spectra and fiber length distributions. For a second set of models the structure in the X-matrix, which was orthogonal to the matrix described by the wood properties, was eliminated using a soft target rotation technique called orthogonal signal correction (OSC). The PLS model obtained using “raw” untreated NIR spectra and fiber length distributions had a poor modeling power as evidenced by the cumulative $ Q^{2} $ values. For the PLS models based on untreated NIR spectra the cumulative $ Q^{2} $ values ranged from a minimum of 16% (wood density) to a maximum of 46% (no. of annual rings). Orthogonal signal correction of the X-matrix (NIR spectra or fiber length distributions) gave PLS models with a modeling power corresponding to cumulative $ Q^{2} $ values well in excess of 70%. The improvement in predictive ability accomplished by the OSC procedure was verified by placing four of the 27 observations in an external test set and comparing RMSEP values for the test set observations without OSC and with OSC. Partial Little Square Wood Density Partial Little Square Regression Wood Property Partial Little Square Model Bergqvist, Göran aut Bergsten, Urban aut Sjöström, Michael aut Edlund, Ulf aut Enthalten in Wood science and technology Springer-Verlag, 1967 35(2001), 6 vom: Dez., Seite 475-485 (DE-627)129600679 (DE-600)241313-9 (DE-576)015094227 0043-7719 nnns volume:35 year:2001 number:6 month:12 pages:475-485 https://doi.org/10.1007/s00226-001-0123-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OPC-FOR GBV_ILN_40 GBV_ILN_70 GBV_ILN_252 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_2542 GBV_ILN_4046 GBV_ILN_4219 GBV_ILN_4277 GBV_ILN_4330 AR 35 2001 6 12 475-485 |
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10.1007/s00226-001-0123-3 doi (DE-627)OLC2073067905 (DE-He213)s00226-001-0123-3-p DE-627 ger DE-627 rakwb eng 670 VZ 23 ssgn Hauksson, Jón B. verfasserin aut Prediction of basic wood properties for Norway spruce. Interpretation of Near Infrared Spectroscopy data using partial least squares regression 2001 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2001 Abstract This work was undertaken to investigate the feasibility of using near infrared spectroscopy (NIR) and partial least squares regression (PLS) as a tool to characterize the basic wood properties of Norway Spruce (Picea abies (L.) Karst.). The wood samples originated from a trial located in the province of Västerbotten in Sweden. In this trial, the effects of birch shelterwoods (Betula pendula Roth) of different densities on growth and yield in Norway spruce understorey were examined. All Norway spruce trees in each shelterwood treatment were divided into three growth rate classes based on diameter at breast height (1.3 m) over bark. Five discs were cut from each tree (i.e. from the root stem, and at 20%, 40%, 60%, and 80% of the total height). The discs from 40% tree height were used (i.e., where the largest variations in annual ring widths and wood density were found). A total of 27 discs were selected. The discs were used for measuring annual ring widths, wood density, average fiber length and the fiber length distributions. Milled wood samples prepared from the discs were used for recording NIR spectra. PLS regression was used to generate prediction models for the wood properties (Y-matrix) and NIR spectra (X-matrix) as well as between the wood properties (Y-matrix) and the fiber length distributions (X-matrix). One set of models was generated using untreated spectra and fiber length distributions. For a second set of models the structure in the X-matrix, which was orthogonal to the matrix described by the wood properties, was eliminated using a soft target rotation technique called orthogonal signal correction (OSC). The PLS model obtained using “raw” untreated NIR spectra and fiber length distributions had a poor modeling power as evidenced by the cumulative $ Q^{2} $ values. For the PLS models based on untreated NIR spectra the cumulative $ Q^{2} $ values ranged from a minimum of 16% (wood density) to a maximum of 46% (no. of annual rings). Orthogonal signal correction of the X-matrix (NIR spectra or fiber length distributions) gave PLS models with a modeling power corresponding to cumulative $ Q^{2} $ values well in excess of 70%. The improvement in predictive ability accomplished by the OSC procedure was verified by placing four of the 27 observations in an external test set and comparing RMSEP values for the test set observations without OSC and with OSC. Partial Little Square Wood Density Partial Little Square Regression Wood Property Partial Little Square Model Bergqvist, Göran aut Bergsten, Urban aut Sjöström, Michael aut Edlund, Ulf aut Enthalten in Wood science and technology Springer-Verlag, 1967 35(2001), 6 vom: Dez., Seite 475-485 (DE-627)129600679 (DE-600)241313-9 (DE-576)015094227 0043-7719 nnns volume:35 year:2001 number:6 month:12 pages:475-485 https://doi.org/10.1007/s00226-001-0123-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OPC-FOR GBV_ILN_40 GBV_ILN_70 GBV_ILN_252 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_2542 GBV_ILN_4046 GBV_ILN_4219 GBV_ILN_4277 GBV_ILN_4330 AR 35 2001 6 12 475-485 |
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10.1007/s00226-001-0123-3 doi (DE-627)OLC2073067905 (DE-He213)s00226-001-0123-3-p DE-627 ger DE-627 rakwb eng 670 VZ 23 ssgn Hauksson, Jón B. verfasserin aut Prediction of basic wood properties for Norway spruce. Interpretation of Near Infrared Spectroscopy data using partial least squares regression 2001 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2001 Abstract This work was undertaken to investigate the feasibility of using near infrared spectroscopy (NIR) and partial least squares regression (PLS) as a tool to characterize the basic wood properties of Norway Spruce (Picea abies (L.) Karst.). The wood samples originated from a trial located in the province of Västerbotten in Sweden. In this trial, the effects of birch shelterwoods (Betula pendula Roth) of different densities on growth and yield in Norway spruce understorey were examined. All Norway spruce trees in each shelterwood treatment were divided into three growth rate classes based on diameter at breast height (1.3 m) over bark. Five discs were cut from each tree (i.e. from the root stem, and at 20%, 40%, 60%, and 80% of the total height). The discs from 40% tree height were used (i.e., where the largest variations in annual ring widths and wood density were found). A total of 27 discs were selected. The discs were used for measuring annual ring widths, wood density, average fiber length and the fiber length distributions. Milled wood samples prepared from the discs were used for recording NIR spectra. PLS regression was used to generate prediction models for the wood properties (Y-matrix) and NIR spectra (X-matrix) as well as between the wood properties (Y-matrix) and the fiber length distributions (X-matrix). One set of models was generated using untreated spectra and fiber length distributions. For a second set of models the structure in the X-matrix, which was orthogonal to the matrix described by the wood properties, was eliminated using a soft target rotation technique called orthogonal signal correction (OSC). The PLS model obtained using “raw” untreated NIR spectra and fiber length distributions had a poor modeling power as evidenced by the cumulative $ Q^{2} $ values. For the PLS models based on untreated NIR spectra the cumulative $ Q^{2} $ values ranged from a minimum of 16% (wood density) to a maximum of 46% (no. of annual rings). Orthogonal signal correction of the X-matrix (NIR spectra or fiber length distributions) gave PLS models with a modeling power corresponding to cumulative $ Q^{2} $ values well in excess of 70%. The improvement in predictive ability accomplished by the OSC procedure was verified by placing four of the 27 observations in an external test set and comparing RMSEP values for the test set observations without OSC and with OSC. Partial Little Square Wood Density Partial Little Square Regression Wood Property Partial Little Square Model Bergqvist, Göran aut Bergsten, Urban aut Sjöström, Michael aut Edlund, Ulf aut Enthalten in Wood science and technology Springer-Verlag, 1967 35(2001), 6 vom: Dez., Seite 475-485 (DE-627)129600679 (DE-600)241313-9 (DE-576)015094227 0043-7719 nnns volume:35 year:2001 number:6 month:12 pages:475-485 https://doi.org/10.1007/s00226-001-0123-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OPC-FOR GBV_ILN_40 GBV_ILN_70 GBV_ILN_252 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_2542 GBV_ILN_4046 GBV_ILN_4219 GBV_ILN_4277 GBV_ILN_4330 AR 35 2001 6 12 475-485 |
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10.1007/s00226-001-0123-3 doi (DE-627)OLC2073067905 (DE-He213)s00226-001-0123-3-p DE-627 ger DE-627 rakwb eng 670 VZ 23 ssgn Hauksson, Jón B. verfasserin aut Prediction of basic wood properties for Norway spruce. Interpretation of Near Infrared Spectroscopy data using partial least squares regression 2001 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2001 Abstract This work was undertaken to investigate the feasibility of using near infrared spectroscopy (NIR) and partial least squares regression (PLS) as a tool to characterize the basic wood properties of Norway Spruce (Picea abies (L.) Karst.). The wood samples originated from a trial located in the province of Västerbotten in Sweden. In this trial, the effects of birch shelterwoods (Betula pendula Roth) of different densities on growth and yield in Norway spruce understorey were examined. All Norway spruce trees in each shelterwood treatment were divided into three growth rate classes based on diameter at breast height (1.3 m) over bark. Five discs were cut from each tree (i.e. from the root stem, and at 20%, 40%, 60%, and 80% of the total height). The discs from 40% tree height were used (i.e., where the largest variations in annual ring widths and wood density were found). A total of 27 discs were selected. The discs were used for measuring annual ring widths, wood density, average fiber length and the fiber length distributions. Milled wood samples prepared from the discs were used for recording NIR spectra. PLS regression was used to generate prediction models for the wood properties (Y-matrix) and NIR spectra (X-matrix) as well as between the wood properties (Y-matrix) and the fiber length distributions (X-matrix). One set of models was generated using untreated spectra and fiber length distributions. For a second set of models the structure in the X-matrix, which was orthogonal to the matrix described by the wood properties, was eliminated using a soft target rotation technique called orthogonal signal correction (OSC). The PLS model obtained using “raw” untreated NIR spectra and fiber length distributions had a poor modeling power as evidenced by the cumulative $ Q^{2} $ values. For the PLS models based on untreated NIR spectra the cumulative $ Q^{2} $ values ranged from a minimum of 16% (wood density) to a maximum of 46% (no. of annual rings). Orthogonal signal correction of the X-matrix (NIR spectra or fiber length distributions) gave PLS models with a modeling power corresponding to cumulative $ Q^{2} $ values well in excess of 70%. The improvement in predictive ability accomplished by the OSC procedure was verified by placing four of the 27 observations in an external test set and comparing RMSEP values for the test set observations without OSC and with OSC. Partial Little Square Wood Density Partial Little Square Regression Wood Property Partial Little Square Model Bergqvist, Göran aut Bergsten, Urban aut Sjöström, Michael aut Edlund, Ulf aut Enthalten in Wood science and technology Springer-Verlag, 1967 35(2001), 6 vom: Dez., Seite 475-485 (DE-627)129600679 (DE-600)241313-9 (DE-576)015094227 0043-7719 nnns volume:35 year:2001 number:6 month:12 pages:475-485 https://doi.org/10.1007/s00226-001-0123-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OPC-FOR GBV_ILN_40 GBV_ILN_70 GBV_ILN_252 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_2542 GBV_ILN_4046 GBV_ILN_4219 GBV_ILN_4277 GBV_ILN_4330 AR 35 2001 6 12 475-485 |
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10.1007/s00226-001-0123-3 doi (DE-627)OLC2073067905 (DE-He213)s00226-001-0123-3-p DE-627 ger DE-627 rakwb eng 670 VZ 23 ssgn Hauksson, Jón B. verfasserin aut Prediction of basic wood properties for Norway spruce. Interpretation of Near Infrared Spectroscopy data using partial least squares regression 2001 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2001 Abstract This work was undertaken to investigate the feasibility of using near infrared spectroscopy (NIR) and partial least squares regression (PLS) as a tool to characterize the basic wood properties of Norway Spruce (Picea abies (L.) Karst.). The wood samples originated from a trial located in the province of Västerbotten in Sweden. In this trial, the effects of birch shelterwoods (Betula pendula Roth) of different densities on growth and yield in Norway spruce understorey were examined. All Norway spruce trees in each shelterwood treatment were divided into three growth rate classes based on diameter at breast height (1.3 m) over bark. Five discs were cut from each tree (i.e. from the root stem, and at 20%, 40%, 60%, and 80% of the total height). The discs from 40% tree height were used (i.e., where the largest variations in annual ring widths and wood density were found). A total of 27 discs were selected. The discs were used for measuring annual ring widths, wood density, average fiber length and the fiber length distributions. Milled wood samples prepared from the discs were used for recording NIR spectra. PLS regression was used to generate prediction models for the wood properties (Y-matrix) and NIR spectra (X-matrix) as well as between the wood properties (Y-matrix) and the fiber length distributions (X-matrix). One set of models was generated using untreated spectra and fiber length distributions. For a second set of models the structure in the X-matrix, which was orthogonal to the matrix described by the wood properties, was eliminated using a soft target rotation technique called orthogonal signal correction (OSC). The PLS model obtained using “raw” untreated NIR spectra and fiber length distributions had a poor modeling power as evidenced by the cumulative $ Q^{2} $ values. For the PLS models based on untreated NIR spectra the cumulative $ Q^{2} $ values ranged from a minimum of 16% (wood density) to a maximum of 46% (no. of annual rings). Orthogonal signal correction of the X-matrix (NIR spectra or fiber length distributions) gave PLS models with a modeling power corresponding to cumulative $ Q^{2} $ values well in excess of 70%. The improvement in predictive ability accomplished by the OSC procedure was verified by placing four of the 27 observations in an external test set and comparing RMSEP values for the test set observations without OSC and with OSC. Partial Little Square Wood Density Partial Little Square Regression Wood Property Partial Little Square Model Bergqvist, Göran aut Bergsten, Urban aut Sjöström, Michael aut Edlund, Ulf aut Enthalten in Wood science and technology Springer-Verlag, 1967 35(2001), 6 vom: Dez., Seite 475-485 (DE-627)129600679 (DE-600)241313-9 (DE-576)015094227 0043-7719 nnns volume:35 year:2001 number:6 month:12 pages:475-485 https://doi.org/10.1007/s00226-001-0123-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OPC-FOR GBV_ILN_40 GBV_ILN_70 GBV_ILN_252 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2016 GBV_ILN_2018 GBV_ILN_2542 GBV_ILN_4046 GBV_ILN_4219 GBV_ILN_4277 GBV_ILN_4330 AR 35 2001 6 12 475-485 |
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Hauksson, Jón B. |
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prediction of basic wood properties for norway spruce. interpretation of near infrared spectroscopy data using partial least squares regression |
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Prediction of basic wood properties for Norway spruce. Interpretation of Near Infrared Spectroscopy data using partial least squares regression |
| abstract |
Abstract This work was undertaken to investigate the feasibility of using near infrared spectroscopy (NIR) and partial least squares regression (PLS) as a tool to characterize the basic wood properties of Norway Spruce (Picea abies (L.) Karst.). The wood samples originated from a trial located in the province of Västerbotten in Sweden. In this trial, the effects of birch shelterwoods (Betula pendula Roth) of different densities on growth and yield in Norway spruce understorey were examined. All Norway spruce trees in each shelterwood treatment were divided into three growth rate classes based on diameter at breast height (1.3 m) over bark. Five discs were cut from each tree (i.e. from the root stem, and at 20%, 40%, 60%, and 80% of the total height). The discs from 40% tree height were used (i.e., where the largest variations in annual ring widths and wood density were found). A total of 27 discs were selected. The discs were used for measuring annual ring widths, wood density, average fiber length and the fiber length distributions. Milled wood samples prepared from the discs were used for recording NIR spectra. PLS regression was used to generate prediction models for the wood properties (Y-matrix) and NIR spectra (X-matrix) as well as between the wood properties (Y-matrix) and the fiber length distributions (X-matrix). One set of models was generated using untreated spectra and fiber length distributions. For a second set of models the structure in the X-matrix, which was orthogonal to the matrix described by the wood properties, was eliminated using a soft target rotation technique called orthogonal signal correction (OSC). The PLS model obtained using “raw” untreated NIR spectra and fiber length distributions had a poor modeling power as evidenced by the cumulative $ Q^{2} $ values. For the PLS models based on untreated NIR spectra the cumulative $ Q^{2} $ values ranged from a minimum of 16% (wood density) to a maximum of 46% (no. of annual rings). Orthogonal signal correction of the X-matrix (NIR spectra or fiber length distributions) gave PLS models with a modeling power corresponding to cumulative $ Q^{2} $ values well in excess of 70%. The improvement in predictive ability accomplished by the OSC procedure was verified by placing four of the 27 observations in an external test set and comparing RMSEP values for the test set observations without OSC and with OSC. © Springer-Verlag Berlin Heidelberg 2001 |
| abstractGer |
Abstract This work was undertaken to investigate the feasibility of using near infrared spectroscopy (NIR) and partial least squares regression (PLS) as a tool to characterize the basic wood properties of Norway Spruce (Picea abies (L.) Karst.). The wood samples originated from a trial located in the province of Västerbotten in Sweden. In this trial, the effects of birch shelterwoods (Betula pendula Roth) of different densities on growth and yield in Norway spruce understorey were examined. All Norway spruce trees in each shelterwood treatment were divided into three growth rate classes based on diameter at breast height (1.3 m) over bark. Five discs were cut from each tree (i.e. from the root stem, and at 20%, 40%, 60%, and 80% of the total height). The discs from 40% tree height were used (i.e., where the largest variations in annual ring widths and wood density were found). A total of 27 discs were selected. The discs were used for measuring annual ring widths, wood density, average fiber length and the fiber length distributions. Milled wood samples prepared from the discs were used for recording NIR spectra. PLS regression was used to generate prediction models for the wood properties (Y-matrix) and NIR spectra (X-matrix) as well as between the wood properties (Y-matrix) and the fiber length distributions (X-matrix). One set of models was generated using untreated spectra and fiber length distributions. For a second set of models the structure in the X-matrix, which was orthogonal to the matrix described by the wood properties, was eliminated using a soft target rotation technique called orthogonal signal correction (OSC). The PLS model obtained using “raw” untreated NIR spectra and fiber length distributions had a poor modeling power as evidenced by the cumulative $ Q^{2} $ values. For the PLS models based on untreated NIR spectra the cumulative $ Q^{2} $ values ranged from a minimum of 16% (wood density) to a maximum of 46% (no. of annual rings). Orthogonal signal correction of the X-matrix (NIR spectra or fiber length distributions) gave PLS models with a modeling power corresponding to cumulative $ Q^{2} $ values well in excess of 70%. The improvement in predictive ability accomplished by the OSC procedure was verified by placing four of the 27 observations in an external test set and comparing RMSEP values for the test set observations without OSC and with OSC. © Springer-Verlag Berlin Heidelberg 2001 |
| abstract_unstemmed |
Abstract This work was undertaken to investigate the feasibility of using near infrared spectroscopy (NIR) and partial least squares regression (PLS) as a tool to characterize the basic wood properties of Norway Spruce (Picea abies (L.) Karst.). The wood samples originated from a trial located in the province of Västerbotten in Sweden. In this trial, the effects of birch shelterwoods (Betula pendula Roth) of different densities on growth and yield in Norway spruce understorey were examined. All Norway spruce trees in each shelterwood treatment were divided into three growth rate classes based on diameter at breast height (1.3 m) over bark. Five discs were cut from each tree (i.e. from the root stem, and at 20%, 40%, 60%, and 80% of the total height). The discs from 40% tree height were used (i.e., where the largest variations in annual ring widths and wood density were found). A total of 27 discs were selected. The discs were used for measuring annual ring widths, wood density, average fiber length and the fiber length distributions. Milled wood samples prepared from the discs were used for recording NIR spectra. PLS regression was used to generate prediction models for the wood properties (Y-matrix) and NIR spectra (X-matrix) as well as between the wood properties (Y-matrix) and the fiber length distributions (X-matrix). One set of models was generated using untreated spectra and fiber length distributions. For a second set of models the structure in the X-matrix, which was orthogonal to the matrix described by the wood properties, was eliminated using a soft target rotation technique called orthogonal signal correction (OSC). The PLS model obtained using “raw” untreated NIR spectra and fiber length distributions had a poor modeling power as evidenced by the cumulative $ Q^{2} $ values. For the PLS models based on untreated NIR spectra the cumulative $ Q^{2} $ values ranged from a minimum of 16% (wood density) to a maximum of 46% (no. of annual rings). Orthogonal signal correction of the X-matrix (NIR spectra or fiber length distributions) gave PLS models with a modeling power corresponding to cumulative $ Q^{2} $ values well in excess of 70%. The improvement in predictive ability accomplished by the OSC procedure was verified by placing four of the 27 observations in an external test set and comparing RMSEP values for the test set observations without OSC and with OSC. © Springer-Verlag Berlin Heidelberg 2001 |
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PLS regression was used to generate prediction models for the wood properties (Y-matrix) and NIR spectra (X-matrix) as well as between the wood properties (Y-matrix) and the fiber length distributions (X-matrix). One set of models was generated using untreated spectra and fiber length distributions. For a second set of models the structure in the X-matrix, which was orthogonal to the matrix described by the wood properties, was eliminated using a soft target rotation technique called orthogonal signal correction (OSC). The PLS model obtained using “raw” untreated NIR spectra and fiber length distributions had a poor modeling power as evidenced by the cumulative $ Q^{2} $ values. For the PLS models based on untreated NIR spectra the cumulative $ Q^{2} $ values ranged from a minimum of 16% (wood density) to a maximum of 46% (no. of annual rings). Orthogonal signal correction of the X-matrix (NIR spectra or fiber length distributions) gave PLS models with a modeling power corresponding to cumulative $ Q^{2} $ values well in excess of 70%. The improvement in predictive ability accomplished by the OSC procedure was verified by placing four of the 27 observations in an external test set and comparing RMSEP values for the test set observations without OSC and with OSC.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Partial Little Square</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wood Density</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Partial Little Square Regression</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wood Property</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Partial Little Square Model</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bergqvist, Göran</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bergsten, Urban</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sjöström, Michael</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Edlund, Ulf</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Wood science and technology</subfield><subfield code="d">Springer-Verlag, 1967</subfield><subfield code="g">35(2001), 6 vom: Dez., Seite 475-485</subfield><subfield code="w">(DE-627)129600679</subfield><subfield code="w">(DE-600)241313-9</subfield><subfield code="w">(DE-576)015094227</subfield><subfield code="x">0043-7719</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:35</subfield><subfield code="g">year:2001</subfield><subfield code="g">number:6</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:475-485</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00226-001-0123-3</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-CHE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-FOR</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-FOR</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_252</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2016</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2542</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4219</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4330</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">35</subfield><subfield code="j">2001</subfield><subfield code="e">6</subfield><subfield code="c">12</subfield><subfield code="h">475-485</subfield></datafield></record></collection>
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