Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies
Abstract Because plant cell walls vary in their polysaccharide compositions and lignin contents, their monosaccharide compositions and lignin contents are often determined, but these analyses are time consuming and laborious. We therefore investigated Fourier transform infrared (FTIR) spectroscopy c...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fahey, Leona M. [verfasserIn] Nieuwoudt, Michél K. [verfasserIn] Harris, Philip J. [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
Attenuated total reflectance Fourier transform infrared (ATR FTIR) spectroscopy Transmission Fourier transform infrared (transmission FTIR) spectroscopy |
|---|
| Übergeordnetes Werk: |
Enthalten in: Cellulose - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 24(2017), 12 vom: 03. Okt., Seite 5275-5293 |
|---|---|
| Übergeordnetes Werk: |
volume:24 ; year:2017 ; number:12 ; day:03 ; month:10 ; pages:5275-5293 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10570-017-1506-4 |
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| Katalog-ID: |
SPR011598751 |
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| 245 | 1 | 0 | |a Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies |
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| 520 | |a Abstract Because plant cell walls vary in their polysaccharide compositions and lignin contents, their monosaccharide compositions and lignin contents are often determined, but these analyses are time consuming and laborious. We therefore investigated Fourier transform infrared (FTIR) spectroscopy coupled with partial least squares (PLS) regression analysis as a way of rapidly predicting the monosaccharide compositions and lignin contents of the cell walls of compression wood (CW) and opposite wood (OW) of the gymnosperm Pinus radiata. The effects were investigated of sample moisture content (ambient or dry) and sample particle size (large particles, < 0.422 mm or small particles, < 0.178 mm) of milled wood on attenuated total reflectance (ATR) and transmission FTIR spectra, as well as the PLS-1 models and subsequent predictions. PLS-1 models were built using mixtures of CW and OW as the training set, to provide a linear range of monosaccharide compositions and lignin contents. Models were externally validated by predicting another set of wood mixtures before predicting CW and OW of a separate test set. Most of the monosaccharide amounts in the separate test set were best predicted by ATR spectroscopy of ambient large particles, achieving the lowest standard error values for the monosaccharides arabinose (0.36%), xylose (1.05%), galactose (1.79%), glucose (6.32%), and 4-O-methylglucuronic acid (0.20%). The results show the feasibility of using ATR spectroscopy of ambient large particles for the rapid prediction of monosaccharide compositions and lignin contents of plant cell walls. | ||
| 650 | 4 | |a (radiata pine) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Monosaccharide composition |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Lignin |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Attenuated total reflectance Fourier transform infrared (ATR FTIR) spectroscopy |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Transmission Fourier transform infrared (transmission FTIR) spectroscopy |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Partial least squares (PLS) regression |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Nieuwoudt, Michél K. |e verfasserin |4 aut | |
| 700 | 1 | |a Harris, Philip J. |e verfasserin |4 aut | |
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10.1007/s10570-017-1506-4 doi (DE-627)SPR011598751 (SPR)s10570-017-1506-4-e DE-627 ger DE-627 rakwb eng 540 ASE 35.63 bkl 35.77 bkl Fahey, Leona M. verfasserin aut Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Because plant cell walls vary in their polysaccharide compositions and lignin contents, their monosaccharide compositions and lignin contents are often determined, but these analyses are time consuming and laborious. We therefore investigated Fourier transform infrared (FTIR) spectroscopy coupled with partial least squares (PLS) regression analysis as a way of rapidly predicting the monosaccharide compositions and lignin contents of the cell walls of compression wood (CW) and opposite wood (OW) of the gymnosperm Pinus radiata. The effects were investigated of sample moisture content (ambient or dry) and sample particle size (large particles, < 0.422 mm or small particles, < 0.178 mm) of milled wood on attenuated total reflectance (ATR) and transmission FTIR spectra, as well as the PLS-1 models and subsequent predictions. PLS-1 models were built using mixtures of CW and OW as the training set, to provide a linear range of monosaccharide compositions and lignin contents. Models were externally validated by predicting another set of wood mixtures before predicting CW and OW of a separate test set. Most of the monosaccharide amounts in the separate test set were best predicted by ATR spectroscopy of ambient large particles, achieving the lowest standard error values for the monosaccharides arabinose (0.36%), xylose (1.05%), galactose (1.79%), glucose (6.32%), and 4-O-methylglucuronic acid (0.20%). The results show the feasibility of using ATR spectroscopy of ambient large particles for the rapid prediction of monosaccharide compositions and lignin contents of plant cell walls. (radiata pine) (dpeaa)DE-He213 Monosaccharide composition (dpeaa)DE-He213 Lignin (dpeaa)DE-He213 Attenuated total reflectance Fourier transform infrared (ATR FTIR) spectroscopy (dpeaa)DE-He213 Transmission Fourier transform infrared (transmission FTIR) spectroscopy (dpeaa)DE-He213 Partial least squares (PLS) regression (dpeaa)DE-He213 Nieuwoudt, Michél K. verfasserin aut Harris, Philip J. verfasserin aut Enthalten in Cellulose Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 24(2017), 12 vom: 03. Okt., Seite 5275-5293 (DE-627)306353857 (DE-600)1496831-9 1572-882X nnns volume:24 year:2017 number:12 day:03 month:10 pages:5275-5293 https://dx.doi.org/10.1007/s10570-017-1506-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.63 ASE 35.77 ASE AR 24 2017 12 03 10 5275-5293 |
| spelling |
10.1007/s10570-017-1506-4 doi (DE-627)SPR011598751 (SPR)s10570-017-1506-4-e DE-627 ger DE-627 rakwb eng 540 ASE 35.63 bkl 35.77 bkl Fahey, Leona M. verfasserin aut Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Because plant cell walls vary in their polysaccharide compositions and lignin contents, their monosaccharide compositions and lignin contents are often determined, but these analyses are time consuming and laborious. We therefore investigated Fourier transform infrared (FTIR) spectroscopy coupled with partial least squares (PLS) regression analysis as a way of rapidly predicting the monosaccharide compositions and lignin contents of the cell walls of compression wood (CW) and opposite wood (OW) of the gymnosperm Pinus radiata. The effects were investigated of sample moisture content (ambient or dry) and sample particle size (large particles, < 0.422 mm or small particles, < 0.178 mm) of milled wood on attenuated total reflectance (ATR) and transmission FTIR spectra, as well as the PLS-1 models and subsequent predictions. PLS-1 models were built using mixtures of CW and OW as the training set, to provide a linear range of monosaccharide compositions and lignin contents. Models were externally validated by predicting another set of wood mixtures before predicting CW and OW of a separate test set. Most of the monosaccharide amounts in the separate test set were best predicted by ATR spectroscopy of ambient large particles, achieving the lowest standard error values for the monosaccharides arabinose (0.36%), xylose (1.05%), galactose (1.79%), glucose (6.32%), and 4-O-methylglucuronic acid (0.20%). The results show the feasibility of using ATR spectroscopy of ambient large particles for the rapid prediction of monosaccharide compositions and lignin contents of plant cell walls. (radiata pine) (dpeaa)DE-He213 Monosaccharide composition (dpeaa)DE-He213 Lignin (dpeaa)DE-He213 Attenuated total reflectance Fourier transform infrared (ATR FTIR) spectroscopy (dpeaa)DE-He213 Transmission Fourier transform infrared (transmission FTIR) spectroscopy (dpeaa)DE-He213 Partial least squares (PLS) regression (dpeaa)DE-He213 Nieuwoudt, Michél K. verfasserin aut Harris, Philip J. verfasserin aut Enthalten in Cellulose Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 24(2017), 12 vom: 03. Okt., Seite 5275-5293 (DE-627)306353857 (DE-600)1496831-9 1572-882X nnns volume:24 year:2017 number:12 day:03 month:10 pages:5275-5293 https://dx.doi.org/10.1007/s10570-017-1506-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.63 ASE 35.77 ASE AR 24 2017 12 03 10 5275-5293 |
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10.1007/s10570-017-1506-4 doi (DE-627)SPR011598751 (SPR)s10570-017-1506-4-e DE-627 ger DE-627 rakwb eng 540 ASE 35.63 bkl 35.77 bkl Fahey, Leona M. verfasserin aut Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Because plant cell walls vary in their polysaccharide compositions and lignin contents, their monosaccharide compositions and lignin contents are often determined, but these analyses are time consuming and laborious. We therefore investigated Fourier transform infrared (FTIR) spectroscopy coupled with partial least squares (PLS) regression analysis as a way of rapidly predicting the monosaccharide compositions and lignin contents of the cell walls of compression wood (CW) and opposite wood (OW) of the gymnosperm Pinus radiata. The effects were investigated of sample moisture content (ambient or dry) and sample particle size (large particles, < 0.422 mm or small particles, < 0.178 mm) of milled wood on attenuated total reflectance (ATR) and transmission FTIR spectra, as well as the PLS-1 models and subsequent predictions. PLS-1 models were built using mixtures of CW and OW as the training set, to provide a linear range of monosaccharide compositions and lignin contents. Models were externally validated by predicting another set of wood mixtures before predicting CW and OW of a separate test set. Most of the monosaccharide amounts in the separate test set were best predicted by ATR spectroscopy of ambient large particles, achieving the lowest standard error values for the monosaccharides arabinose (0.36%), xylose (1.05%), galactose (1.79%), glucose (6.32%), and 4-O-methylglucuronic acid (0.20%). The results show the feasibility of using ATR spectroscopy of ambient large particles for the rapid prediction of monosaccharide compositions and lignin contents of plant cell walls. (radiata pine) (dpeaa)DE-He213 Monosaccharide composition (dpeaa)DE-He213 Lignin (dpeaa)DE-He213 Attenuated total reflectance Fourier transform infrared (ATR FTIR) spectroscopy (dpeaa)DE-He213 Transmission Fourier transform infrared (transmission FTIR) spectroscopy (dpeaa)DE-He213 Partial least squares (PLS) regression (dpeaa)DE-He213 Nieuwoudt, Michél K. verfasserin aut Harris, Philip J. verfasserin aut Enthalten in Cellulose Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 24(2017), 12 vom: 03. Okt., Seite 5275-5293 (DE-627)306353857 (DE-600)1496831-9 1572-882X nnns volume:24 year:2017 number:12 day:03 month:10 pages:5275-5293 https://dx.doi.org/10.1007/s10570-017-1506-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.63 ASE 35.77 ASE AR 24 2017 12 03 10 5275-5293 |
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10.1007/s10570-017-1506-4 doi (DE-627)SPR011598751 (SPR)s10570-017-1506-4-e DE-627 ger DE-627 rakwb eng 540 ASE 35.63 bkl 35.77 bkl Fahey, Leona M. verfasserin aut Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Because plant cell walls vary in their polysaccharide compositions and lignin contents, their monosaccharide compositions and lignin contents are often determined, but these analyses are time consuming and laborious. We therefore investigated Fourier transform infrared (FTIR) spectroscopy coupled with partial least squares (PLS) regression analysis as a way of rapidly predicting the monosaccharide compositions and lignin contents of the cell walls of compression wood (CW) and opposite wood (OW) of the gymnosperm Pinus radiata. The effects were investigated of sample moisture content (ambient or dry) and sample particle size (large particles, < 0.422 mm or small particles, < 0.178 mm) of milled wood on attenuated total reflectance (ATR) and transmission FTIR spectra, as well as the PLS-1 models and subsequent predictions. PLS-1 models were built using mixtures of CW and OW as the training set, to provide a linear range of monosaccharide compositions and lignin contents. Models were externally validated by predicting another set of wood mixtures before predicting CW and OW of a separate test set. Most of the monosaccharide amounts in the separate test set were best predicted by ATR spectroscopy of ambient large particles, achieving the lowest standard error values for the monosaccharides arabinose (0.36%), xylose (1.05%), galactose (1.79%), glucose (6.32%), and 4-O-methylglucuronic acid (0.20%). The results show the feasibility of using ATR spectroscopy of ambient large particles for the rapid prediction of monosaccharide compositions and lignin contents of plant cell walls. (radiata pine) (dpeaa)DE-He213 Monosaccharide composition (dpeaa)DE-He213 Lignin (dpeaa)DE-He213 Attenuated total reflectance Fourier transform infrared (ATR FTIR) spectroscopy (dpeaa)DE-He213 Transmission Fourier transform infrared (transmission FTIR) spectroscopy (dpeaa)DE-He213 Partial least squares (PLS) regression (dpeaa)DE-He213 Nieuwoudt, Michél K. verfasserin aut Harris, Philip J. verfasserin aut Enthalten in Cellulose Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 24(2017), 12 vom: 03. Okt., Seite 5275-5293 (DE-627)306353857 (DE-600)1496831-9 1572-882X nnns volume:24 year:2017 number:12 day:03 month:10 pages:5275-5293 https://dx.doi.org/10.1007/s10570-017-1506-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.63 ASE 35.77 ASE AR 24 2017 12 03 10 5275-5293 |
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10.1007/s10570-017-1506-4 doi (DE-627)SPR011598751 (SPR)s10570-017-1506-4-e DE-627 ger DE-627 rakwb eng 540 ASE 35.63 bkl 35.77 bkl Fahey, Leona M. verfasserin aut Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Because plant cell walls vary in their polysaccharide compositions and lignin contents, their monosaccharide compositions and lignin contents are often determined, but these analyses are time consuming and laborious. We therefore investigated Fourier transform infrared (FTIR) spectroscopy coupled with partial least squares (PLS) regression analysis as a way of rapidly predicting the monosaccharide compositions and lignin contents of the cell walls of compression wood (CW) and opposite wood (OW) of the gymnosperm Pinus radiata. The effects were investigated of sample moisture content (ambient or dry) and sample particle size (large particles, < 0.422 mm or small particles, < 0.178 mm) of milled wood on attenuated total reflectance (ATR) and transmission FTIR spectra, as well as the PLS-1 models and subsequent predictions. PLS-1 models were built using mixtures of CW and OW as the training set, to provide a linear range of monosaccharide compositions and lignin contents. Models were externally validated by predicting another set of wood mixtures before predicting CW and OW of a separate test set. Most of the monosaccharide amounts in the separate test set were best predicted by ATR spectroscopy of ambient large particles, achieving the lowest standard error values for the monosaccharides arabinose (0.36%), xylose (1.05%), galactose (1.79%), glucose (6.32%), and 4-O-methylglucuronic acid (0.20%). The results show the feasibility of using ATR spectroscopy of ambient large particles for the rapid prediction of monosaccharide compositions and lignin contents of plant cell walls. (radiata pine) (dpeaa)DE-He213 Monosaccharide composition (dpeaa)DE-He213 Lignin (dpeaa)DE-He213 Attenuated total reflectance Fourier transform infrared (ATR FTIR) spectroscopy (dpeaa)DE-He213 Transmission Fourier transform infrared (transmission FTIR) spectroscopy (dpeaa)DE-He213 Partial least squares (PLS) regression (dpeaa)DE-He213 Nieuwoudt, Michél K. verfasserin aut Harris, Philip J. verfasserin aut Enthalten in Cellulose Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 24(2017), 12 vom: 03. Okt., Seite 5275-5293 (DE-627)306353857 (DE-600)1496831-9 1572-882X nnns volume:24 year:2017 number:12 day:03 month:10 pages:5275-5293 https://dx.doi.org/10.1007/s10570-017-1506-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 35.63 ASE 35.77 ASE AR 24 2017 12 03 10 5275-5293 |
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Enthalten in Cellulose 24(2017), 12 vom: 03. Okt., Seite 5275-5293 volume:24 year:2017 number:12 day:03 month:10 pages:5275-5293 |
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Enthalten in Cellulose 24(2017), 12 vom: 03. Okt., Seite 5275-5293 volume:24 year:2017 number:12 day:03 month:10 pages:5275-5293 |
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(radiata pine) Monosaccharide composition Lignin Attenuated total reflectance Fourier transform infrared (ATR FTIR) spectroscopy Transmission Fourier transform infrared (transmission FTIR) spectroscopy Partial least squares (PLS) regression |
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Cellulose |
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Fahey, Leona M. @@aut@@ Nieuwoudt, Michél K. @@aut@@ Harris, Philip J. @@aut@@ |
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2017-10-03T00:00:00Z |
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We therefore investigated Fourier transform infrared (FTIR) spectroscopy coupled with partial least squares (PLS) regression analysis as a way of rapidly predicting the monosaccharide compositions and lignin contents of the cell walls of compression wood (CW) and opposite wood (OW) of the gymnosperm Pinus radiata. The effects were investigated of sample moisture content (ambient or dry) and sample particle size (large particles, < 0.422 mm or small particles, < 0.178 mm) of milled wood on attenuated total reflectance (ATR) and transmission FTIR spectra, as well as the PLS-1 models and subsequent predictions. PLS-1 models were built using mixtures of CW and OW as the training set, to provide a linear range of monosaccharide compositions and lignin contents. Models were externally validated by predicting another set of wood mixtures before predicting CW and OW of a separate test set. Most of the monosaccharide amounts in the separate test set were best predicted by ATR spectroscopy of ambient large particles, achieving the lowest standard error values for the monosaccharides arabinose (0.36%), xylose (1.05%), galactose (1.79%), glucose (6.32%), and 4-O-methylglucuronic acid (0.20%). 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Fahey, Leona M. |
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Fahey, Leona M. ddc 540 bkl 35.63 bkl 35.77 misc (radiata pine) misc Monosaccharide composition misc Lignin misc Attenuated total reflectance Fourier transform infrared (ATR FTIR) spectroscopy misc Transmission Fourier transform infrared (transmission FTIR) spectroscopy misc Partial least squares (PLS) regression Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies |
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540 ASE 35.63 bkl 35.77 bkl Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies (radiata pine) (dpeaa)DE-He213 Monosaccharide composition (dpeaa)DE-He213 Lignin (dpeaa)DE-He213 Attenuated total reflectance Fourier transform infrared (ATR FTIR) spectroscopy (dpeaa)DE-He213 Transmission Fourier transform infrared (transmission FTIR) spectroscopy (dpeaa)DE-He213 Partial least squares (PLS) regression (dpeaa)DE-He213 |
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ddc 540 bkl 35.63 bkl 35.77 misc (radiata pine) misc Monosaccharide composition misc Lignin misc Attenuated total reflectance Fourier transform infrared (ATR FTIR) spectroscopy misc Transmission Fourier transform infrared (transmission FTIR) spectroscopy misc Partial least squares (PLS) regression |
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ddc 540 bkl 35.63 bkl 35.77 misc (radiata pine) misc Monosaccharide composition misc Lignin misc Attenuated total reflectance Fourier transform infrared (ATR FTIR) spectroscopy misc Transmission Fourier transform infrared (transmission FTIR) spectroscopy misc Partial least squares (PLS) regression |
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ddc 540 bkl 35.63 bkl 35.77 misc (radiata pine) misc Monosaccharide composition misc Lignin misc Attenuated total reflectance Fourier transform infrared (ATR FTIR) spectroscopy misc Transmission Fourier transform infrared (transmission FTIR) spectroscopy misc Partial least squares (PLS) regression |
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Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies |
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Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies |
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Fahey, Leona M. Nieuwoudt, Michél K. Harris, Philip J. |
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Fahey, Leona M. |
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predicting the cell-wall compositions of pinus radiata (radiata pine) wood using atr and transmission ftir spectroscopies |
| title_auth |
Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies |
| abstract |
Abstract Because plant cell walls vary in their polysaccharide compositions and lignin contents, their monosaccharide compositions and lignin contents are often determined, but these analyses are time consuming and laborious. We therefore investigated Fourier transform infrared (FTIR) spectroscopy coupled with partial least squares (PLS) regression analysis as a way of rapidly predicting the monosaccharide compositions and lignin contents of the cell walls of compression wood (CW) and opposite wood (OW) of the gymnosperm Pinus radiata. The effects were investigated of sample moisture content (ambient or dry) and sample particle size (large particles, < 0.422 mm or small particles, < 0.178 mm) of milled wood on attenuated total reflectance (ATR) and transmission FTIR spectra, as well as the PLS-1 models and subsequent predictions. PLS-1 models were built using mixtures of CW and OW as the training set, to provide a linear range of monosaccharide compositions and lignin contents. Models were externally validated by predicting another set of wood mixtures before predicting CW and OW of a separate test set. Most of the monosaccharide amounts in the separate test set were best predicted by ATR spectroscopy of ambient large particles, achieving the lowest standard error values for the monosaccharides arabinose (0.36%), xylose (1.05%), galactose (1.79%), glucose (6.32%), and 4-O-methylglucuronic acid (0.20%). The results show the feasibility of using ATR spectroscopy of ambient large particles for the rapid prediction of monosaccharide compositions and lignin contents of plant cell walls. |
| abstractGer |
Abstract Because plant cell walls vary in their polysaccharide compositions and lignin contents, their monosaccharide compositions and lignin contents are often determined, but these analyses are time consuming and laborious. We therefore investigated Fourier transform infrared (FTIR) spectroscopy coupled with partial least squares (PLS) regression analysis as a way of rapidly predicting the monosaccharide compositions and lignin contents of the cell walls of compression wood (CW) and opposite wood (OW) of the gymnosperm Pinus radiata. The effects were investigated of sample moisture content (ambient or dry) and sample particle size (large particles, < 0.422 mm or small particles, < 0.178 mm) of milled wood on attenuated total reflectance (ATR) and transmission FTIR spectra, as well as the PLS-1 models and subsequent predictions. PLS-1 models were built using mixtures of CW and OW as the training set, to provide a linear range of monosaccharide compositions and lignin contents. Models were externally validated by predicting another set of wood mixtures before predicting CW and OW of a separate test set. Most of the monosaccharide amounts in the separate test set were best predicted by ATR spectroscopy of ambient large particles, achieving the lowest standard error values for the monosaccharides arabinose (0.36%), xylose (1.05%), galactose (1.79%), glucose (6.32%), and 4-O-methylglucuronic acid (0.20%). The results show the feasibility of using ATR spectroscopy of ambient large particles for the rapid prediction of monosaccharide compositions and lignin contents of plant cell walls. |
| abstract_unstemmed |
Abstract Because plant cell walls vary in their polysaccharide compositions and lignin contents, their monosaccharide compositions and lignin contents are often determined, but these analyses are time consuming and laborious. We therefore investigated Fourier transform infrared (FTIR) spectroscopy coupled with partial least squares (PLS) regression analysis as a way of rapidly predicting the monosaccharide compositions and lignin contents of the cell walls of compression wood (CW) and opposite wood (OW) of the gymnosperm Pinus radiata. The effects were investigated of sample moisture content (ambient or dry) and sample particle size (large particles, < 0.422 mm or small particles, < 0.178 mm) of milled wood on attenuated total reflectance (ATR) and transmission FTIR spectra, as well as the PLS-1 models and subsequent predictions. PLS-1 models were built using mixtures of CW and OW as the training set, to provide a linear range of monosaccharide compositions and lignin contents. Models were externally validated by predicting another set of wood mixtures before predicting CW and OW of a separate test set. Most of the monosaccharide amounts in the separate test set were best predicted by ATR spectroscopy of ambient large particles, achieving the lowest standard error values for the monosaccharides arabinose (0.36%), xylose (1.05%), galactose (1.79%), glucose (6.32%), and 4-O-methylglucuronic acid (0.20%). The results show the feasibility of using ATR spectroscopy of ambient large particles for the rapid prediction of monosaccharide compositions and lignin contents of plant cell walls. |
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Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies |
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| score |
7.401602 |