Non-destructive estimation of Pinus taeda L tracheid morphological characteristics for samples from a wide range of sites in Georgia

Jones, Paul D.; Schimleck, Laurence R.; Peter, Gary F.; Daniels, Richard F.; Clark, Alexander

doi:10.1007/s00226-005-0021-1

Cited by 34 publications

(33 citation statements)

References 16 publications

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“…In contrast, the SEP of the model for % fixed carbon was slightly lower than its SECV. Although errors associated with a model is generally expected to increase when used for predicting future unknown, this is not always the case [31]; as is apparent for the fixed carbon model in this study and other studies [22,23]. Except for the model for HHV, the others did not perform well when used to predict properties of the independent test set.…”

Section: Nir-model Calibration and Evaluation For Proximate Compositimentioning

confidence: 55%

“…According to the literature, this qualified all developed models to be used as a preliminary screening tool [28,29]. The model statistics for the chemical components were comparable to what was determined for lignin (SEC = 0.48%; SECV = 0.92%; R 2 = 0.85), cellulose (SEC = 1.03%; SECV = 1.86%; R 2 = 0.8), glucan (SEC = 1.09%; SECV = 1.96%; R 2 = 0.82) and hemicelluloses (SEC = 0.92%; SECV = 1.24%; R 2 = 0.59) for loblolly pine wood [23].…”

Section: Nir-model Calibration and Evaluation For Chemical Compositionmentioning

confidence: 99%

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High Throughput Screening of Elite Loblolly Pine Families for Chemical and Bioenergy Traits with Near Infrared Spectroscopy

Via

Gallagher

Billor

et al. 2018

Forests

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Pinus taeda L. (loblolly pine) dominates 13.4 million ha of US southeastern forests and contributes over $30 billion to the economy of the region. The species will also form an important component of the renewable energy portfolio as the United States seeks national and energy security as well as environmental sustainability. This study employed NIR-based chemometric models as a high throughput screening tool to estimate the chemical traits and bioenergy potential of 351 standing loblolly pine trees representing 14 elite genetic families planted on two forest sites. The genotype of loblolly pine families affected the chemical, proximate and energy traits studied. With a range of 36.7% to 42.0%, the largest genetic variation (p-value < 0.0001) was detected in the cellulose content. Furthermore, although family by site interactions were significant for all traits, cellulose was the most stable across the two sites. Considering that cellulose content has strong correlations with other properties, selecting and breeding for cellulose could generate some gains.

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Section: Nir-model Calibration and Evaluation For Proximate Compositimentioning

confidence: 55%

Section: Nir-model Calibration and Evaluation For Chemical Compositionmentioning

confidence: 99%

High Throughput Screening of Elite Loblolly Pine Families for Chemical and Bioenergy Traits with Near Infrared Spectroscopy

Via

Gallagher

Billor

et al. 2018

Forests

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“…Autoscaling of the data is calculated according to the follow equation: (13) Where s j is the standard deviation for the variable j. First derivative and second derivative are based on a Savitzky -Golay polynomial filter 41 .…”

Section: (12)mentioning

confidence: 99%

“…Near infrared (NIR) spectroscopy, has been used together with pattern recognition methods in order to solve problems about plants classification [10][11][12] , based on morphological 13,14 , chemical 15 and genetics properties 16 . NIR, has also been used on wood samples for forestry applications, specially in prediction of its chemical composition 7,13,[17][18][19][20] mechanical properties, 18,20 and species identification 6,21 .…”

Section: Introductionmentioning

confidence: 99%

Supervised Pattern Recognition Techniques for Classification of Eucalyptus Species From Leaves Nir Spectra

Castillo

Contreras

Freer

et al. 2008

J. Chil. Chem. Soc.

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Three supervised pattern recognition methods (SPRM) were evaluated to discriminate between Eucalyptus globulus and Eucalyptus nitens species applying near infrared (NIR) spectroscopy on leaves. The methods used were k-nearest neighbor (KNN), soft modeling class analogy (SIMCA) and discriminant partial least squares (PLS-DA). First and second derivatives were used as transform techniques and mean-center (MC) and autoscaling (AS) as preprocessing techniques. The training set was constitued by 288 samples and 20 samples were used as validation set. A significant difference between the assayed methods was not observed, however best results for separation of classes and prediction rate were obtained when first derivative and MC were used for all the recognition pattern methods. Use of leaves and NIR spectroscopy avoids the destructive usual wood analysis in forest industries and facilities the fast classification of these species for forest applications.

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“…Este resultado es coincidente con lo informado por Melo et al (1988) para Pinus radiata de Chile y contrasta con lo informado también para madera de Pinus radiata en la que se reconoce un incremento de los diámetros de las traqueidas desde la médula a la corteza (Cown 1975). También en Pinus taeda , el diámetro tangencial aumentó en los primeros 100 mm desde la médula (Jones et al 2005).…”

Section: Diámetro De Traqueidas (1/diámetro 2 )unclassified

Madera De Compresión en Pinus Radiata D. Don: I, Características Anatómicas

Diaz-Vaz

Fernández

Valenzuela

et al. 2007

Maderas, Cienc. tecnol.

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Non-destructive estimation of Pinus taeda L tracheid morphological characteristics for samples from a wide range of sites in Georgia

Cited by 34 publications

References 16 publications

High Throughput Screening of Elite Loblolly Pine Families for Chemical and Bioenergy Traits with Near Infrared Spectroscopy

High Throughput Screening of Elite Loblolly Pine Families for Chemical and Bioenergy Traits with Near Infrared Spectroscopy

Supervised Pattern Recognition Techniques for Classification of Eucalyptus Species From Leaves Nir Spectra

Madera De Compresión en Pinus Radiata D. Don: I, Características Anatómicas

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