Relationship between near-infrared (NIR) spectra and the geographical provenance of timber

Sandak, Anna; Sandak, Jakub; Negri, Martino

doi:10.1007/s00226-010-0313-y

Cited by 79 publications

(58 citation statements)

References 18 publications

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“…The second-derivative spectra of the untreated wood (solid line) displayed peaks in some regions with stronger intensities. However, the spectra of the ethanol-extracted samples (dotted line) showed less absorption, such as for the bands at 5976 cm -1 and 4686 cm -1 , which were determined to be closely related to the aromatic ring in the lignin and extractives (Michell et al 1996;Tsuchikawa and Siesler 2003;Workman and Weyer 2007;Sandak et al 2011). Overall, these combined results confirmed that the CE extractives were changed by the extraction pretreatment.…”

Section: Nir Model Developmentmentioning

confidence: 65%

“…Band 1 located at 5976 cm -1 was assigned to the Car-H 1 st stretching vibration of aromatic groups in the lignin (aromatic skeletal, or aromatic C-H in lignin) (Workman and Weyer 2007;Tsuchikawa and Siesler 2003;Sandak et al 2011). Band 2 located at 5936 cm -1 was assigned to the Car-H 1 st stretching vibration of the aromatic ring associated with aromatic skeletal structures in the lignin (Tsuchikawa and Siesler 2003;Workman and Weyer 2007;Sandak et al 2011). Band 3 located at 5848 cm -1 was assigned to the C-H 1 st stretching vibration of furanose/pyranose in the hemicellulose (Yonenobu and Tsuchikawa 2003).…”

Section: Nir Model Developmentmentioning

confidence: 99%

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Quantitative Prediction of Agarotetrol in Chinese Eaglewood Using Near Infrared Spectroscopy

Ding

et al. 2018

BioResources

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To overcome the numerous disadvantages of existing testing technology, a novel, fast, nondestructive, and quantitative technology for quality evaluation of Chinese eaglewood (CE) based on near-infrared (NIR) technology was proposed in this study. The extractives of CE were qualitatively analyzed to determine the types of volatile compounds using gas chromatography-mass spectroscopy and were quantitatively determined using high performance liquid chromatography (HPLC). Agarotetrol was quantitatively determined by the HPLC analysis. The content was found to range widely from 0.016 to 0.104 mg/g. A quantitative prediction model aimed at quality control was proposed based on the qualitative and quantitative results coupled with a partial least squares regression. The coefficient of correlation and residual predictive deviation of the prediction model were determined to be 0.9697 and 5.77, respectively. The practical tests showed an average error of 0.000327%, which indicated that the method was able to provide a novel, quick, and effective quality evaluation of CE.

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Section: Nir Model Developmentmentioning

confidence: 65%

Section: Nir Model Developmentmentioning

confidence: 99%

Quantitative Prediction of Agarotetrol in Chinese Eaglewood Using Near Infrared Spectroscopy

Ding

et al. 2018

BioResources

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“…The summary of site characteristics is presented in Table 1. The details for the procedure of sample harvesting, conditioning, and preparation was described previously (Sandak et al 2011). Experimental samples were obtained from adult wood zones ensuring an equal cambial age of all investigated samples.…”

Section: Methodsmentioning

confidence: 99%

“…Instead, clear evidences of variations to the molecular structure (as scrutinized with near-infrared spectra), due to the combined effect of tree provenance, environmental factors, and silvicultural practices, were previously reported by Sandak et al (2011).…”

Section: Chemical Compositionmentioning

confidence: 95%

Differences in wood properties of Picea abies L. Karst. in relation to site of provenance and population genetics

Sandak

Cantini

et al. 2014

Holzforschung

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Differences in wood properties of Picea abies L. Karst. in relation to site of provenance and population genetics Abstract: Chemical and physical characteristics of wood from six European populations of Picea abies were investigated taking into consideration their genetic differentiation determined by four microsatellites markers. The growth conditions of investigated spruce trees differed significantly. As a consequence, several adaptations were observed in samples from varying environments. The adaptation mechanisms include a variation to the annual rings morphology as well as the physical properties of the wood. It was proved that some properties, such as wood density or latewood ratio, are strictly correlated to the environmental variables. Results show that trees from geographically distinct populations present some diversity in the genotypes. It was also noticed that some trees did not share their genetic profile with other plants from the same location due to high intrapopulation variability. Samples from Finland possessed the lowest level of internal differentiation, while samples from northern Poland presented the lowest allele richness and were most divergent from the others. It was not possible to correlate any wood properties (chemical or physical) to genetic features of the population due to high intrapopulation diversity.

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“…The technique can also be applied to distinguish different geographical origins (Sandak et al 2011, Nisgoski et al 2016, although some responses are influenced by hybridisation (Meder et al 2014) and genetic factors (Hein & Chaix 2014). When wood material is analysed, face, shape and particle size influence the spectra, and measurements taken at different points of a sample can produce variations (Brunner et al 1996, Nisgoski et al 2015b) but still allow distinction from other species.…”

Section: Use Of Visible and Near-infrared Spectroscopy F O R D I S C mentioning

confidence: 99%

Use of Visible and Near-Infrared Spectroscopy for Discrimination of Eucalypt Species by Examination of Solid Samples

Nisgoski¹,

Muniz²,

Gonçalves³

et al. 2017

JTFS

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Eucalypt species have high importance in the forestry sector in Brazil. The wood has wide range of uses, including pulp, charcoal, plywood panels and treated boards. However, eucalypt species are hard to identify in lumber, which can cause problems in industrial use. So, the aim of this study was to combine visible and near-infrared spectroscopy to discriminate wood samples of two Corymbia spp. and five Eucalyptus spp. Visible spectroscopy was performed according to the CIELab standard. The data were analysed using descriptive statistics and regression analysis. Near-infrared analyses were based on ASTM E1655-05. The data were analysed with the Unscrambler X chemometric program, in the raw form and after pre-processing with second derivative of Savitzky-Golay. Visible and near-infrared spectroscopy can be successfully applied to separate Eucalyptus and Corymbia species. In the visible spectroscopic analyses, it was possible to separate the wood samples into two colour groups, i.e. red/rose and grey/yellow/brown. In the near-infrared spectra it was possible to discriminate samples, and three groups associated with density of the species were formed. Both techniques were appropriate to distinguish species and their industrial use might solve practical problems.

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Relationship between near-infrared (NIR) spectra and the geographical provenance of timber

Cited by 79 publications

References 18 publications

Quantitative Prediction of Agarotetrol in Chinese Eaglewood Using Near Infrared Spectroscopy

Quantitative Prediction of Agarotetrol in Chinese Eaglewood Using Near Infrared Spectroscopy

Differences in wood properties of Picea abies L. Karst. in relation to site of provenance and population genetics

Use of Visible and Near-Infrared Spectroscopy for Discrimination of Eucalypt Species by Examination of Solid Samples

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