Measurement of intra-ring wood density by means of imaging VIS/NIR spectroscopy (hyperspectral imaging) (original) (raw)
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Evaluation of intra-ring wood density profiles using NIRS: comparison with the X-ray method
Annals of Forest Science, 2017
& Key message Pith-to-bark wood density profiling is interesting in forestry science. By comparing it with the X-ray method, this study proved that a fiber optic NIR spectrometer with a high-precision displacement system could accurately measure intra-ring wood density with a spatial resolution of 0.5 mm. & Context Most near-infrared spectroscopy (NIRS) studies for wood density determination use samples that have been pulverized beforehand. Attenuation of ionizing radiation is still the standard method to determine wood density with high spatial resolution. However, there is evidence that NIRS could be an accurate and affordable method for determining intraring density in solid wood strips. & Aims In this study, we research whether the results published for intra-ring density predictions in wood can be improved when calibrated with X-ray microdensitometry. & Methods The measurements were made using a fiber optic probe with a separation between measurement points of 0.508 mm in a range between 1200 and 2200 nm. A total of 4520 density points were used to create partial least squares regression (PLSR). X-ray densitometry data were used as reference values. Twenty PLSR calibrations were randomly executed on 31 samples collected from 28 Pinus radiata D. Don trees. & Results Upon selecting 20 latent variables, the R 2 value was 0.873 for the training group and 0.895 for the validation group, while RMSEP values are 43.1 × 10 −3 and 47.1 × 10 −3 g cm −3 for the training and validation groups, respectively. The range error ratio (RER) was 13.7. & Conclusion The RER was high and almost in the range suggested for quantification purposes. Results are superior to wood density studies in the literature which do not employ spatial resolution and to those found in studies using hyperspectral imaging.
Computers and Electronics in Agriculture, 2013
In this paper, a procedure for transforming hyperspectral imaging information into intra-growth ring wood densities is presented. Particular focus was given to comparing the neural network and Partial Least Squares Regression (PLSR) processing methods. The hyperspe ctral measurements were performed in a wavelength range of 380-1028 nm, with a spatial separation of 79 lm. The study employed 34 samples from the same number of Pinus pinea tree samples. Density values were analyzed at a total of 34,093 positions in the samples. For neural networks, the mean absolute percentage error (MAPE) and standard deviation of absolute percentage error (StdAPE) values were 6.49% and 5.43%, respectively. For the PLSR method the MAPE and StdAPE were 6.87% and 5.70%, respectively. The neural networks allow reducing the percentage of sample positions with large errors. The proposed method for density measure ment can be used for dendrochronology and dendroclimatolog y.
A portable method to estimate wood basic density from increment cores using spectroscopic techniques
Journal of Near Infrared Spectroscopy, 2010
The aim of this paper is to report the use of spectroscopic techniques for the measurement of basic wood density in Eucalyptus nitens, using samples in a state similar to that found in the field; so the current design can later be extended to a portable instrument that could eventually estimate the wood characteristics of a standing tree. Model calibration was carried out using wet samples taken from eight-year-old trees. Each sample was scanned to acquire two types of spectra: Raman and near infrared (NIR). Several pre-processing techniques were applied to the spectra in order to obtain the best possible prediction models for wood basic density using partial least squares (PLS) regression. The model selection criteria were based on maximising the coefficients of determination R2c and r2v and minimising the root mean square error ( RMSE). The potential of NIR for this purpose was demonstrated; r2v up to 0.87 and RMSEP down to 8.6 kg m−3 were obtained. Raman spectroscopy proved to be...
Forests
Loblolly pine (Pinus taeda L.) is one of the most important commercial timber species in the world. While the species is native to the southeastern United States of America (USA), it has been widely planted in southern Brazil, where it is the most commonly planted exotic species. Interest exists in utilizing nondestructive testing methods for wood property assessment to aid in improving the wood quality of Brazilian grown loblolly pine. We used near-infrared hyperspectral imaging (NIR-HSI) on increment cores to provide data representative of the radial variation of families sampled from a 10-year-old progeny test located in Rio Negrinho municipality, Santa Catarina, Brazil. Hyperspectral images were averaged to provide an individual NIR spectrum per tree for cluster analysis (hierarchical complete linkage with square Euclidean distance) to identify trees with similar wood properties. Four clusters (0, 1, 2, 3) were identified, and based on SilviScan data for air-dry density, microfi...
Near infrared hyperspectral imaging applied to mapping chemical composition in wood samples
Journal of Near Infrared Spectroscopy, 2010
This paper describes a method for the two-dimensional mapping of chemical composition on the transverse face of cross-sectional discs from trees. The method uses an imaging spectrograph coupled to a near infrared (NIR) camera (900-1700 nm) to obtain NIR hyper spectral data sets which are processed using partial least squares regression to visualise the distribution and variation of lignin, galactose and glucose in Pinus radiata discs with R 2 /standard error of performance values of 0.84/1.48 (lignin), 0.87/0.68 (galactose) and 0.87/0.95 (glucose). The hardware design and software control are described along with a method for calibration based on one dimension spatially resolved predictions of chemical composition from conventional NIR spectroscopy. The NIR imaging system was designed as a rapid and cost-effective means of mapping chemical composition over the entire disc at a spatial resolution of ~4 mm 2 /pixel. The resulting maps of chemical composition clearly indicate, at high spatial resolution, the extent of heterogeneity that occurs in logs.
CERNE, 2019
The objective of this study was to establish multivariate models for the prediction of wood basic density with reference to the values of average density of trees and near infrared (NIR) spectra measured in the breast height. The wood basic density of 39 Eucalyptus clones was determined in the laboratory by means of the mean longitudinal positions of 0%, 2%, 10%, 30%, 50% and 75% of the commercial height of the tree by the gravimetric method. NIR spectra were recorded using a spectrometer using optical fiber probe and integrating sphere directly on the transverse plane of the solid wood in disks collected from diameter at breast height and later in the sawdust. The performance of the NIR based models was evaluated according to the spectral acquisition method and sample preparation. The results showed that the best model for basic density estimation using indirect measurements was developed from the average spectra per clone measured in solid wood disks (R2cv of 0.77 and RMSEcv of 15 kg.m-³).
Near Infrared Spectroscopy for Estimating Wood Basic Density
Cerne, Lavras, 2009
ABSTRACT: Wood basic density is indicative of several other wood properties and is considered as a key feature for many industrial applications. Near infrared spectroscopy (NIRS) is a fast, efficient technique that is capable of estimating that property. However, it should be ...
Hyperspectral Imaging Surface Analysis for Dried and Thermally Modified Wood: An Exploratory Study
Journal of Spectroscopy, 2018
Naturally seasoned, kiln-dried, and thermally modified wood has been studied by hyperspectral near-infrared imaging between 980 and 2500 nm in order to obtain spatial chemical information. Evince software was used to explore, preprocess, and analyse spectral data from image pixels and link these data to chemical information via spectral wavelength assignment. A PCA model showed that regions with high absorbance were related to extractives with phenolic groups and aliphatic hydrocarbons. The sharp wavelength band at 2135 nm was found by multivariate analysis to be useful for multivariate calibration. This peak represents the largest variation that characterizes the knot area and can be related to areas in wood rich in hydrocarbons and phenol, and it can perhaps be used for future calibration of other wood surfaces. The discriminant analysis of thermally treated wood showed the strongest differentiation between the planed and rip-cut wood surfaces and a fairly clear discrimination bet...