Estimation of Pinus radiata D. Don tracheid morphological characteristics by near infrared spectroscopy

Holzforschung ◽  
2004 ◽  
Vol 58 (1) ◽  
pp. 66-73 ◽  
Author(s):  
L. R. Schimleck ◽  
R. Evans
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Wall Thickness ◽  
Pinus Radiata ◽  
Near Infrared ◽  
Morphological Characteristics ◽  
Predictive Ability ◽  
Increment Core ◽  
Calibration Samples ◽  
Strong Agreement

Abstract Eight Pinus radiata D. Don increment core samples were selected from a total of 32 cores for the development of calibrations for several tracheid morphological characteristics: coarseness, perimeter, radial and tangential diameter and wall thickness. Near infrared (NIR) spectra, obtained from the radial-longitudinal face of each core in 10-mm sections from pith to bark, were used to develop the calibrations. Calibrations for coarseness and wall thickness were excellent, with coefficients of determination (R2) of 0.91 and 0.89, respectively. Calibrations for the remaining characteristics were weaker (R2 ranged from 0.65 to 0.69). To test the predictive ability of the calibrations, two intact P. radiata increment cores (core A and B) were selected from the same set as the calibration samples. NIR-predicted tracheid coarseness and wall thickness were in strong agreement with measured (SilviScan-determined) values. Radial patterns of variation (NIR-predicted, measured) closely followed each other for both cores, but coarseness and wall thickness were underestimated for core B. Tracheid tangential perimeter was well predicted with R2 of 0.69 (core A) and 0.79 (core B). Relationships for the remaining characteristics were weak. Collection of NIR spectra in smaller increments, to capture more of the variation, may improve calibration.

Prediction of diet quality for sheep from faecal characteristics: comparison of near-infrared spectroscopy and conventional chemistry predictive models

2015 ◽  
Vol 55 (1) ◽  
pp. 1 ◽  
Author(s):  
D. G. Kneebone ◽  
G. McL. Dryden
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Predictive Ability ◽  
Validation Dataset ◽  
Least Squares Regression ◽  
Prediction Equations ◽  
Faecal Excretion ◽  
Excretion Rates

This study evaluated the ability of equations developed from the analysis of faecal material by conventional chemistry (F.CHEM), and by near-infrared spectroscopy (F.NIRS), to predict intake and digestibility of forages fed with or without supplements. In vivo datasets were obtained using 30 sheep and 25 diets to provide 124 diet–faecal pairs, with each sheep fed four or five of the diets. The diets were five forages fed alone or with urea, molasses, cottonseed meal or sorghum grain supplements. Ninety-nine diet–faecal pairs were selected at random, but ensuring that all diets were represented and both the F.CHEM and F.NIRS prediction equations were developed from this dataset. The remaining 25 diet–faecal pairs were used as a validation dataset. Regressions for F.CHEM were developed by stepwise regression, and F.NIRS prediction equations were developed by partial least-squares regression. Prediction equations based solely on faecal analyte concentrations (F.CHEMc) had poor predictive ability, and models incorporating faecal constituent excretion rates (F.CHEMe) were the best at predicting feed constituent intakes. These models had slightly lower standard errors of prediction (SEP) for organic matter (OM) intake and digestible OM intake compared with the F.NIRS models that did not include faecal excretion rates. However, F.NIRS models had lower SEP for protein intake and OM digestibility. Good agreement between the F.CHEMe and F.NIRS methods was evident (according to the 95% limits-of-agreement test), and both predicted the reference values precisely and with small bias. Equations derived from a dataset that included representatives of all diets used in the experiment gave much better prediction of diet characteristics than those developed from a dataset constructed entirely at random. Equations for F.NIRS developed in this way successfully predicted the characteristics of diets that included forages fed alone and with the type of supplements used in tropical Australia.

Prediction of browse nutritive attributes in a Pinus radiata D. Don silvopastoral system based on visible-near infrared spectroscopy

2018 ◽  
Vol 93 (1) ◽  
pp. 103-112 ◽  
Author(s):  
Sorkunde Mendarte ◽  
Maite Gandariasbeitia ◽  
Isabel Albizu ◽  
Santiago Larregla ◽  
Gerardo Besga
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Pinus Radiata ◽  
Near Infrared ◽  
Silvopastoral System

Stiffness Prediction of Radiata Pine Clearwood Test Pieces Using near Infrared Spectroscopy

2001 ◽  
Vol 9 (2) ◽  
pp. 117-122 ◽  
Author(s):  
Armin Thumm ◽  
Roger Meder
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Pinus Radiata ◽  
Modulus Of Elasticity ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Radiata Pine ◽  
Model Based ◽  
Test Pieces ◽  
The Mean

Near infrared (NIR) spectroscopy has been used to predict the modulus of elasticity (stiffness) of samples taken from knot-free sapwood specimens of radiata pine ( Pinus radiata D. Don). The method shows the potential of using NIR spectroscopy for assessment of lumber stiffness. A model based on NIR spectra taken on the radial face of 404 samples of radiata pine clearwood was established to predict stiffness. Samples were moved past the detector at a rate of 900 mm min−1. This model then was used to predict the stiffness of a further 80 samples and the results show an error in prediction of 14% of the mean measured value.

Analysis of Pinus radiata D. Don Bark by Near Infrared Spectroscopy

Holzforschung ◽  
2003 ◽  
Vol 57 (5) ◽  
pp. 520-526 ◽  
Author(s):  
L. R. Schimleck ◽  
Y. Yazaki
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Pinus Radiata ◽  
Near Infrared ◽  
Practical Importance ◽  
Nir Spectroscopy ◽  
Hot Water ◽  
Standard Errors ◽  
Test Set

Summary The estimation of a range of Pinus radiata D. Don bark properties by calibrated near infrared (NIR) spectroscopy is reported. A series of P. radiata samples were characterised in terms of hot water extractives, NaOH extractives and Stiasny value. NIR spectra were obtained from the milled bark of each sample and used to develop calibrations for each parameter. Coefficients of determination (R2) ranged from 0.84 (NaOH extractives) to 0.94 (Stiasny value). Standard errors of calibration ranged from 0.96 (NaOH extractives) to 2.47 (Stiasny value). When applied to a separate test set, the hot water extractives and Stiasny value calibrations performed well, while the NaOH calibration was disappointing. The calibration developed for Stiasny value could be of considerable practical importance as the method used to determine Stiasny value is particularly time consuming.

Determination of Ephedrine Hydrochloride in Mixtures of Ephedrine Hydrochloride and Pseudoephedrine Hydrochloride Using near Infrared Spectroscopy

2005 ◽  
Vol 13 (3) ◽  
pp. 155-160 ◽  
Author(s):  
Yanga K. Dijiba ◽  
Thomas M. Niemczyk
Keyword(s):  
Infrared Spectroscopy ◽  
Solid State ◽  
Near Infrared Spectroscopy ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Minor Component ◽  
Ephedrine Hydrochloride ◽  
Calibration Samples ◽  
Precision And Accuracy

There are a number of situations where there is a need to determine the concentrations of components in solid-state mixtures without dissolving the samples. The experiments described here were designed to demonstrate that diffuse reflectance near infrared spectroscopy coupled with partial least squares (PLS) data analysis can be used to determine the minor component in a mixture of structurally-similar solid-state compounds, in this case mixtures of ephedrine hydrochloride and pseudoephedrine hydrochloride. It is shown that when care is taken to produce homogeneous calibration samples very good results can be obtained; in this case, cross-validated standard error of predictions of 2.30 wt% when the minor component spanned the concentration range of 0 to 50 wt% and 0.30 wt% when the minor component spanned the concentration range of 0 to 5 wt%. Results are presented that indicate that the amount of data available to the PLS calibration routine relative to the range over which the calibration is performed can limit the precision and accuracy of the determinations.

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