Has the Time Finally Come for NIR in the Forestry Sector?

Meder, Roger; Schimleck, Laurie

doi:10.1255/jnirs.956

Cited by 10 publications

(9 citation statements)

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“…Visible and near infrared (Vis-NIR) spectroscopy has been widely used in many sectors, including the food, material, and life sciences [7][8][9]. In the field of forestry, many studies have demonstrated its potential to determine components, such as moisture, density, lignin content, and so on; detect wood preservation; and classify species [10][11][12][13][14][15][16]. Sandak et al applied cluster analysis (CA) and principal component analysis (PCA) to classify powdered samples (fraction < 0.5 mm) and wood samples, respectively.…”

Section: Introductionmentioning

confidence: 99%

Visible-Near Infrared Spectroscopy and Chemometric Methods for Wood Density Prediction and Origin/Species Identification

Via

Young

et al. 2019

Forests

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This study aimed to rapidly and accurately identify geographical origin, tree species, and model wood density using visible and near infrared (Vis-NIR) spectroscopy coupled with chemometric methods. A total of 280 samples with two origins (Jilin and Heilongjiang province, China), and three species, Dahurian larch (Larix gmelinii (Rupr.) Rupr.), Japanese elm (Ulmus davidiana Planch. var. japonica Nakai), and Chinese white poplar (Populus tomentosa carriere), were collected for classification and prediction analysis. The spectral data were de-noised using lifting wavelet transform (LWT) and linear and nonlinear models were built from the de-noised spectra using partial least squares (PLS) and particle swarm optimization (PSO)-support vector machine (SVM) methods, respectively. The response surface methodology (RSM) was applied to analyze the best combined parameters of PSO-SVM. The PSO-SVM model was employed for discrimination of origin and species. The identification accuracy for tree species using wavelet coefficients were better than models developed using raw spectra, and the accuracy of geographical origin and species was greater than 98% for the prediction dataset. The prediction accuracy of density using wavelet coefficients was better than that of constructed spectra. The PSO-SVM models optimized by RSM obtained the best results with coefficients of determination of the calibration set of 0.953, 0.974, 0.959, and 0.837 for Dahurian larch, Japanese elm, Chinese white poplar (Jilin), and Chinese white poplar (Heilongjiang), respectively. The results showed the feasibility of Vis-NIR spectroscopy coupled with chemometric methods for determining wood property and geographical origin with simple, rapid, and non-destructive advantages.

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Section: Introductionmentioning

confidence: 99%

Visible-Near Infrared Spectroscopy and Chemometric Methods for Wood Density Prediction and Origin/Species Identification

Via

Young

et al. 2019

Forests

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“…In the last 20 or more years, a large number of properties have successfully been determined via NIR spectroscopy and these have been reviewed extensively. 2,3 However, despite the numerous applications, there is still very little implementation in the forestry sector 4,5 and that may be due to the cost of developing reliable calibrations. In Australia, the federal government research organisation, CSIRO, has invested in excess of $1.5 million to obtain the KPY reference data for multiple species of eucalyptus, across multiple geographies (Australia, South Africa and South America) and multiple age classes.…”

Section: The Analytical Challengementioning

confidence: 99%

The Magnitude of Tree Breeding and the Role of near Infrared Spectroscopy

Meder

2015

NIR news

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“…While imaging applications in the agriculture, food and pharmaceutical sectors were prominent until recently, research efforts in the fields of wood processing and medicine are now emerging. Three excellent and comprehensive special issues in the Journal of Near Infrared Spectroscopy on wood 18,19 and medicine 20 have illustrated the use of NIR spectroscopy and NIR hyperspectral imaging in these fields. Inclusion of papers on wood and medicine is thus unavoidable in this special issue focussing only on NIR hyperspectral imaging.…”

Section: Overview Of Papersmentioning

confidence: 99%