Prediction of Douglas-Fir Lumber Properties: Comparison between a Benchtop Near-Infrared Spectrometer and Hyperspectral Imaging System

Schimleck, Laurence R.; Dahlen, Joseph; Yoon, Seung-Chul; Lawrence, Kurt C.; Jones, Paul D.

doi:10.3390/app8122602

Cited by 8 publications

(5 citation statements)

References 54 publications

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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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“…Following the measurements described in [51], a radial arm saw was used to cut a block (38 mm × 89 mm × 51 mm longitudinally) from each piece of lumber for NIRS and HSI analysis. Only 390 blocks (Canada-88, Idaho-95, Oregon-109, Washington-98) were available (utilized in [18]). From the 390 blocks, a subsample of 100 was selected for this study with the aim of capturing the full range of values for MOE and SG and equally representing the different sampling locations.…”

Section: Specimen Preparation and Testingmentioning

confidence: 99%

“…It has been demonstrated that the spectral data provided by NIR-HSI and SWIR-HSI could produce wood property calibrations whose performance was equivalent to those obtained using a benchtop NIR spectrometer [18,23]. These results are encouraging although, even with recent developments in HSI technology, hyperspectral cameras typically record fewer wavelengths (i.e., spectral sampling interval-SSI) compared to NIR benchtop spectrometers.…”

Section: Introductionmentioning

confidence: 97%

“…Similarly, wood-related NIR-and SWIR-HSI research has been increasingly reported in the literature, where cameras have been used to examine various spectral and spatial properties and variations of wood materials. Applications include the estimation of physical and mechanical properties of radial strips [11][12][13][14][15] and lumber [16][17][18] as well as the detection of compression wood [19,20], tension wood [21] and resin defects [22]. A common area of research has been the estimation of wood moisture content [23][24][25], and associated research includes an investigation of changes in the spatial distribution of water when wood is dried [20].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Performance of NIR-Hyperspectral Imaging Systems

Schimleck

Dahlen

et al. 2022

Foundations

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Near-infrared spectroscopy (NIRS) allows for the rapid estimation of a wide range of wood properties. Typically, NIRS studies on wood have utilized benchtop spectrometers, but efforts to utilize NIR hyperspectral imaging to examine wood and wood products have increased. Compared to benchtop NIR systems, hyperspectral imaging has several advantages (speed, visualization of spatial variability), but the data typically have a lower signal-to-noise ratio as well as fewer wavelengths saved; thus, hyperspectral imaging systems have a larger spectral sampling interval (SSI). Furthermore, the SSI and wavelength range varies considerably among different HSI cameras. NIR-HSI systems based on indium gallium arsenide (InGaAs) detectors have a wavelength range typically from 900 to 1700 nm, while short-wave infrared hyperspectral imaging (SWIR-HSI) systems based on mercury cadmium telluride (MCT) detectors have the ‘full’ NIR wavelength range from 1000 to 2500 nm. These factors may influence the performance of wood property calibrations. We compared one NIR-HSI (900–1700 nm) and three SWIR-HSI (1000–2500 nm) commercially available cameras with an NIRS benchtop spectrometer (1100–2500 nm). The performance of specific gravity (SG) and stiffness (MOE) calibration models was compared with one-hundred Douglas-fir (Pseudotsuga menziesii) samples. The limited wavelength range of an NIR-HSI camera provided the best models for MOE, whereas the NIR-HSI and two SWIR-HSI cameras provided similar SG results. SWIR-HSI models heavily favored wavelengths greater than 1900 nm.

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“…In addition, distribution maps obtained using hyperspectral imaging techniques are now widely used in agricultural studies, forestry, meat quality testing, etc. [26][27][28][29]. Meanwhile, the use of hyperspectral imaging techniques for generating soil TAs concentration distribution maps using machine learning models remains to be studied [30].…”

Section: Introductionmentioning

confidence: 99%

Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil

Wei

Zhang

et al. 2020

Applied Sciences

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Soil total arsenic (TAs) contamination caused by human activities—such as mining, smelting, and agriculture—is a problem of global concern. Visible/near-infrared (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) do not need too much sample preparation and utilization of chemicals to evaluate total arsenic (TAs) concentration in soil. VNIR with hyperspectral imaging has the potential to predict TAs concentration in soil. In this study, 59 soil samples were collected from the Daye City mining area of China, and hyperspectral imaging of the soil samples was undertaken using a visible/near-infrared hyperspectral imaging system (wavelength range 470–900 nm). Spectral preprocessing included standard normal variate (SNV) transformation, multivariate scatter correction (MSC), first derivative (FD) preprocessing, and second derivative (SD) preprocessing. Characteristic bands were then identified based on Spearman’s rank correlation coefficients. Four regression models were used for the modeling prediction: partial least squares regression (PLSR) (R2 = 0.71, RMSE = 0.48), support vector machine regression (SVMR) (R2 = 0.78, RMSE = 0.42), random forest (RF) (R2 = 0.78, RMSE = 0.42), and extremely randomized trees regression (ETR) (R2 = 0.81, RMSE = 0.38). The prediction results were compared with the results of atomic fluorescence spectrometry methods. In the prediction results of the models, the accuracy of ETR using FD preprocessing was the highest. The results confirmed that hyperspectral imaging combined with Spearman’s rank correlation with machine learning models can be used to estimate soil TAs content.

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Prediction of Douglas-Fir Lumber Properties: Comparison between a Benchtop Near-Infrared Spectrometer and Hyperspectral Imaging System

Cited by 8 publications

References 54 publications

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

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

Comparative Performance of NIR-Hyperspectral Imaging Systems

Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil

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