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

Li, Ying; Via, Brian K.; Young, Timothy M.; Li, Yaoxiang

doi:10.3390/f10121078

Cited by 20 publications

(12 citation statements)

References 61 publications

(65 reference statements)

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“…The mean of 90° angles model (0.81, range: 0.77–0.84) in our results was mostly equivalent to the result reported in multiple wood chips (R 2 = 0.89) [ 39 ] and Dahurian larch (R 2 = 0.84), Japanese elm (R 2 = 0.83) and Chinese white poplar (R 2 = 0.84) [ 40 ] when using the VIS–NIR/NIR spectroscopy to predict WBD. The spectra from 90° angles (transverse) shown the best results compared to other angles for the prediction of WBD, the 45° and 0° angles showed similar lower prediction results than the 90° angle model.…”

Section: Discussionsupporting

confidence: 89%

Spectrometric prediction of wood basic density by comparison of different grain angles and variable selection methods

Liu

Cao

et al. 2021

Plant Methods

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Background Wood basic density (WBD) is one of the most crucial wood property in tree and mainly determined the end use of wood for industry. However, the measurement WBD is time- and cost-consuming, which an alternatively fast and no-destructive measurement is needed. In this study, capability of NIR spectroscopy combined with partial least squares regression (PLSR) to quantify the WBD were examined in multiple wood species. To obtain more accurate and robust prediction models, the grain angle (0° (transverse surface), 45°, 90° (radial surface)) influence on the collection of solid wood spectra and a comparison of found variable selection methods for NIR spectral variables optimization were conducted, including significant Multivariate Correlation (sMC), Regularized elimination procedure (Rep), Iterative predictor weighting (Ipw) and Genetic algorithm (Ga). Models made by random calibration data selection were conducted 200 times performance evaluation. Results These results indicate that 90° angle models display relatively highest efficiency than other angle models, mixed angle model yield a satisfied WBD prediction results as well and could reduce the influence of grain angle. Rep method shows a higher efficiency than other methods which could eliminate the uninformative variables and enhance the predictive performance of 90° angle and mix angle models. Conclusions This study is potentially shown that the WBD (g/cm3) on solid wood across grain angles and varies wood species could be measured in a rapid and efficient way using NIR technology. Combined with the PLSR model, our methodology could serve as a tool for wood properties breeding and silviculture study.

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Section: Discussionsupporting

confidence: 89%

Spectrometric prediction of wood basic density by comparison of different grain angles and variable selection methods

Liu

Cao

et al. 2021

Plant Methods

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“…Further, the selection of appropriate parameter values is essential to the model performance. In former research, the particle swarm optimization (PSO) procedure has been well used in SVM parameter selection [55,56]. In this study, we chose the RBF as kernel function and the PSO to search the optional parameter.…”

Section: Modeling Methodsmentioning

confidence: 99%

Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm

Jiang

et al. 2021

Agriculture

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The adulteration in Chinese chestnuts affects the quality, taste, and brand value. The objective of this study was to explore the feasibility of the hyperspectral imaging (HSI) technique to determine the geographical origin of Chinese chestnuts. An HSI system in spectral range of 400–1000 nm was applied to identify a total of 417 Chinese chestnuts from three different geographical origins. Principal component analysis (PCA) was preliminarily used to investigate the differences of average spectra of the samples from different geographical origins. A deep-learning-based model (1D-CNN, one-dimensional convolutional neural network) was developed first, and then the model based on full spectra and optimal wavelengths were established for various machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and particle swarm optimization-support vector machine (PSO-SVM). The optimal results based on full spectra for 1D-CNN, PLS-DA, and PSO-SVM models were 97.12%, 97.12%, and 95.68%, respectively. Competitive adaptive reweighted sampling (CARS) and a successive projections algorithm (SPA) were individually utilized for wavelengths selection, and the results of simplified models generally improved. The contrasting results demonstrated that the prediction accuracies of SPA-PLS-DA and 1D-CNN both reached 97.12%, but 1D-CNN presented a higher Kappa coefficient value than SPA-PLS-DA. Meanwhile, the sensitivities and specificities of SPA-PLS-DA and 1D-CNN models were both above 90% for the samples from each geographical origin. These results indicated that both SPA-PLS-DA and 1D-CNN models combined with HSI have great potential for the geographical origin identification of Chinese chestnuts.

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“…Each sample was then scanned three times with a glare probe (unit 6523 h.i. for the contact probe), and the average spectrum was considered the raw spectrum [31]. The signals were generated in the reflectance mode and transformed into absorbance using log1/R.…”

Section: Spectra Collectionmentioning

confidence: 99%

“…Such spectroscopic models have been derived from a wide range of wood attributes, especially those related to organic molecules and the physical properties of wood, such as wood lignin content, wood density, and the bending strength of wood. Li Ying et al applied Vis-NIRS and chemometric methods to accomplish wood-density predictions and origin/species identification [31,32]. Steffen Herrmann's research results showed that NIRS models were suitable for predicting concentrations of C, N, P, lignin, and extractives of coarse woody debris [33].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

Meng

Zhang

et al. 2021

Forests

Self Cite

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This study aimed to measure the carbon content of tree species rapidly and accurately using visible and near-infrared (Vis-NIR) spectroscopy coupled with chemometric methods. Currently, the carbon content of trees used for calculating the carbon storage of forest trees in the study of carbon sequestration is obtained by two methods. One involves measuring carbon content in the laboratory (K2CrO7-H2SO4 oxidation method or elemental analyzer), and another involves directly using the IPCC (Intergovernmental Panel on Climate Change) default carbon content of 0.45 or 0.5. The former method is destructive, time-consuming, and expensive, while the latter is subjective. However, Vis-NIR detection technology can avoid these shortcomings and rapidly determine carbon content. In this study, 96 increment core samples were collected from six tree species in the Heilongjiang province of China for analysis. The spectral data were preprocessed using seven methods, including extended multiplicative scatter correction (EMSC), first derivative (1D), second derivative (2D), baseline correction, de-trend, orthogonal signal correction (OSC), and normalization to eliminate baseline drifting and noise, as well as to enhance the model quality. Linear models were established from the spectra using partial least squares regression (PLS). At the same time, we also compared the effects of full-spectrum and reduced spectrum on the model’s performance. The results showed that the spectral data processed by 1D with the full spectrum could obtain a better prediction model. The 1D method yielded the highest R2c of 0.92, an RMSEC (root-mean-square error of calibration) of 0.0056, an R2p of 0.99, an RMSEP (root-mean-square error of prediction) of 0.0020, and the highest RPD (residual prediction deviation) value of 8.9. The results demonstrate the feasibility of Vis-NIR spectroscopy coupled with chemometric methods in determining the carbon content of tree species as a simple, rapid, and non-destructive method.

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

Cited by 20 publications

References 61 publications

Spectrometric prediction of wood basic density by comparison of different grain angles and variable selection methods

Spectrometric prediction of wood basic density by comparison of different grain angles and variable selection methods

Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm

Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

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