Lifting Wavelet Transform De-noising for Model Optimization of Vis-NIR Spectroscopy to Predict Wood Tracheid Length in Trees

Li, Ying; Via, Brian K.; Cheng, Qiuming; Li, Yaoxiang

doi:10.3390/s18124306

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…LWT was used for de-noising and the optimal de-noising parameters were discussed in the prediction of wood tracheid length in our previous study [34]; the results demonstrated that LWT, as a second-generation wavelet, has more power to suppress noise and improve model performance when compared to other de-noising methods such as moving average, loess, Savitzky-Golay, and lowess. According to the advantages of LWT, the performance of models using wavelet coefficients of LWT and constructed spectra were first compared in this study.…”

Section: Discussionmentioning

confidence: 94%

“…Taking density as an example, 280 wood samples from two different geographical origins belonging to three of the major commercial species (i.e., Dahurian larch, Japanese elm, Chinese white poplar) were collected for spectra collection and density estimation. The spectra were processed by lifting wavelet transform (LWT) [34], and different reconstruct means for spectra (wavelet coefficients and constructed spectra after de-noising) were also compared. The support vector machine (SVM) optimized by particle swarm optimization (PSO) was used for tracking origin and identifying species.…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 94%

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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“…VIS and NIRS are suitable techniques to replace laborious and uneconomic conventional analyses, as they require no treatment of the sample, are rapid and readily assessed, and have the potential for automation and on-site application (Casson et al 2020, Santos et al 2021. In wood science, these techniques have proven useful in predicting chemical properties (Lengowski et al 2018), moisture content (Kobori et al 2013, Chen and, basic density (Li et al 2020, de Abreu Neto et al 2020, anatomical element sizes (Li et al 2018) and mechanical properties , de Abreu Neto et al 2020, as well as for identifying species (Nisgoski et al 2017). In the energy context, they are feasible for the assessment of biodiesel quality (Pilar Dorado et al 2011, Fernandes et al 2011, prediction of the heating value of manure (Preece et al 2013), and rapid determination of total petroleum hydrocarbon content (Douglas et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Performance of visible and Near-infrared spectroscopy to predict the energetic properties of wood

Gmach,

Jacobowski,

Bonfatti Júnior

et al. 2023

Maderas-Cienc Tecnol

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Wood can be used for fuel by direct burning, or as a raw material for other fuels; however, it is necessary to evaluate the energy properties to ensure the optimal use of this material. The most relevant characteristics to be analyzed are the higher heating value, volatile material content, fixed carbon content, and ash content. Along with the traditional methods, there are also non-destructive evaluations that are optimized for speed and reliability. Among these methods, visible spectroscopy and near-infrared spectroscopy have been proven to be robust for the prediction of several wood properties. The aim of this study was to evaluate the potential of visible spectroscopy and near-infrared spectroscopy for species discrimination and prediction of higher heating value, volatile material content, fixed carbon content, and ash content for Eucalyptus saligna, Eucalyptus dunnii, and Eucalyptus benthamii woods. For this purpose, multivariate principal component analysis and partial least squares regression were applied to the collected spectra. The principal component analysis satisfactorily discriminated the three species, explaining 99% of the variance of the visible spectroscopy spectra and 73% of that of the near-infrared spectra. The estimation of energetic properties through partial least squares regression was satisfactory for both visible spectroscopy and near-infrared spectroscopies, which presented calibration R² values close to 1 and low errors for all properties studied.

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“…The R 2 values were higher than 0.85 for soluble solids content, moisture content, and pH. In the forestry field, WT (Daubechies-5, db5) and LWT (db2) were used to optimize Populus davidiana and larch spectra, respectively in our previous studies (Li et al, 2018a;Li et al, 2018b). In this study, LWT with four wavelet functions including Haar, sym3, db3, and bior1.3 were compared simultaneously among various tree species from two locations.…”

Section: Discussionmentioning

confidence: 97%

Comparison of various chemometric methods on visible and near-infrared spectral analysis for wood density prediction among different tree species and geographical origins

Liu

Via²,

Han³

et al. 2023

Front. Plant Sci.

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Visible and near-infrared (Vis-NIR) spectroscopy has been widely applied in many fields for the qualitative and quantitative analysis. Chemometric techniques including pre-processing, variable selection, and multivariate calibration models play an important role to better extract useful information from spectral data. In this study, a new de-noising method (lifting wavelet transform, LWT), four variable selection methods, as well as two non-linear machine learning models were simultaneously analyzed to compare the impact of chemometric approaches on wood density determination among various tree species and geographical locations. In addition, fruit fly optimization algorithm (FOA) and response surface methodology (RSM) were employed to optimize the parameters of generalized regression neural network (GRNN) and particle swarm optimization-support vector machine (PSO-SVM), respectively. As for various chemometric methods, the optimal chemometric method was different for the same tree species collected from different locations. FOA-GRNN model combined with LWT and CARS deliver the best performance for Chinese white poplar of Heilongjiang province. In contrast, PLS model showed a good performance for Chinese white poplar collected from Jilin province based on raw spectra. However, for other tree species, RSM-PSO-SVM models can improve the performance of wood density prediction compared to traditional linear and FOA-GRNN models. Especially for Acer mono Maxim, when compared to linear models, the coefficient of determination of prediction set (Rp2) and relative prediction deviation (RPD) were increased by 47.70% and 44.48%, respectively. And the dimensionality of Vis-NIR spectral data was decreased from 2048 to 20. Therefore, the appropriate chemometric technique should be selected before building calibration models.

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Lifting Wavelet Transform De-noising for Model Optimization of Vis-NIR Spectroscopy to Predict Wood Tracheid Length in Trees

Cited by 8 publications

References 27 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

Performance of visible and Near-infrared spectroscopy to predict the energetic properties of wood

Comparison of various chemometric methods on visible and near-infrared spectral analysis for wood density prediction among different tree species and geographical origins

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