New Pretreatment Methods for Visible–Near-Infrared Calibration Modeling of Air-Dry Density of <i>Ulmus pumila</i> Wood

Liu, Ying; Via, Brian K.; Cheng, Qiuming; Zhao, Jinghan; Li, Yaoxiang

doi:10.13073/fpj-d-19-00004

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Wood density is an important physical property to test the mechanical properties of wood ( Li et al., 2019 ), and it is also an important indicator to identify the quality of wood ( Thomas et al., 2009 ). From the perspective of forestry, wood density can be used to predict the physical and mechanical properties of wood, such as hygroexpansion, hardness, and strength ( Missanjo and Matsumura, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning

Zhang

Liu

2022

Front. Plant Sci.

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Wood density, as a key indicator to measure wood properties, is of weighty significance in enhancing wood utilization and modifying wood properties in sustainable forest management. Visible–near-infrared (Vis-NIR) spectroscopy provides a feasible and efficient solution for obtaining wood density by the advantages of its efficiency and non-destructiveness. However, the spectral responses are different in wood products with different moisture content conditions, and changes in external factors may cause the regression model to fail. Although some calibration transfer methods and convolutional neural network (CNN)-based deep transfer learning methods have been proposed, the generalization ability and prediction accuracy of the models still need to be improved. For the prediction problem of Vis-NIR wood density in different moisture contents, a deep transfer learning hybrid method with automatic calibration capability (Resnet1D-SVR-TrAdaBoost.R2) was proposed in this study. The disadvantage of overfitting was avoided when CNN processes small sample data, which considered the complex exterior factors in actual production to enhance feature extraction and migration between samples. Density prediction of the method was performed on a larch dataset with different moisture content conditions, and the hybrid method was found to achieve the best prediction results under the calibration samples with different target domain calibration samples and moisture contents, and the performance of models was better than that of the traditional calibration transfer and migration learning methods. In particular, the hybrid model has achieved an improvement of about 0.1 in both R2 and root mean square error (RMSE) values compared to the support vector regression model transferred by piecewise direct standardization method (SVR+PDS), which has the best performance among traditional calibration methods. To further ascertain the generalizability of the hybrid model, the model was validated with samples collected from mixed moisture contents as the target domain. Various experiments demonstrated that the Resnet1D-SVR-TrAdaBoost.R2 model could predict larch wood density with a high generalization ability and accuracy effectively but was computation consuming. It showed the potential to be extended to predict other metrics of wood.

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Section: Introductionmentioning

confidence: 99%

Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning

Zhang

Liu

2022

Front. Plant Sci.

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“…Due to its advantages of a simple operational process, accurate analysis, strong model generalization ability, and real-time online monitoring, Vis-NIRS can meet the needs of rapid and accurate determination of large numbers of samples in an area [12]. The analytical information carried in the near-infrared spectral region is mainly the frequency multiplication and combination characteristic information from the vibrations of hydrogen-containing groups in the molecule [13,14]. Therefore, Vis-NIRS testing can obtain the wood mechanical properties without damaging the wood and its original shape, structure and state.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Property Prediction of Larix gmelinii Wood Based on Vis-Near-Infrared Spectroscopy

Zhao

et al. 2022

Forests

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Larix gmelinii is the major tree species in Northeast China. The wood properties of different Larix gmelinii are quite different and under strong genetic controls, so it can be better improved through oriented breeding. In order to detect the longitudinal compressive strength (LCS), modulus of rupture (MOR) and modulus of elasticity (MOE) in real-time, fast and non-destructively, a prediction model of wood mechanical properties with high precision and stability is constructed based on visible-near-infrared spectroscopy (Vis-NIRS) technology. The featured wavelengths were selected with the algorithms of competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), uninformative variable elimination (UVE), synergy interval partial least squares (SiPLS) and their combinations. The prediction models were then developed based on the partial least square regression (PLSR). The predictive ability of models was evaluated with coefficient of determination (R2) and root mean square error (RMSE). It indicated that CARS performed the best among the four methods examined in terms of wavelength-variable selection. The combined featured wavelength selecting method of SiPLS-CARS showed better performance than the single wavelength selection method. The optimal models of LCS, MOR and MOE are the SiPLS-CARS-PLSR model, with the R2 of the calibration set and the validation set are both greater than 0.99, and RMSE the smallest. The NIR optimal models for wood mechanical properties predictions has high predictive accuracy and good robustness.

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“…Coupled with suitable chemo-metric methods, NIR has been successfully applied in many fields such as the petrochemical, agriculture, food, pharmaceutical, and traditional Chinese medicine industries [13]. VIS-NIRS is the absorption spectrum of frequency multiplication and frequency synthesis of different H-base groups [14,15]. The application of visible near infrared reflectance spectroscopy (VIS-NIRS) to characterize the physical and chemical information of soil samples has become a popular method to predict soil properties and components.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Prediction of Soil Organic Matter Content Based on Visible-Near-Infrared Spectroscopy

Zhao

et al. 2021

Forests

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In order to explore the ever-changing law of soil organic matter (SOM) content in the forest of the Greater Khingan Mountains, a prediction model of the SOM content with a high accuracy and stability has been developed based on visible near-infrared (VIS-NIR) technology and multiple regression analysis. A total of 105 soil samples were collected from Cuifeng forest farm in Jagdaqi City, Greater Khingan Mountains region, Heilongjiang Province, China. Five classical preprocessing algorithms, including Savitzky−Golay convolution smoothing (S-G smoothing), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative, second derivative, and the combinations of the above five methods were applied to the raw spectra. Wavelengths were optimized with five methods of competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), uninformative variable elimination (UVE), synergy interval partial least square (SiPLS), and their combinations, and PLS models were developed accordingly. The results showed that when S-G smoothing is combined with SNV or MSC, both preprocessing strategies can improve the performance of the model. The prediction accuracy of SiPLS-PLS model and SiPLS-UVE-PLS model for the SOM content is higher than for other models, withan Rc2 of 0.9663 and 0.9221, RMSEC of 0.0645 and 0.0981, Rv2 of 0.9408 and 0.9270, and RMSEV of 0.0615 and 0.0683, respectively. The pretreatment strategies and characteristic variable selection methods used in this study could significantly improve the model performance and predicting efficiency.

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New Pretreatment Methods for Visible–Near-Infrared Calibration Modeling of Air-Dry Density of Ulmus pumila Wood

Cited by 6 publications

References 20 publications

Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning

Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning

Mechanical Property Prediction of Larix gmelinii Wood Based on Vis-Near-Infrared Spectroscopy

Modeling and Prediction of Soil Organic Matter Content Based on Visible-Near-Infrared Spectroscopy

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