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

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1078
Author(s):  
Ying Li ◽  
Brian K. Via ◽  
Tim Young ◽  
Yaoxiang Li
Keyword(s):  
Tree Species ◽  
Near Infrared ◽  
Geographical Origin ◽  
Nir Spectroscopy ◽  
Dahurian Larch ◽  
Chemometric Methods ◽  
White Poplar ◽  
Chinese White ◽  
Chinese White Poplar ◽  
Better Than

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.

scholarly journals Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy

Forests ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 62
Author(s):  
Ying Li ◽  
Guozhong Wang ◽  
Gensheng Guo ◽  
Yaoxiang Li ◽  
Brian K. Via ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Variable Selection ◽  
Wood Density ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Support Vector ◽  
White Poplar ◽  
Chinese White ◽  
Calibration Methods ◽  
Chinese White Poplar

Wood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood density by visible and near infrared (Vis-NIR) spectroscopy. The objective of this study was to select appropriate pre-processing, variable selection and multivariate calibration techniques to improve the prediction accuracy of density in Chinese white poplar (Populus tomentosa carriere) wood. The Vis-NIR spectra were de-noised using four methods (lifting wavelet transform, LWT; wavelet transform, WT; multiplicative scatter correction, MSC; and standard normal variate, SNV), and four variable selection techniques, including successive projections algorithm (SPA), uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV), were compared to simplify the dimension of the high-dimensional spectral matrix. The non-linear models of generalized regression neural network (GRNN) and support vector machine (SVM) were performed using these selected variables. The results showed that the best prediction was obtained by GRNN models combined with the LWT and CARS method for Chinese white poplar wood density (Rp2 = 0.870; RMSEP = 13 Kg/m3; RPDp = 2.774).

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

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1233
Author(s):  
Yongbin Meng ◽  
Yuanyuan Zhang ◽  
Chunxu Li ◽  
Jinghan Zhao ◽  
Zichun Wang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Carbon Content ◽  
Spectral Data ◽  
Mean Square Error ◽  
Tree Species ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Mean Square ◽  
Chemometric Methods ◽  
Full Spectrum

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.

scholarly journals Tracing the Geographical Origin of Durum Wheat by FT-NIR Spectroscopy

Foods ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 450 ◽  
Author(s):  
Annalisa De Girolamo ◽  
Marina Cortese ◽  
Salvatore Cervellieri ◽  
Vincenzo Lippolis ◽  
Michelangelo Pascale ◽  
...  
Keyword(s):  
Durum Wheat ◽  
Near Infrared ◽  
Southern Italy ◽  
Geographical Origin ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
North Central ◽  
Linear Discriminant ◽  
The World

Fourier transform near infrared (FT-NIR) spectroscopy, in combination with principal component-linear discriminant analysis (PC-LDA), was used for tracing the geographical origin of durum wheat samples. The classification model PC-LDA was applied to discriminate durum wheat samples originating from Northern, Central, and Southern Italy (n = 181), and to differentiate Italian durum wheat samples from those cultivated in other countries across the world (n = 134). Developed models were validated on a separated set of wheat samples. Different pre-treatments of spectral data and different spectral regions were selected and compared in terms of overall discrimination (OD) rates obtained in validation. The LDA models were able to correctly discriminate durum Italian wheat samples according to their geographical origin (i.e., North, Central, and South) with OD rates of up of 96.7%. Better results were obtained when LDA models were applied to the discrimination of Italian durum wheat samples from those originating from other countries across the world, having OD rates of up to 100%. The excellent results obtained herein clearly indicate the potential of FT-NIR spectroscopy to be used for the discrimination of durum wheat samples according to their geographical origin.

A comparative study of the stomata and PM2.5particles on the leaf surface of Chinese white poplar (Populus tomentosa Carr.) in different polluted places

2015 ◽  
Vol 35 (22) ◽  
Author(s):  
石婕 SHI Jie ◽  
刘庆倩 LIU Qingqian ◽  
安海龙 AN Hailong ◽  
曹学慧 CAO Xuehui ◽  
刘超 LIU Chao ◽  
...  
Keyword(s):  
Comparative Study ◽  
Leaf Surface ◽  
Populus Tomentosa ◽  
White Poplar ◽  
Chinese White ◽  
Populus Tomentosa Carr ◽  
Chinese White Poplar

The rapid determination of total polyphenols content and antioxidant activity in Dendrobium officinale using near-infrared spectroscopy

2016 ◽  
Vol 8 (23) ◽  
pp. 4584-4589 ◽  
Author(s):  
Longhui Ma ◽  
Zhimin Zhang ◽  
Xingbing Zhao ◽  
Sufeng Zhang ◽  
Hongmei Lu
Keyword(s):  
Antioxidant Activity ◽  
Infrared Spectroscopy ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Dendrobium Officinale ◽  
Chemometric Methods ◽  
Total Polyphenols ◽  
Rapid Quantification

NIR spectroscopy coupled with chemometric methods for rapid quantification of total polyphenols content and antioxidant activity inDendrobium officinale.

Insect-resistant mechanism of transgenic triploid of Chinese white poplar

2006 ◽  
Vol 1 (2) ◽  
pp. 196-200 ◽  
Author(s):  
Shengliang Yuan ◽  
Baojia Gao ◽  
Na Zhang
Keyword(s):  
White Poplar ◽  
Chinese White ◽  
Insect Resistant ◽  
Chinese White Poplar

scholarly journals Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning

2019 ◽  
Vol 9 (6) ◽  
pp. 1111 ◽  
Author(s):  
Meng Lei ◽  
Zhongyu Rao ◽  
Ming Li ◽  
Xinhui Yu ◽  
Liang Zou
Keyword(s):  
Near Infrared ◽  
Geographical Origin ◽  
Learning Algorithm ◽  
Nir Spectroscopy ◽  
Chemical Compositions ◽  
Important Indicator ◽  
Quantitative And Qualitative Research ◽  
Quality Grading ◽  
Improved Model ◽  
Origin Identification

Geographical origin, an important indicator of the chemical composition and quality grading, is one essential factor that should be taken into account in evaluating coal quality. However, traditional coal origin identification methods based on chemistry experiments are not only time consuming and labour intensive, but also costly. Near-Infrared (NIR) spectroscopy is an effective and efficient way to measure the chemical compositions of samples and has demonstrated excellent performance in various fields of quantitative and qualitative research. In this study, we employ NIR spectroscopy to identify coal origin. Considering the fact that the NIR spectra of coal samples always contain a large amount of redundant information and the number of samples is small, the broad learning algorithm is utilized here as the modelling system to classify the coal geographical origin. In addition, the particle swarm optimization algorithm is introduced to improve the structure of the Broad Learning (BL) model. We compare the improved model with the other five multivariate classification methods on a dataset with 243 coal samples collected from five countries. The experimental results indicate that the improved BL model can achieve the highest overall accuracy of 97.05%. The results obtained in this study suggest that the NIR technique combined with machine learning methods has significant potential for further development of coal geographical origin identification systems.

scholarly journals The Combined Optimization of Savitzky-Golay Smoothing and Multiplicative Scatter Correction for FT-NIR PLS Models

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Huazhou Chen ◽  
Qiqing Song ◽  
Guoqiang Tang ◽  
Quanxi Feng ◽  
Liang Lin
Keyword(s):  
Latent Variables ◽  
Near Infrared ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Spectroscopy Analysis ◽  
Multiplicative Scatter Correction ◽  
Pls Modeling ◽  
Smoothing Parameters ◽  
Better Than

The combined optimization of Savitzky-Golay (SG) smoothing and multiplicative scatter correction (MSC) were discussed based on the partial least squares (PLS) models in Fourier transform near-infrared (FT-NIR) spectroscopy analysis. A total of 5 cases of separately (or combined) using SG smoothing and MSC were designed and compared for optimization. For every case, the SG smoothing parameters were optimized with the number of PLS latent variables (F), with an expanded number of smoothing points. Taking the FT-NIR analysis of soil organic matter (SOM) as an example, the joint optimization of SG smoothing and MSC was achieved based on PLS modeling. The results showed that the optimal pretreatment was successively using SG smoothing and MSC, in which the SG smoothing parameters were 4th degree of polynomial, 2nd-order derivative, and 67 smoothing points, the best corresponding F, RMSEP, and RP were 7, 0.3982 (%), and 0.8862, respectively. This result was far better than those without any pretreatment. The combined optimization of SG smoothing and MSC could obviously improve the modeling result for NIR analysis of SOM. In addition, a new method for the classification of calibration and prediction was proposed by normalization principle. The optimizations were done on this basis of this classification.

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