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).

Spectroscopy quantitative analysis cotton content of blend fabrics

2016 ◽  
Vol 28 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Xudong Sun ◽  
Mingxing Zhou ◽  
Yize Sun
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Content Type ◽  
Svm Model ◽  
Calibration Methods ◽  
Learning Machine ◽  
Mean Square Errors

Purpose – The purpose of this paper is to develop near infrared (NIR) techniques coupled with multivariate calibration methods to rapid measure cotton content in blend fabrics. Design/methodology/approach – In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with LS-SVM model. The correlation coefficient of prediction (r p ) and root mean square errors of prediction were 0.98 and 4.50 percent, respectively. Findings – The results suggest that NIR technique combining with LS-SVM method has significant potential to quantitatively analyze cotton content in blend fabrics. Originality/value – It may have commercial and regulatory potential to avoid time consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

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 Near-Infrared Spectroscopy Combined with Multivariate Calibration to Predict the Yield of Sesame Oil Produced by Traditional Aqueous Extraction Process

2017 ◽  
Vol 2017 ◽  
pp. 1-5
Author(s):  
Yong-Dong Xu ◽  
Yan-Ping Zhou ◽  
Jing Chen
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Extraction Process ◽  
Sesame Oil ◽  
Aqueous Extraction ◽  
Oil Yield ◽  
Support Vector ◽  
Calibration Model ◽  
Aqueous Extraction Process

Sesame oil produced by the traditional aqueous extraction process (TAEP) has been recognized by its pleasant flavor and high nutrition value. This paper developed a rapid and nondestructive method to predict the sesame oil yield by TAEP using near-infrared (NIR) spectroscopy. A collection of 145 sesame seed samples was measured by NIR spectroscopy and the relationship between the TAEP oil yield and the spectra was modeled by least-squares support vector machine (LS-SVM). Smoothing, taking second derivatives (D2), and standard normal variate (SNV) transformation were performed to remove the unwanted variations in the raw spectra. The results indicated that D2-LS-SVM (4000–9000 cm−1) obtained the most accurate calibration model with root mean square error of prediction (RMSEP) of 1.15 (%, w/w). Moreover, the RMSEP was not significantly influenced by different initial values of LS-SVM parameters. The calibration model could be helpful to search for sesame seeds with higher TAEP oil yields.

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

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
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