The Relationship between near Infrared Spectra of Radial Wood Surfaces and Wood Mechanical Properties

2001 ◽  
Vol 9 (4) ◽  
pp. 255-261 ◽  
Author(s):  
Wolfgang Gindl ◽  
Alfred Teischinger ◽  
Manfred Schwanninger ◽  
Barbara Hinterstoisser
Keyword(s):  
Mechanical Properties ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra ◽  
The Relationship ◽  
Wood Surfaces

A data-driven near infrared calibration process including near infrared spectral thumbprints

2018 ◽  
Vol 26 (3) ◽  
pp. 169-185
Author(s):  
Sharon Nielsen ◽  
Kenneth G Russell ◽  
Alison Kelly ◽  
Glen Fox
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Calibration Model ◽  
Calibration Process ◽  
Near Infrared Spectra ◽  
Infrared Spectral ◽  
Highly Correlated ◽  
Complex Relationships ◽  
High Level ◽  
The Relationship

Near infrared spectra are highly correlated, complex and noisy, and potentially have many more predictor variables than are required to estimate a parsimonious calibration equation. It is difficult to appreciate the implication of pre-processing choices that are made during calibration, especially in connection with the relationship between the transformed data and the reference values. Graphical methods can be used to understand these relationships better and decisions made during the calibration process can be based on the data alone. In this paper, new graphical tools are introduced to help the researcher better understand these complex relationships in the data. When combined with the proposed algorithm to explore spectra in relation to calibration, these tools enable a parsimonious calibration model to be formed. The results from two different (diesel and wheat) near infrared spectra show that it is possible to form successful calibration equations based on the proposed algorithm, which includes the two new graphical tools. There is a high level of correlation between the results of the different transformations considered, suggesting that in terms of parsimony, developing a calibration using the raw spectra could provide the most judicious outcome.

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

Discrimination of transgenic tomatoes based on visible/near-infrared spectra

2007 ◽  
Vol 584 (2) ◽  
pp. 379-384 ◽  
Author(s):  
Lijuan Xie ◽  
Yibin Ying ◽  
Tiejin Ying ◽  
Haiyan Yu ◽  
Xiaping Fu
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra ◽  
Transgenic Tomatoes

Mid and near Infrared Study of Carbohydrates by Canonical Correlation Analysis

1993 ◽  
Vol 1 (2) ◽  
pp. 99-108 ◽  
Author(s):  
P. Robert ◽  
M.F. Devaux ◽  
A. Qannari ◽  
M. Safar
Keyword(s):  
Correlation Analysis ◽  
Infrared Spectra ◽  
Canonical Correlation ◽  
Near Infrared ◽  
Sugar Content ◽  
Infrared Region ◽  
Total Sugar ◽  
Mid Infrared ◽  
Total Sugar Content ◽  
Near Infrared Spectra

Multivariate data treatments were applied to mid and near infrared spectra of glucose, fructose and sucrose solutions in order to specify near infrared frequencies that characterise each carbohydrate. As a first step, the mid and near infrared regions were separately studied by performing Principal Component Analyses. While glucose, fructose and sucrose could be clearly identified on the similarity maps derived from the mid infrared spectra, only the total sugar content of the solutions was observed when using the near infrared region. Characteristic wavelengths of the total sugar content were found at 2118, 2270 and 2324 nm. In a second step, the mid and near infrared regions were jointly studied by a Canonical Correlation Analysis. As the assignments of frequencies are generally well known in the mid infrared region, it should be useful to study the relationships between the two infrared regions. Thus, the canonical patterns obtained from the near infrared spectra revealed wavelengths that characterised each carbohydrate. The OH and CH combination bands were observed at: 2088 and 2332 nm for glucose, 2134 and 2252 nm for fructose, 2058 and 2278 nm for sucrose. Although a precise assignment of the near infrared bands to chemical groups within the molecules was not possible, the present work showed that near infrared spectra of carbohydrates presented specific features.

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