Sawmill Trial of At-Line Prediction of Recovered Lumber Stiffness by NIR Spectroscopy of Pinus Radiata Cants

2003 ◽  
Vol 11 (2) ◽  
pp. 137-143 ◽  
Author(s):  
Roger Meder ◽  
Armin Thumm ◽  
David Marston
Keyword(s):  
Infrared Spectra ◽  
Pinus Radiata ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Bending Test ◽  
Long Span ◽  
Four Point Bending ◽  
Centre Line ◽  
Near Infrared Spectra ◽  
Four Point Bending Test

Pinus radiata D. Don cants (100 or 200 mm thick × 4.8 m) from a commercial sawmill operation were assessed in the green state using near infrared (NIR) spectroscopy. Near infrared spectra were acquired along the centre line of one cant face and at 50 mm offsets to one side of the centre line. The cants were ripped to produce either 50 × 100 or 50 × 200 mm rough sawn boards, which were then kiln-dried and gauged to final dimensions. The long-span modulus of elasticity ( L MoE) on each board was determined using a four-point bending test and the corresponding NIR spectra of each board (the 50 mm edge from the cant) were regressed against the long-span MoE value using partial least squares modeling. The results are explained in terms of the potential for NIR to predict the potential upgrade to higher value products for timber recovered from the corewood zone of logs.

scholarly journals Nebular spectroscopy of SN 2014J: Detection of stable nickel in near-infrared spectra

2018 ◽  
Vol 619 ◽  
pp. A102 ◽  
Author(s):  
S. Dhawan ◽  
A. Flörs ◽  
B. Leibundgut ◽  
K. Maguire ◽  
W. Kerzendorf ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Rest Frame ◽  
Near Infrared ◽  
Energy Levels ◽  
Nir Spectroscopy ◽  
Host Galaxy ◽  
Iron Group ◽  
Maximum Light ◽  
Near Infrared Spectra

We present near-infrared (NIR) spectroscopy of the nearby supernova 2014J obtained ∼450 d after explosion. We detect the [Ni II] 1.939 μm line in the spectra indicating the presence of stable 58Ni in the ejecta. The stable nickel is not centrally concentrated but rather distributed as the iron. The spectra are dominated by forbidden [Fe II] and [Co II] lines. We used lines, in the NIR spectra, arising from the same upper energy levels to place constraints on the extinction from host galaxy dust. We find that that our data are in agreement with the high AV and low RV found in earlier studies from data near maximum light. Using a 56Ni mass prior from near maximum light γ-ray observations, we find 0.053 ± 0.018 M⊙ of stable nickel to be present in the ejecta. We find that the iron group features are redshifted from the host galaxy rest frame by ∼600 km s−1.

Linearity of Near-Infrared Spectra of Alkanes

2000 ◽  
Vol 54 (4) ◽  
pp. 624-629 ◽  
Author(s):  
Oliver C. Mullins ◽  
Nikhil B. Joshi ◽  
Henning Groenzin ◽  
Tim Daigle ◽  
Chris Crowell ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Variable Temperature ◽  
Chemical Properties ◽  
Nir Spectroscopy ◽  
End Users ◽  
Process Flow ◽  
Spectral Signatures ◽  
Near Infrared Spectra ◽  
Flow Stream

Near-infrared (NIR) spectroscopy is used to monitor a large variety of process flow streams. Hydrocarbons with their strong and resolvable NIR spectral signatures are good candidate analytes. NIR has been exploited to monitor many chemical properties for optimal hydrocarbon utilization particularly for well-characterized flow streams of small variability for end users. The utility of NIR in the context of the production of hydrocarbon resources necessitates application over a much broader range of flow stream conditions. Here we examine the spectral impact of variable temperature, pressure, and composition to determine the robustness of NIR methods in upstream applications.

Non-Supervised Neural Categorisation of near Infrared Spectra. Application to Pure Compounds

1995 ◽  
Vol 3 (2) ◽  
pp. 97-110 ◽  
Author(s):  
Paloma Cáceres-Alonso ◽  
Alvaro García-Tejedor
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Complex Data ◽  
Neural Learning ◽  
Near Infrared Spectra ◽  
Pure Compounds ◽  
Open Issue ◽  
Preliminary Study ◽  
Recognition Technique

Near infrared (NIR) spectroscopy is recognised world-wide as a powerful tool for substance quantification and identification provided that good data analysis tools are used. Most of the identification algorithms use supervised learning and require previous knowledge of existing categories to construct the mathematical models that will be later used at runtime. The use of non-supervised neural learning algorithms is not a common tool in identification of near infrared spectra, although they are widely employed as a pattern recognition technique. Problems analogous to NIR identification have already been solved by means of non-supervised neural networks. Their main advantages are the ability to learn from examples as well as the processing speed, once they are trained. We present in this work a preliminary study of a non-supervised classifier built using Kohonen self-organising maps (SOM). The result is a model useful for identification of a group of NIR spectra belonging to 15 pure products. The accuracy of the classification is discussed. The generalisation of the method for more complex data is still an open issue.

Near Infrared Spectroscopic Study on the Adsorption of Methanol on Silica Gel

2013 ◽  
Vol 650 ◽  
pp. 150-155
Author(s):  
Alfred A. Christy
Keyword(s):  
Silica Gel ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Polar Molecules ◽  
Infrared Spectroscopic Study ◽  
Oh Groups ◽  
Glass Vial ◽  
Silanol Groups ◽  
Near Infrared Spectra

The silanol groups on Silica gel surface are sites for adsorption of polar molecules. Alcohols and other polar molecules are easily adsorbed by forming hydrogen bondings with OH groups on silica gel surface. A study on the adsorption of methanol on silica gel was carried out by using NIR spectroscopy in combination with ssecond derivative techniques. Four of the well characterised silica gel samples were used in this study. Each of the silica gel (0.25g) samples with different surface areas and silanol number was pressed into a small disc, placed in a glass vial and the physically adsorbed water molecules from the surface of the silica gel particles were removed by heating the sample to 200 °C under vacuum. The near infrared spectra of the cooled sample was recorded by a Perkin Elmer spectrum one NIR spectrometer equipped with a transflectance accessory and a deuterated triglycine detector at a resolution of 16 cm-1. The glass vial was then opened and a tiny tube filled with methanol was inserted in the glass vial. Then the near infrared spectra of the sample during the adsorption of methanol were recorded at regular time intervals until there is no apparent change in the spectra. The second derivative profiles of the spectra were obtained using the instruments’ software. The mass of the silica gel pellet was determined by an analytical balance and the methanol adsorbed on the surface was calculated. The number of methanol layers on the silica gel surface was calculated using the silica gel particle characteristics of the samples. The results show that the adsorption evolution of methanol progresses on the samples and the surface was covered by a mono layer within the first 60 minutes. Furthermore, it appears that the adsorption of multilayer on methanol starts after all the surface silanol groups are exhausted.

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.

Sign in / Sign up

Export Citation Format

Share Document