scholarly journals Spectral Classification and Effective Temperatures of L and T Dwarfs Based on Near‐Infrared Spectra

2004 ◽  
Vol 607 (1) ◽  
pp. 499-510 ◽  
Author(s):  
Tadashi Nakajima ◽  
Takashi Tsuji ◽  
Kenshi Yanagisawa
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Spectral Classification ◽  
Near Infrared Spectra ◽  
Effective Temperatures

scholarly journals Confirming new white dwarf-ultracool dwarf binary candidates

2020 ◽  
Vol 498 (1) ◽  
pp. 12-24
Author(s):  
M A Hogg ◽  
S L Casewell ◽  
G A Wynn ◽  
E S Longstaff ◽  
I P Braker ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
White Dwarf ◽  
White Dwarfs ◽  
Near Infrared ◽  
Infrared Excess ◽  
Photometric Variability ◽  
Near Infrared Spectra ◽  
Effective Temperatures ◽  
M Dwarf

ABSTRACT We present the results of a study to discover prospective new white dwarf-L dwarf binaries as identified by their near-infrared excesses in the UKIDSS catalogue. We obtained optical spectra to validate the white dwarf nature for 22 of the candidate primary stars, confirming ten as white dwarfs and determining their effective temperatures and gravities. For all 10 white dwarfs, we determined that the near-infrared excess was indeed indicative of a cool companion. Six of these are suggestive of late M dwarf companions, and three are candidate L dwarf companions, with one straddling the M−L boundary. We also present near-infrared spectra of eight additional candidate white dwarf-ultracool dwarf binaries, where the white dwarf primary had been previously confirmed. These spectra indicate one candidate at the M−L boundary, three potential L dwarf companions, and one suspected M dwarf, which showed photometric variability on a ∼6 h period, suggesting the system may be close. Radial velocity follow-up is required to confirm whether these systems are close, or widely separated.

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.

Analysis of the near-infrared spectra of C60−

1995 ◽  
Vol 247 (1-2) ◽  
pp. 57-62 ◽  
Author(s):  
Robert D. Bolskar ◽  
Sean H. Gallagher ◽  
Robert S. Armstrong ◽  
Peter A. Lay ◽  
Christopher A. Reed
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra
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