scholarly journals An Efficient Low-Resolution NIR Classification Scheme for M, L, and T dwarfs and Its Application to Young BDs

2003 ◽  
Vol 211 ◽  
pp. 359-360
Author(s):  
L. Testi ◽  
A. Natta ◽  
C. Baffa ◽  
G. Comoretto ◽  
S. Gennari ◽  
...  
Keyword(s):  
Near Infrared ◽  
Classification Scheme ◽  
Optical Element ◽  
System Throughput ◽  
Single Shot ◽  
Low Resolution ◽  
Near Infrared Spectra ◽  
Infrared Spectral ◽  
High System ◽  
Large Telescopes

We present the preliminary results of a programme aimed at defining a low-resolution near-infrared spectral classification scheme for faint M, L, and T-dwarfs. The method is based on the global shape of R˜100 complete near-infrared spectra from 0.8 to 2.4μm as obtained through a high-throughput prism-based optical element, the Amici device, mounted inside the NICS instrument at the TNG 3.5m telescope (Baffa et al. 2001; Oliva 2000). The aim of our project is to provide an efficient classification scheme based on very low-resolution near infrared spectroscopy, which can be carried on at a 4m-class telescope. The results for the L-type dwarfs have already been presented in Testi et al. (2001), sample spectra for the M and T-dwarfs range are shown in Figure 1. A preliminary application of the method to the classification of young embedded brown-dwarf candidates has been successfully attempted by Testi et al. (2002) and Natta et al. (2002). The method is shown to be accurate and competitive: the high system throughput coupled with the possibility of obtaining in a “single shot” the complete spectrum of the objects make the NICS/TNG system more efficient than existing large telescopes.

NICS-TNG Low-Resolution 0.85–2.45 micron Spectra of L Dwarfs: A Near-Infrared Spectral Classification Scheme for Faint Dwarfs

2001 ◽  
Vol 552 (2) ◽  
pp. L147-L150 ◽  
Author(s):  
L. Testi ◽  
F. D’Antona ◽  
F. Ghinassi ◽  
J. Licandro ◽  
A. Magazzù ◽  
...  
Keyword(s):  
Near Infrared ◽  
Classification Scheme ◽  
Spectral Classification ◽  
Low Resolution ◽  
Infrared Spectral ◽  
L Dwarfs

scholarly journals Evidence of an evolved nature of MWC 349A

2020 ◽  
Vol 493 (3) ◽  
pp. 4308-4314 ◽  
Author(s):  
M Kraus ◽  
M L Arias ◽  
L S Cidale ◽  
A F Torres
Keyword(s):  
Main Sequence ◽  
Near Infrared ◽  
Gas Temperature ◽  
Band Emission ◽  
Galactic Emission ◽  
Near Infrared Spectra ◽  
Infrared Spectral ◽  
Significant Enrichment ◽  
Excellent Tool ◽  
Radio Stars

ABSTRACT The Galactic emission-line object MWC 349A is one of the brightest radio stars in the sky. The central object is embedded in an almost edge-on oriented Keplerian rotating thick disc that seems to drive a rotating bipolar wind. The dense disc is also the site of hot molecular emission such as the CO bands with its prominent band heads in the near-infrared spectral range. Despite numerous studies, the nature of MWC 349A is still controversial with classifications ranging from a pre-main sequence object to an evolved supergiant. We collected new high-resolution near-infrared spectra in the K and Lbands using the GNIRS spectrograph at Gemini-North to study the molecular disc of MWC 349A, and in particular to search for other molecular species such as SiO and the isotope 13CO. The amount of 13CO, obtained from the 12CO/13CO ratio, is recognized as an excellent tool to discriminate between pre-main-sequence and evolved massive stars. We find no signatures of SiO band emission, but detect CO band emission with considerably lower intensity and CO gas temperature compared to previous observations. Moreover, from detailed modelling of the emission spectrum, we derive an isotope ratio of 12CO/13CO = 4 ± 1. Based on this significant enrichment of the circumstellar environment in 13CO, we conclude that MWC 349A belongs to the group of B[e] supergiants, and we discuss possible reasons for the drop in CO intensity.

N-way partial least squares combined with new self-construction strategy—A promising approach of using near infrared spectral data for quantitative determination of multiple compounds

2019 ◽  
Vol 28 (2) ◽  
pp. 113-121
Author(s):  
Xiang-Zhi Zhang ◽  
Ai-Jun Ma ◽  
Na Feng ◽  
Bao Qiong Li
Keyword(s):  
Quantitative Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Least Squares Method ◽  
Three Dimensional ◽  
Near Infrared Spectra ◽  
Infrared Spectral ◽  
Construction Strategy ◽  
Partial Least Squares Method

Because of the complexity of near infrared spectral data, effective strategies are necessary proposed for accurate quantitative analysis purpose. This work explores a new self-construction strategy for the arrangement of conventional near infrared two-dimensional spectra into new self-constructed three-dimensional spectra, and investigate the feasibility of N-way partial least squares combined with the new self-constructed three-dimensional near infrared spectra for obtaining accurate quantitative determination results. A proof-of-concept model system, the quantitative analysis of four components (moisture, oil, protein, and starch) in corn samples, was applied to evaluate the performance of the proposed strategy. The ability of the newly proposed approach to predict the target compounds was checked with test samples. The established models have good predictive power for the target compounds with acceptable values of Rp (range from 0.82 to 0.997) and RMSEP (range from 0.03 to 0.47). Compared with partial least squares method on pretreated near infrared spectra and N-way partial least squares method on the basis of near infrared self-constructed three-dimensional spectra, the proposed method is competitive.

Study of Analyte Diffusion into a Silicone-Clad Fiber-Optic Chemical Sensor by Evanescent Wave Spectroscopy

1995 ◽  
Vol 49 (11) ◽  
pp. 1636-1645 ◽  
Author(s):  
Dianna S. Blair ◽  
Lloyd W. Burgess ◽  
Anatol M. Brodsky
Keyword(s):  
Diffusion Coefficients ◽  
Evanescent Wave ◽  
Near Infrared ◽  
Chemical Sensor ◽  
Optical Element ◽  
Constant Diffusion ◽  
Infrared Spectral ◽  
Diffusion Rates ◽  
Two Parameter ◽  
Infrared Data

The diffusion rates of various polar and nonpolar analytes in dimethylsiloxane were examined with the use of a commercially available 200-μm silica-core/300-μm silicone-clad fiber as the optical element for evanescent wave spectroscopy in the near-infrared spectral region. An analytical solution to Fick's second law was used to model the time-dependent analyte concentration at the core/cladding interface. Successful fit of the analytical solutions to infrared data verifies the assumption of constant diffusion coefficients that is necessary to solve the equation. Transport rates of polar analytes in silicone can be estimated with the use of a single-parameter model that results in diffusion coefficients of 3.2 × 10−1, 1.6 × 10−1, 8.1 × 10−7, and 3.9 × 10−7 cm2/s for methanol, ethanol, 2-propanol, and n-butanol, respectively. Estimating the transport of larger nonpolar analytes in the silicone cladding requires a two-parameter model that includes a diffusion coefficient and an interfacial conductance term. For pentane, hexane, heptane, and cyclohexane the resultant diffusion coefficients and interfacial conductance parameters are 6.9 × 10−7, 4.6 × 10−7, 4.4 × 10−7, and 2.3 × 10−7 cm2/s and 2500, 2000, 2000, and 600 μm−1, respectively.

Near- versus Mid-Infrared Spectroscopy: Relationships between Spectral Changes Induced by Water and Relative Information Content of the Two Spectral Regions in Regard to High-Moisture Samples

1995 ◽  
Vol 49 (3) ◽  
pp. 295-303 ◽  
Author(s):  
James B. Reeves
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
High Moisture ◽  
Mid Infrared ◽  
Spectral Changes ◽  
Near Infrared Spectra ◽  
Infrared Spectral ◽  
Water Ph ◽  
Relative Information Content ◽  
Relationship Of

The objectives of this work were to examine similarities and differences in the near-infrared and mid-infrared spectral regions when one is working with high-moisture materials and to study spectral changes in these regions as a method to identify the relationship of spectral information in the near-IR to fundamental absorptions in the mid-IR. Near- and mid-infrared spectra were taken with a Digilab FTS-65 Fourier transform spectrometer. Liquids were examined by transmission and solids by reflectance. Results with solutions showed that less spectral distortion arises when one is subtracting water from mid- rather than from near-infrared spectra. It was also easier to produce high-quality spectra in the mid-infrared by using attenuated total reflectance than by using transmission in the near-infrared. While mid-infrared spectra showed changes (induced by water, pH, physical state, and ionic strength) similar to those found in the near-infrared, there appeared to be more information available in the mid-infrared, even in the presence of water.

scholarly journals Rapid identification of Lactobacillus species using near infrared spectral features of bacterial colonies

2019 ◽  
Vol 27 (4) ◽  
pp. 302-313
Author(s):  
Jiyong Shi ◽  
Xuetao Hu ◽  
Xiaobo Zou ◽  
Zhiming Guo ◽  
Mel Holmes ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Near Infrared ◽  
Rapid Identification ◽  
Lactobacillus Species ◽  
Support Vector ◽  
Spectral Features ◽  
Mid Infrared ◽  
Near Infrared Spectra ◽  
Infrared Spectral ◽  
Chemical Groups

The feasibility of rapid identification of Lactobacillus species using near-infrared spectral features coupled with chemometrics was investigated. First, bacterial colonies of 11 Lactobacillus strains covering four species ( Lactobacillus casei, Lactobacillus reuteri, Lactobacillus brevis, and Lactobacillus fermentum) were cultured using the spread-plate technique. Near-infrared spectra data of the Lactobacillus species were collected directly from the bacterial colonies. Second, 10 wavenumbers were selected from the near-infrared spectra data using uninformative variables elimination and genetic algorithm, and calibration models based on the 10 selected wavenumbers were built using least squares support vector machine. The identification rates for the prediction set and validation set were 89.04 and 85%, respectively. Third, chemical groups of the Lactobacillus cells contributing to the identification of the Lactobacillus strains were identified using mid infrared. The results of mid infrared data analysis indicated that 9 chemical groups could be considered characteristics for categorizing the 11 Lactobacillus strains. The relationship between the 10 selected wavenumbers and identified chemical groups was identified, which supported the satisfactory performance of the least squares support vector machine calibration model. This study demonstrated that near-infrared spectral features of bacterial colonies could be used for Lactobacillus typing at the strain level.

scholarly journals The NIRSPEC Brown Dwarf Spectroscopic Survey. I. Low‐Resolution Near‐Infrared Spectra

2003 ◽  
Vol 596 (1) ◽  
pp. 561-586 ◽  
Author(s):  
Ian S. McLean ◽  
Mark R. McGovern ◽  
Adam J. Burgasser ◽  
J. Davy Kirkpatrick ◽  
L. Prato ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Brown Dwarf ◽  
Low Resolution ◽  
Near Infrared Spectra

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.

Uniform Retrieval Analysis of Brown Dwarfs

2020 ◽  
Author(s):  
Kezman Saboi ◽  
Michael R. Line ◽  
Joseph Zalesky ◽  
Adam Schneider ◽  
Zhoujian Zhang ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Brown Dwarfs ◽  
Equilibrium Temperature ◽  
Temperature Profiles ◽  
Low Resolution ◽  
Thermal Profile ◽  
Elemental Abundances ◽  
Retrieval Analysis ◽  
Near Infrared Spectra

<p>Spectra of brown dwarfs are key to exploring the chemistry and physics that take place in their atmospheres. Late T dwarf (850 - 500 K) spectra are particularly diagnostic due to their relatively cloud free atmospheres and deep molecular bands. With the use of powerful atmospheric retrieval tools, these properties permit proper allocation of constraints on the molecular/atomic abundances and temperature profiles. These constraints can be used to derive the elemental abundances (metallicity, C/O), chemical disequilibrium, and non-radiative-convective equilibrium temperature perturbations. Building upon previous analyses on T and Y dwarfs (Line et al. 2017; Zalesky et al. 2019), we present a uniform retrieval analysis of 52 T dwarfs via their low-resolution near-infrared spectra. This analysis more than doubles the sample of T dwarfs with retrieved properties. We present updates on current compositional trends and thermal profile constraints amongst the T dwarf population.</p>

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