scholarly journals Near-Infrared Spectral Classification of Late M and L Dwarfs

2001 ◽  
Vol 121 (3) ◽  
pp. 1710-1721 ◽  
Author(s):  
I. Neill Reid ◽  
A. J. Burgasser ◽  
K. L. Cruz ◽  
J. Davy Kirkpatrick ◽  
J. E. Gizis
Keyword(s):  
Near Infrared ◽  
Spectral Classification ◽  
Infrared Spectral ◽  
L Dwarfs

scholarly journals The Classification of L Dwarfs: Is It Based on Clouds or Temperature?

2003 ◽  
Vol 211 ◽  
pp. 355-358 ◽  
Author(s):  
Denise C. Stephens
Keyword(s):  
Effective Temperature ◽  
Classification System ◽  
Near Infrared ◽  
Spectral Indices ◽  
Combined Use ◽  
Infrared Spectral ◽  
L Dwarfs

A continuous L dwarf classification sequence requires the combined use of far optical (visible) and near infrared spectral indices. However, the visible and near infrared indices currently in use assign subtypes that differ by up to three subclasses due to differences in cloud opacity for objects with the same effective temperature. Therefore, it may be impossible to combine visible and near infrared spectral indices to create one L dwarf classification system, and two classification variables may be necessary.

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

Classification of rhizosphere components using visible–near infrared spectral images

2007 ◽  
Vol 310 (1-2) ◽  
pp. 245-261 ◽  
Author(s):  
Tatsuro Nakaji ◽  
Kyotaro Noguchi ◽  
Hiroyuki Oguma
Keyword(s):  
Near Infrared ◽  
Infrared Spectral

scholarly journals The Carbon-rich Dust Sequence: Infrared Spectral Classification of Carbon Stars

1998 ◽  
Vol 115 (2) ◽  
pp. 809-820 ◽  
Author(s):  
G. C. Sloan ◽  
I. R. Little-Marenin ◽  
S. D. Price
Keyword(s):  
Spectral Classification ◽  
Carbon Stars ◽  
Infrared Spectral

scholarly journals New Standards for the Spectral Classification of M Dwarfs

1973 ◽  
Vol 50 ◽  
pp. 209-219
Author(s):  
R. F. Wing
Keyword(s):  
Spectral Type ◽  
Near Infrared ◽  
Color Temperature ◽  
Spectral Classification ◽  
M Dwarfs ◽  
Dwarf Stars ◽  
Photoelectric Measurements

Spectral types based upon photoelectric measurements of the strong TiO band near 7100 Å and having an internal accuracy of one-tenth of a subclass are presented for 26 dwarf stars in the range K4 to M6. For the calibration of the TiO index into spectral type, the MK scale for giants established by Wing and Keenan was adopted. Accordingly the types differ systematically from those previously published on any of the systems used for dwarfs, being closest to Joy's. The advantages of the MK giant scale are discussed, and it is suggested that the stars classified here be adopted as standards for a revised system of MK classification for dwarfs.The relation between TiO band strength and near-infrared color temperature for dwarfs differs significantly from the giant relation. The coolest dwarfs observed were Wolf 359 and Proxima Centauri; although Wolf 359 is both cooler and less luminous than Proxima, they have nearly identical TiO band strengths.

scholarly journals Mid-infrared spectral classification of endometrial cancer compared to benign controls in serum or plasma samples.

The Analyst ◽  
2021 ◽  
Author(s):  
David Paul Mabwa ◽  
Ketankumar Gajjar ◽  
David Furniss ◽  
Roberta Schiemer ◽  
Richard Crane ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Plasma Samples ◽  
Spectral Classification ◽  
Mid Infrared ◽  
Mir Spectroscopy ◽  
Liquid Samples ◽  
Detailed Evaluation ◽  
Infrared Spectral

This study demonstrates a discrimination of endometrial cancer versus (non-cancerous) benign controls based on mid-infrared (MIR) spectroscopy of dried plasma or serum liquid samples. A detailed evaluation was performed of...

scholarly journals Multivariate and machine learning approaches for honey botanical origin authentication using near infrared spectroscopy

2019 ◽  
Vol 27 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Vittoria Bisutti ◽  
Roberta Merlanti ◽  
Lorenzo Serva ◽  
Lorena Lucatello ◽  
Massimo Mirisola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Canonical Discriminant Analysis ◽  
Learning Approaches ◽  
Botanical Origin ◽  
Infrared Spectral

In this work the feasibility of near infrared spectroscopy was evaluated combined with chemometric approaches, as a tool for the botanical origin prediction of 119 honey samples. Four varieties related to polyfloral, acacia, chestnut, and linden were first characterized by their physical–chemical parameters and then analyzed in triplicate using a near infrared spectrophotometer equipped with an optical path gold reflector. Three different classifiers were built on distinct multivariate and machine learning approaches for honey botanical classification. A partial least squares discriminant analysis was used as a first approach to build a predictive model for honey classification. Spectra pretreatments named autoscale, standard normal variate, detrending, first derivative, and smoothing were applied for the reduction of scattering related to the presence of particle size, like glucose crystals. The values of the descriptive statistics of the partial least squares discriminant analysis model allowed a sufficient floral group prediction for the acacia and polyfloral honeys but not in the cases of chestnut and linden. The second classifier, based on a support vector machine, allowed a better classification of acacia and polyfloral and also achieved the classification of chestnut. The linden samples instead remained unclassified. A further investigation, aimed to improve the botanical discrimination, exploited a feature selection algorithm named Boruta, which assigned a pool of 39 informative averaged near infrared spectral variables on which a canonical discriminant analysis was assessed. The canonical discriminant analysis accounted a better separation of samples according to the botanical origin than the partial least squares discriminant analysis. The approach used has permitted to achieve a complete authentication of the acacia honeys but not a precise segregation of polyfloral ones. The comparison between the variables important in projection and the Boruta pool showed that the informative wavelengths are partially shared especially in the middle and far band of the near infrared spectral range.

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