Application of machine learning to estimate fireball characteristics and their uncertainty from infrared spectral data

2021 ◽  
Author(s):  
Joseph G. Gorka ◽  
Derek Armstrong
Keyword(s):  
Machine Learning ◽  
Spectral Data ◽  
Infrared Spectral

scholarly journals Biomechanical, biochemical, and near infrared spectral data of bovine knee ligaments and patellar tendon

Data in Brief ◽  
2021 ◽  
Vol 36 ◽  
pp. 106976
Author(s):  
Aapo Ristaniemi ◽  
Jari Torniainen ◽  
Tommi Paakkonen ◽  
Lauri Stenroth ◽  
Mikko A.J. Finnilä ◽  
...  
Keyword(s):  
Spectral Data ◽  
Patellar Tendon ◽  
Near Infrared ◽  
Knee Ligaments ◽  
Infrared Spectral

Basic Chromium(III) Formate: Reaction of Chromiiim(III) Chloride with Formic Acid

1979 ◽  
Vol 34 (2) ◽  
pp. 160-162 ◽  
Author(s):  
R. C. Paul ◽  
P. Kapoor ◽  
. B. Baidya ◽  
R. Kapoor
Keyword(s):  
Formic Acid ◽  
Spectral Data ◽  
Magnetic Moment ◽  
Title Compound ◽  
Temperature Range ◽  
Infrared Spectral ◽  
Anhydrous Formic Acid

Abstract Chromium(III) Chloride, Basic Chromium(III) Formate, IR, Thermogravimetry, Magnetic Moment Chromium(III) chloride reacts with anhydrous formic acid to give basic chromium(III) formate [Cr3O(OOCH)6(H2O)2(HCOOH)](OOCH), HCOOH. Its reactions with bases (B) give compounds of the general composition [Cr30(C00CH)6(B)3](00CH). The title compound has been characterized by infrared spectral data, temperature range (266-110K) magnetic moment and thermogravimetry.

1,3,4-Thiadiazole-2,5-dithiol as a complexing agent. Complexes of RuIII, RuII, RhIII, PdIV IrIII and PtIV

1975 ◽  
Vol 28 (4) ◽  
pp. 763 ◽  
Author(s):  
MR Gajendragad ◽  
U Agarwala
Keyword(s):  
Magnetic Susceptibility ◽  
Metal Ions ◽  
Spectral Data ◽  
Crystal Field ◽  
Complexing Agent ◽  
Chemical Analyses ◽  
Infrared Spectral ◽  
Crystal Field Parameters

Complexes of RuIII, RuII, RhIII, PdIV, IrIII and PtIV with 1,3,4-thiadiazole- 2,5-dithiol have been prepared. Probable structures for the complexes have been proposed on the basis of chemical analyses, magnetic susceptibility and electronic and infrared spectral data. Crystal field parameters have been calculated which are in accordance with the structures proposed. In every complex the ligand behaves as a unidentate towards two metal ions.

Classifying Copper-Molybdenum-Gold Porphyry Deposit Alteration using Magnetic and Spectral data

2021 ◽  
Author(s):  
Oliver Dixon ◽  
William McCarthy ◽  
Nasser Madani ◽  
Michael Petronis ◽  
Steve McRobbie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Susceptibility ◽  
Spectral Data ◽  
Learning Algorithm ◽  
Copper Production ◽  
Magnetic Data ◽  
Machine Learning Algorithm ◽  
Porphyry Deposit ◽  
Remanent Magnetisation ◽  
Magnetic Remanence

<p>Copper is one of the most important critical metal resources needed to achieve carbon neutrality with a projected increase in demand of >300% over the next half century from electronics and renewables.  Porphyry deposits account for most of the global copper production, but the discovery of new reserves is ever more challenging. Machine learning presents an opportunity to cross reference new and traditionally under-utilised data sets with a view to developing quantitative predictive models of hydrothermal alteration zones to guide new, ambitious exploration programs.</p><p>The aim of this study is to demonstrate a new alteration classification scheme driven by quantitative magnetic and spectral data to feed a machine learning algorithm. The benefits of an alteration model based on quantitative data rather than subjective observations by geologists, are that there is no bias in the data collected, the arising model is quantifiable and therefore easy to model and the process be fully automated. Ultimately, this approach aids more detailed exploration and mine modelling, in turn, reducing the extraction process carbon footprint and more effectively identifying new deposits.</p><p>Presented here are magnetic susceptibility and shortwave infrared (SWIR) data collected from the KazMinerals plc. owned Aktogay Cu-Mo giant porphyry deposit, eastern Kazakhstan, which has a throughput of 30Mtpa of ore. These data are cross referenced using a newly developed machine learning algorithm. Generated autonomously, our results reveal twelve statistically and geologically significant clusters that define a new alteration classification for porphyry style mineralisation. Results are entirely non-subjective, reproducible, quantitative and modellable.</p><p>Importantly, magnetic susceptibility measurements improve the algorithm’s ability to identify clusters by between 29-36%; enhancing the sophistication of the included magnetic data promises to yield substantially better statistical results. Magnetic remanence data are therefore being complied on representative samples from each of the twelve identified clusters, including hysteresis, isothermal remanent magnetisation (IRM) acquisition, FORC measurements, natural remanent magnetisation (NRM) and anhysteretic remanent magnetisation (ARM). Through collaboration with industry partners, we aim to develop an automated means of collecting these magnetic remanence data to accompany the machine learning algorithm.</p>

Wavelet Analysis of Near Infrared Spectral Data in the Application of Denoising

2011 ◽  
Vol 48-49 ◽  
pp. 1358-1362
Author(s):  
Xiao Mei Lin ◽  
Juan Wang ◽  
Qing Hua Yao
Keyword(s):  
Wavelet Analysis ◽  
Infrared Spectrum ◽  
Spectral Data ◽  
Signal Analysis ◽  
Near Infrared ◽  
Fourier Method ◽  
Signal Denoising ◽  
Data Pretreatment ◽  
Infrared Spectral ◽  
Near Infrared Spectrum

Spectrum signal may contain many peaks or mutations and noise also is not smooth white noise, to this kind of signal analysis, must do signal pretreatment, remove part of signal and extract useful part of signal.Based on the data of blood glucose near-infrared spectrum as the research object to explore the application of wavelet transform in the near infrared spectrum signal denoising, and through the simulation results show that using wavelet analysis of near infrared spectral data pretreatment than the traditional Fourier method can be higher precision of prediction.

scholarly journals Bottle Fermented Sparkling Wine: Cork or Crown Closures During the Second Fermentation?

2021 ◽  
Vol 42 (2) ◽  
Author(s):  
N. Jolly ◽  
P. Minnaar ◽  
M. Booyse ◽  
P. Gerber
Keyword(s):  
Infrared Spectroscopy ◽  
Spectral Data ◽  
South African ◽  
Phenolic Acids ◽  
Sparkling Wine ◽  
Anecdotal Evidence ◽  
Legal Requirements ◽  
Infrared Spectral ◽  
Marker Compounds ◽  
Polyphenol Profile

Bottle-fermented sparkling wine producers are continuously striving to increase quality and produceniche products. One production tool that could be used is a cork closure instead of a crown cap closureduring the second fermentation and maturation on yeast lees. Anecdotal evidence suggests that thisleads to stylistic differences in the wine. Six pairs of South African bottle-fermented sparkling wines(Méthode Cap Classique), closed by either a cork or crown cap, were investigated. Analyses includedbottle pressure, infrared spectroscopy, phenolic acids, sensory attributes and CO2 kinetics. Generally,there were differences between the cork-closed and crown-capped wines. Cork-closed wines tended tohave lower pressure compared to crown-capped wines, albeit still well within legal requirements. Otherdifferences were evident in the infrared spectral data and in the polyphenol profile of the analysed wines.Levels of gallic, caftaric, caffeic and p-coumaric acids could be used collectively as marker compounds todifferentiate between cork-closed and crown-capped wines. The effect of the cork was also evident in thesensory attributes and CO2 kinetics. Cork-closed wines were judged to have smaller bubbles and a longeraftertaste. It was also shown that the cork-closed wines tended to lose CO2 from the glass slower after beingpoured than their crown-capped counterparts. The data tentatively support the anecdotal evidence thatcork can be used during the second fermentation and maturation on the yeast lees to change the style ofbottle-fermented sparkling wine.

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