scholarly journals A numerical method for mass spectral data analysis

2005 ◽  
Vol 18 (12) ◽  
pp. 1412-1417 ◽  
Author(s):  
Anthony J. Kearsley ◽  
William E. Wallace ◽  
Javier Bernal ◽  
Charles M. Guttman
Keyword(s):  
Data Analysis ◽  
Numerical Method ◽  
Spectral Data ◽  
Mass Spectral Data ◽  
Mass Spectral ◽  
Spectral Data Analysis

Attribution of fentanyl analogue synthesis routes by multivariate data analysis of orthogonal mass spectral data

Talanta ◽  
2019 ◽  
Vol 203 ◽  
pp. 122-130 ◽  
Author(s):  
Lina Mörén ◽  
Johanna Qvarnström ◽  
Magnus Engqvist ◽  
Robin Afshin-Sander ◽  
Xiongyu Wu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Mass Spectral Data ◽  
Mass Spectral ◽  
Synthesis Routes

Synthesis and Biological Activity of 3-(substitutedphenyl)-6-(4-methoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine: Part II

2020 ◽  
Vol 18 ◽  
Author(s):  
Niranjan Kaushik ◽  
Nitin Kumar ◽  
Anoop Kumar ◽  
Vikas Sharma
Keyword(s):  
Antifungal Activity ◽  
Opportunistic Infections ◽  
Fungal Infections ◽  
Mass Spectral Data ◽  
Triazole Derivatives ◽  
Mass Spectral ◽  
Fungal Strains ◽  
Antifungal Properties ◽  
Antifungal Potential ◽  
Spectral Data Analysis

Background: Fungal infections are opportunistic infections that become a serious problem to human health. Objective: Considering the antifungal potential of triazole nucleus, the study was carried out with the objective to synthesize some novel triazole derivatives with antifungal potential. Method: 1,2,4-triazole derivatives were synthesized via a two step reaction (reported earlier). The first step involves reaction of substituted benzoic acid with thiocarbohydrazide to form 4-amino-3-(substituted phenyl)-5-mercapto-1, 2, 4-triazole derivatives (1a-1k) while in second step, synthesized compounds (1a-1k) were then subsequently treated with substituted acetophenone to yield substituted (4-methoxyphenyl-7H-[1, 2, 4] triazolo [3, 4-b][1,3,4] thiadiazine derivatives (2a-2k). All synthesized compounds were characterized by IR, 1H NMR, and Mass spectral data analysis and were screened for their antifungal properties against different fungal strains i.e. Candida tropicalis (ATCC-13803, ATCC-20913), Candida albicans (ATCC-60193), Candida inconspicua (ATCC-16783) and Candida glabrata (ATCC-90030, ATCC-2001). Results: Compound 2d displayed better percentage inhibition (26.29%, 24.81%) than fluconazole (24.44%, 22.96%) against ATCC-16783, ATCC-2001 fungal strains respectively at 100µg/ml. Compound 2f also displayed better percentage inhibition (28.51%) against ATCC-90030 as compared to fluconazone (27.4%) at 200 µg/ml. Similarly, compounds 2e and 2j also exhibited better antifungal properties than fluconazole at 200µg/ml. Compound 2e was found most potent against ATCC13803 (30.37%) and ATCC-90030 (30.37%) fungal strains as compared to fluconazole (28.14%, 27.4%) at 200 µg/ml respectively whereas compound 2j exhibited better antifungal activity (28.51%) against ATCC-60193 than fluconazole (27.7%) at 200 µg/ml. Conclusion: The results were in accordance with our assertions for triazole derivatives, as all compounds displayed moderate to good antifungal activity.

scholarly journals Green Synthetic Protocol for (E)-1-Aryl-3-(2-morpholinoquinolin- 3-yl)prop-2-en-1-ones and Their Antimicrobial Activity

2019 ◽  
Vol 31 (9) ◽  
pp. 1895-1898
Author(s):  
Relangi Siva Subrahmanyam ◽  
Venkateswara Rao Anna
Keyword(s):  
Antimicrobial Activity ◽  
Spectral Data ◽  
1H Nmr ◽  
13C Nmr ◽  
Mass Spectral Data ◽  
In Vitro Antimicrobial Activity ◽  
Mass Spectral ◽  
Fungal Organisms

We report here an easy, efficient and green synthetic protocol for the (E)-1-aryl-3-(2-morpholinoquinolin-3-yl)prop-2-en-1-ones by the Claisen-Schmidt condensation of 2-morpholinoquinoline-3-carbaldehyde and different substituted acetophenones by using 1-butyl-3-methylimidazolium tetrafluoroborate (Bmim)BF4. The compounds were characterized by using 1H NMR, 13C NMR and mass spectral data and screened there in vitro antimicrobial activity against different bacterial and fungal organisms.

scholarly journals N-(2-(1H-Indol-3-yl)ethyl)-2-(6-methoxynaphthalen-2-yl)propanamide

Molbank ◽  
10.3390/m1187 ◽  
2021 ◽  
Vol 2021 (1) ◽  
pp. M1187
Author(s):  
Stanimir Manolov ◽  
Iliyan Ivanov ◽  
Dimitar Bojilov
Keyword(s):  
Spectral Data ◽  
13C Nmr ◽  
Title Compound ◽  
High Yield ◽  
Mass Spectral Data ◽  
Mass Spectral

The title compound was obtained in high yield in the reaction between tryptamine and naproxen. The newly synthesized naproxen derivative was fully analyzed and characterized via 1H, 13C-NMR, UV, IR, and mass spectral data.

Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine

2021 ◽  
Vol 87 (6) ◽  
pp. 1079-1086
Author(s):  
M. De Luca ◽  
G. Ioele ◽  
G. Ragno
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Drug Mixture ◽  
Potassium Canrenoate ◽  
Spectral Data Analysis ◽  
Complex Drug

scholarly journals (1R,4S,5S)-5-((3-Hydroxypropyl)amino)-4-((1-Methyl-1H-tetrazol-5-yl)thio)Cyclopent-2-en-1-ol

Molbank ◽  
10.3390/m1199 ◽  
2021 ◽  
Vol 2021 (2) ◽  
pp. M1199
Author(s):  
Milene A. G. Fortunato ◽  
Filipa Siopa ◽  
Carlos A. M. Afonso
Keyword(s):  
Spectral Data ◽  
13C Nmr ◽  
Ring Opening ◽  
Environmentally Friendly ◽  
Mass Spectral Data ◽  
Mass Spectral ◽  
Excellent Yield ◽  
Ring Opening Reaction ◽  
Nucleophilic Ring Opening

Using environmentally friendly conditions, the nucleophilic ring-opening reaction of 6-azabicyclo[3.1.0]hex-3-en-2-ol with 1-methyl-1H-tetrazole-5-thiol provided a novel thiol-incorporated aminocyclopentitol, (1R,4S,5S)-5-((3-hydroxypropyl)amino)-4-((1-methyl-1H-tetrazol-5-yl)thio)cyclopent-2-en-1-ol, in excellent yield (95%). The newly synthesized compound was analyzed and characterized via 1H, 13C-NMR, HSQC, and mass spectral data.

Chemical components of the rhizomes of Drynaria fortunei (KUNZE) J. Sm. (polypodiaceae) in Vietnam

2011 ◽  
Vol 76 (9) ◽  
pp. 1133-1139 ◽  
Author(s):  
Pham Thi Nhat Trinh ◽  
Nguyen Cong Hao ◽  
Phan Thanh Thao ◽  
Le Tien Dung
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Phenolic Acid ◽  
Ethanol Extract ◽  
Chemical Components ◽  
Spectroscopic Methods ◽  
Phenylpropanoid Glycoside ◽  
Spectral Data Analysis ◽  
First Time

From the ethanol extract of Drynaria fortunei (KUNZE) J. Sm., a new phenylpropanoid glycoside, fortunamide (1), was isolated and characterized by spectroscopic methods. Together with a new glycoside, 9 known compounds, including three curcuminoids (2–4), two isoprenylated flavonoids (5, 6), two flavonoids (7, 8), one monoterpenoid (9) and one phenolic acid (10) were isolated and identified by spectral data analysis from the rhizomes of Drynaria fortunei (KUNZE) J. Sm. Eight of them were isolated from Drynaria fortunei (KUNZE) J. Sm. for the first time.

scholarly journals Quantitative Spectral Data Analysis Using Extreme Learning Machines Algorithm Incorporated with PCA

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 18
Author(s):  
Michael Li ◽  
Santoso Wibowo ◽  
Wei Li ◽  
Lily D. Li
Keyword(s):  
Data Analysis ◽  
Conceptual Framework ◽  
Spectral Data ◽  
Learning Algorithm ◽  
Structural Parameters ◽  
Inverse Function ◽  
Principal Component ◽  
Learning Task ◽  
Classification Problems ◽  
Spectral Data Analysis

Extreme learning machine (ELM) is a popular randomization-based learning algorithm that provides a fast solution for many regression and classification problems. In this article, we present a method based on ELM for solving the spectral data analysis problem, which essentially is a class of inverse problems. It requires determining the structural parameters of a physical sample from the given spectroscopic curves. We proposed that the unknown target inverse function is approximated by an ELM through adding a linear neuron to correct the localized effect aroused by Gaussian basis functions. Unlike the conventional methods involving intensive numerical computations, under the new conceptual framework, the task of performing spectral data analysis becomes a learning task from data. As spectral data are typical high-dimensional data, the dimensionality reduction technique of principal component analysis (PCA) is applied to reduce the dimension of the dataset to ensure convergence. The proposed conceptual framework is illustrated using a set of simulated Rutherford backscattering spectra. The results have shown the proposed method can achieve prediction inaccuracies of less than 1%, which outperform the predictions from the multi-layer perceptron and numerical-based techniques. The presented method could be implemented as application software for real-time spectral data analysis by integrating it into a spectroscopic data collection system.

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