ARTIFICIAL NEURAL NETWORKS (ANNs) APPLIED TO ATR-FTIR SPECTRA TO CLASSIFY MEDICALLY IMPORTANT Trichosporon SPECIES-Reference-Cited by-同舟云学术

ARTIFICIAL NEURAL NETWORKS (ANNs) APPLIED TO ATR-FTIR SPECTRA TO CLASSIFY MEDICALLY IMPORTANT Trichosporon SPECIES

Published:2021-12-15 Issue: Volume: Page:51-54
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Container-title:GLOBAL JOURNAL FOR RESEARCH ANALYSIS
language:en
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Author:

Parashar Abhila¹,Rastogi Vijaylatha²,Sharma Mitanshu³,Bhatnagar Monica⁴

Affiliation:

1. Research scholar Department of Microbiology, Jawaharlal Nehru Medical College JLNMC & Hospital, Ajmer Rajasthan, India.

2. Sr. Professor, Department of Microbiology, Jawaharlal Nehru Medical College JLNMC & Hospital, Ajmer Rajasthan, India.

3. Research scholar, Department of Smart Healthcare Devices, Indian Institute of Technology IIT, Jodhpur Rajasthan, India.

4. Professor, Department of Microbiology, Maharshi Dayanand Saraswati University MDSU, Ajmer Rajasthan, India.

Abstract

To distinguish clinically signicant fungus, Fourier transform infrared spectroscopy (FTIR) was used. In this work, 75 Trichosporon strains from ve different species were cultivated on SDA media and FTIR attenuated total reection (ATR) readings was taken. The classication (FTIR spectra) results of cluster analysis were compared to articial neural network (ANN) analysis (supervised approach). Validation of training set showed that both techniques properly categorized 100% of the spectra, at least for T. asahii (n = 62) and T. inkin (n = 8). With the addition of T. loubieri (n=1) and T. asteroids (n=1), the ANN's accuracy became reliant on the training database, resulting in 90% to 100% classication.

Publisher

World Wide Journals

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