Please use this identifier to cite or link to this item: https://scholar.ptuk.edu.ps/handle/123456789/425
Title: Studying the Possibility of Indirect Metabolite Microorganisms’ Classification Using Electronic Tongue and Multivariate Data Analysis
Other Titles: إمكانيّة التَّصنيف الغير مباشر للكائنات الحيّة الدَّقيقة من خلال مُنتجات عملياتِ الأيض لها بواسطة مجس كيماوي (اللّسان الإلكتروني) والتَّحليل المتعدد العوامل للبيانات
Authors: Abu Rumaila, Basima Ali
Keywords: Electronic tongue
Metabolite
Microorganisms’ classification
principal component analysis (PCA)
Issue Date: Jan-2019
Publisher: Palestine Technical University- Kadoorie
Citation: Abu Rumaila, Basima Ali.(2019). Studying the Possibility of Indirect Metabolite Microorganisms’ Classification Using Electronic Tongue and Multivariate Data Analysis. Tulkerm,Palestine:Palestine Technical University- Kadoorie
Abstract: Chemical sensor systems become increasingly popular and promising analytical tools for various liquid analyses. One of these systems is the electronic tongue (ET), which is based on a multi-sensor array set with high cross-sensitivity and low selectivity characteristics. This research is a trial to investigate the possibility of using a potentiometric ET as a fast and alternative assessment tool for (complex and native state) bimolecular microorganism’s (bacterial and fungal species) foot-printing in a liquid media. The study was carried out by collecting 44 different fungal and bacterial isolates. These microorganisms were cultivated on suitable liquid media, where the filtrated media were then analyzed using Astree II Alpha MOS ET during their growth cycle. After that, the collected data were analyzed using multivariate data analysis MVDA methods (basically using principal component analysis PCA) for microbial clustering according to their similarities and/ or differences among each other and to follow their growth rate. In the meantime, the clustering patterns of these microorganisms were validated using molecular phylogenetic tree. The results of this study were promising, since ET’s used sensors array showed high discrimination power between samples ranged from 0.927 to 1 for fungi and from 0.960 to 0.999 for bacteria at the end of testing period. In which, a PCA scores plot with 98 and 96 discrimination index (Di) for fungi and 95 Di for bacterial clustering patterns were indicated. Moreover, the similarity test revealed a high similarity of 27.18% Di among F5 and F6 fungal isolates in group 1, 70.52% Di among F14 and F21 in group 2 fungal isolates, 3.18% Di among B3 and B11 in group 1 bacterial isolates and 12.91% Di among B13 and B22 in group 2 bacterial isolates. Also, the PCA clustering patterns were very similar to the validated molecular phylogenetic tree showing the relationship between isolates. Furthermore, ET could follow microbial growth and overlapping (stop of chemical change) in the liquid media
URI: https://scholar.ptuk.edu.ps/handle/123456789/425
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