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Use of Raman spectroscopy to decrease time for identifying the species of Candida growth in cultures.

Chouthai NS, Shah AA, Salimnia H, Palyvoda O, Devpura S, Klein M, Asmar B - Avicenna J Med Biotechnol (2015 Jan-Mar)

Bottom Line: Pure cultures of five Candida species were evaluated using RS to build a limited signature library. 'Raman Processing' (RP) software was used for Principal Component Analysis (PCA) and Differential Functional Analysis (DFA).Eleven principal components described at least 95% variance in the spectra.Raman signatures from these known Candida species were able to identify the species of unknown Candida cultures with 100% accuracy.

View Article: PubMed Central - PubMed

Affiliation: Division of Neonatal-Perinatal Medicine, Wayne State University, Detroit, MI, United States of America.

ABSTRACT

Background: The objective of this study is to establish Raman signatures from pure cultures of different Candida species using Raman Spectroscopy (RS) and use these signatures for rapid identification of unknown Candida species.

Methods: Pure cultures of five Candida species were evaluated using RS to build a limited signature library. 'Raman Processing' (RP) software was used for Principal Component Analysis (PCA) and Differential Functional Analysis (DFA).

Results: Eleven principal components described at least 95% variance in the spectra. Raman signatures from these known Candida species were able to identify the species of unknown Candida cultures with 100% accuracy.

Conclusion: Raman spectroscopy can improve early identification of Candida species and may facilitate early optimal antifungal therapy.

No MeSH data available.


Two dimensional graphical representation of Principal Component Analysis (3A) and Differential Function Analysis (3B).
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Figure 0003: Two dimensional graphical representation of Principal Component Analysis (3A) and Differential Function Analysis (3B).

Mentions: When processed with RP Software, five candida species examined showed eleven principal components generated by PCA, which account for 95.2 percent of the variance. Then the principal components were fed into DFA classifier, which enabled categorizing of unknown species. Figure 1 represents the mean normalized curves for each species and demonstrates the key differentiators among them. The chemical structure of the fungal elements represents different peaks within the Raman spectra. Based on the known spectra, the trained DFA classifier was able to identify unknown sample signatures with 100% accuracy. These differentiating peaks for different biological molecules have already been described (15, 16). The Raman shift regions (wavenumbers/cm-1) associated with significant peaks within the Raman spectra and corresponding biochemical elements are described in Table 1. The Raman signatures for all the unknown species evaluated were compiled and placed together in Figure 2. This figure again demonstrates key differences among the unknown samples. Figure 3A represents two dimensional graphical representation of Principal Component 1 and Principal Component 2, thus demonstrates first step of PCA. Similarly Figure 3B demonstrates two dimensional graphical representation of first step of DFA.


Use of Raman spectroscopy to decrease time for identifying the species of Candida growth in cultures.

Chouthai NS, Shah AA, Salimnia H, Palyvoda O, Devpura S, Klein M, Asmar B - Avicenna J Med Biotechnol (2015 Jan-Mar)

Two dimensional graphical representation of Principal Component Analysis (3A) and Differential Function Analysis (3B).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4388890&req=5

Figure 0003: Two dimensional graphical representation of Principal Component Analysis (3A) and Differential Function Analysis (3B).
Mentions: When processed with RP Software, five candida species examined showed eleven principal components generated by PCA, which account for 95.2 percent of the variance. Then the principal components were fed into DFA classifier, which enabled categorizing of unknown species. Figure 1 represents the mean normalized curves for each species and demonstrates the key differentiators among them. The chemical structure of the fungal elements represents different peaks within the Raman spectra. Based on the known spectra, the trained DFA classifier was able to identify unknown sample signatures with 100% accuracy. These differentiating peaks for different biological molecules have already been described (15, 16). The Raman shift regions (wavenumbers/cm-1) associated with significant peaks within the Raman spectra and corresponding biochemical elements are described in Table 1. The Raman signatures for all the unknown species evaluated were compiled and placed together in Figure 2. This figure again demonstrates key differences among the unknown samples. Figure 3A represents two dimensional graphical representation of Principal Component 1 and Principal Component 2, thus demonstrates first step of PCA. Similarly Figure 3B demonstrates two dimensional graphical representation of first step of DFA.

Bottom Line: Pure cultures of five Candida species were evaluated using RS to build a limited signature library. 'Raman Processing' (RP) software was used for Principal Component Analysis (PCA) and Differential Functional Analysis (DFA).Eleven principal components described at least 95% variance in the spectra.Raman signatures from these known Candida species were able to identify the species of unknown Candida cultures with 100% accuracy.

View Article: PubMed Central - PubMed

Affiliation: Division of Neonatal-Perinatal Medicine, Wayne State University, Detroit, MI, United States of America.

ABSTRACT

Background: The objective of this study is to establish Raman signatures from pure cultures of different Candida species using Raman Spectroscopy (RS) and use these signatures for rapid identification of unknown Candida species.

Methods: Pure cultures of five Candida species were evaluated using RS to build a limited signature library. 'Raman Processing' (RP) software was used for Principal Component Analysis (PCA) and Differential Functional Analysis (DFA).

Results: Eleven principal components described at least 95% variance in the spectra. Raman signatures from these known Candida species were able to identify the species of unknown Candida cultures with 100% accuracy.

Conclusion: Raman spectroscopy can improve early identification of Candida species and may facilitate early optimal antifungal therapy.

No MeSH data available.