Spectroscopic Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups and Special Machine Learning Algorithm
FTIR (Fourier transform infrared spectroscopy) is one analytical technique of the absorption of infrared radiation. FTIR can also be used as a tool to characterize profiles of biomolecules in bacterial cells, which can be useful in differentiating different bacteria. Considering that different bacte...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Antibiotics |
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| Online Access: | https://www.mdpi.com/2079-6382/12/10/1502 |
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| author | Claudia P. Barrera-Patiño Jennifer M. Soares Kate C. Branco Natalia M. Inada Vanderlei Salvador Bagnato |
| author_facet | Claudia P. Barrera-Patiño Jennifer M. Soares Kate C. Branco Natalia M. Inada Vanderlei Salvador Bagnato |
| author_sort | Claudia P. Barrera-Patiño |
| collection | DOAJ |
| description | FTIR (Fourier transform infrared spectroscopy) is one analytical technique of the absorption of infrared radiation. FTIR can also be used as a tool to characterize profiles of biomolecules in bacterial cells, which can be useful in differentiating different bacteria. Considering that different bacterial species have different molecular compositions, it will then result in unique FTIR spectra for each species and even bacterial strains. Having this important tool, here, we have developed a methodology aimed at refining the analysis and classification of the FTIR absorption spectra obtained from samples of <i>Staphylococcus aureus</i>, with the implementation of machine learning algorithms. In the first stage, the system conforming to four specified species groups, Control, Amoxicillin induced (AMO), Gentamicin induced (GEN), and Erythromycin induced (ERY), was analyzed. Then, in the second stage, five hidden samples were identified and correctly classified as with/without resistance to induced antibiotics. The total analyses were performed in three windows, Carbohydrates, Fatty Acids, and Proteins, of five hundred spectra. The protocol for acquiring the spectral data from the antibiotic-resistant bacteria via FTIR spectroscopy developed by Soares et al. was implemented here due to demonstrating high accuracy and sensitivity. The present study focuses on the prediction of antibiotic-induced samples through the implementation of the hierarchical cluster analysis (HCA), principal component analysis (PCA) algorithm, and calculation of confusion matrices (CMs) applied to the FTIR absorption spectra data. The data analysis process developed here has the main objective of obtaining knowledge about the intrinsic behavior of <i>S. aureus</i> samples within the analysis regions of the FTIR absorption spectra. The results yielded values with 0.7 to 1 accuracy and high values of sensitivity and specificity for the species identification in the CM calculations. Such results provide important information on antibiotic resistance in samples of <i>S. aureus</i> bacteria for potential application in the detection of antibiotic resistance in clinical use. |
| first_indexed | 2024-03-10T21:29:16Z |
| format | Article |
| id | doaj.art-49f84954ae7d4c6b9e9755a57cbb471d |
| institution | Directory Open Access Journal |
| issn | 2079-6382 |
| language | English |
| last_indexed | 2024-03-10T21:29:16Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antibiotics |
| spelling | doaj.art-49f84954ae7d4c6b9e9755a57cbb471d2023-11-19T15:26:24ZengMDPI AGAntibiotics2079-63822023-09-011210150210.3390/antibiotics12101502Spectroscopic Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups and Special Machine Learning AlgorithmClaudia P. Barrera-Patiño0Jennifer M. Soares1Kate C. Branco2Natalia M. Inada3Vanderlei Salvador Bagnato4São Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense n° 400, Parque Arnold Schimidt, São Carlos 13566-590, SP, BrazilSão Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense n° 400, Parque Arnold Schimidt, São Carlos 13566-590, SP, BrazilSão Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense n° 400, Parque Arnold Schimidt, São Carlos 13566-590, SP, BrazilSão Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense n° 400, Parque Arnold Schimidt, São Carlos 13566-590, SP, BrazilSão Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense n° 400, Parque Arnold Schimidt, São Carlos 13566-590, SP, BrazilFTIR (Fourier transform infrared spectroscopy) is one analytical technique of the absorption of infrared radiation. FTIR can also be used as a tool to characterize profiles of biomolecules in bacterial cells, which can be useful in differentiating different bacteria. Considering that different bacterial species have different molecular compositions, it will then result in unique FTIR spectra for each species and even bacterial strains. Having this important tool, here, we have developed a methodology aimed at refining the analysis and classification of the FTIR absorption spectra obtained from samples of <i>Staphylococcus aureus</i>, with the implementation of machine learning algorithms. In the first stage, the system conforming to four specified species groups, Control, Amoxicillin induced (AMO), Gentamicin induced (GEN), and Erythromycin induced (ERY), was analyzed. Then, in the second stage, five hidden samples were identified and correctly classified as with/without resistance to induced antibiotics. The total analyses were performed in three windows, Carbohydrates, Fatty Acids, and Proteins, of five hundred spectra. The protocol for acquiring the spectral data from the antibiotic-resistant bacteria via FTIR spectroscopy developed by Soares et al. was implemented here due to demonstrating high accuracy and sensitivity. The present study focuses on the prediction of antibiotic-induced samples through the implementation of the hierarchical cluster analysis (HCA), principal component analysis (PCA) algorithm, and calculation of confusion matrices (CMs) applied to the FTIR absorption spectra data. The data analysis process developed here has the main objective of obtaining knowledge about the intrinsic behavior of <i>S. aureus</i> samples within the analysis regions of the FTIR absorption spectra. The results yielded values with 0.7 to 1 accuracy and high values of sensitivity and specificity for the species identification in the CM calculations. Such results provide important information on antibiotic resistance in samples of <i>S. aureus</i> bacteria for potential application in the detection of antibiotic resistance in clinical use.https://www.mdpi.com/2079-6382/12/10/1502<i>Staphylococcus aureus</i>FTIR spectroscopyantibiotic-resistant bacteriaamoxicillin inducedgentamicin inducederythromycin induced |
| spellingShingle | Claudia P. Barrera-Patiño Jennifer M. Soares Kate C. Branco Natalia M. Inada Vanderlei Salvador Bagnato Spectroscopic Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups and Special Machine Learning Algorithm Antibiotics <i>Staphylococcus aureus</i> FTIR spectroscopy antibiotic-resistant bacteria amoxicillin induced gentamicin induced erythromycin induced |
| title | Spectroscopic Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups and Special Machine Learning Algorithm |
| title_full | Spectroscopic Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups and Special Machine Learning Algorithm |
| title_fullStr | Spectroscopic Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups and Special Machine Learning Algorithm |
| title_full_unstemmed | Spectroscopic Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups and Special Machine Learning Algorithm |
| title_short | Spectroscopic Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups and Special Machine Learning Algorithm |
| title_sort | spectroscopic identification of bacteria resistance to antibiotics by means of absorption of specific biochemical groups and special machine learning algorithm |
| topic | <i>Staphylococcus aureus</i> FTIR spectroscopy antibiotic-resistant bacteria amoxicillin induced gentamicin induced erythromycin induced |
| url | https://www.mdpi.com/2079-6382/12/10/1502 |
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