Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting
Biomolecular abundance detection of fermentation microorganisms is significant for the accurate regulation of fermentation, which is conducive to reducing fermentation costs and improving the yield of target products. However, the development of an accurate analytical method for the detection of bio...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Molecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1420-3049/29/1/157 |
| _version_ | 1797358374916980736 |
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| author | Xinli Li Suyi Li Qingyi Wu |
| author_facet | Xinli Li Suyi Li Qingyi Wu |
| author_sort | Xinli Li |
| collection | DOAJ |
| description | Biomolecular abundance detection of fermentation microorganisms is significant for the accurate regulation of fermentation, which is conducive to reducing fermentation costs and improving the yield of target products. However, the development of an accurate analytical method for the detection of biomolecular abundance still faces important challenges. Herein, we present a non-invasive biomolecular abundance detection method based on Raman spectra combined with target extraction and multimodel fitting. The high gain of the eXtreme Gradient Boosting (XGBoost) algorithm was used to extract the characteristic Raman peaks of metabolically active proteins and nucleic acids within <i>E. coli</i> and yeast. The test accuracy for different culture times and cell cycles of <i>E. coli</i> was 94.4% and 98.2%, respectively. Simultaneously, the Gaussian multi-peak fitting algorithm was exploited to calculate peak intensity from mixed peaks, which can improve the accuracy of biomolecular abundance calculations. The accuracy of Gaussian multi-peak fitting was above 0.9, and the results of the analysis of variance (ANOVA) measurements for the lag phase, log phase, and stationary phase of <i>E. coli</i> growth demonstrated highly significant levels, indicating that the intracellular biomolecular abundance detection was consistent with the classical cell growth law. These results suggest the great potential of the combination of microbial intracellular abundance, Raman spectra analysis, target extraction, and multimodel fitting as a method for microbial fermentation engineering. |
| first_indexed | 2024-03-08T15:01:04Z |
| format | Article |
| id | doaj.art-18094a1feeea474e80f2525959409676 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-08T15:01:04Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-18094a1feeea474e80f25259594096762024-01-10T15:04:20ZengMDPI AGMolecules1420-30492023-12-0129115710.3390/molecules29010157Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel FittingXinli Li0Suyi Li1Qingyi Wu2College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, ChinaCollege of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Changchun 130033, ChinaBiomolecular abundance detection of fermentation microorganisms is significant for the accurate regulation of fermentation, which is conducive to reducing fermentation costs and improving the yield of target products. However, the development of an accurate analytical method for the detection of biomolecular abundance still faces important challenges. Herein, we present a non-invasive biomolecular abundance detection method based on Raman spectra combined with target extraction and multimodel fitting. The high gain of the eXtreme Gradient Boosting (XGBoost) algorithm was used to extract the characteristic Raman peaks of metabolically active proteins and nucleic acids within <i>E. coli</i> and yeast. The test accuracy for different culture times and cell cycles of <i>E. coli</i> was 94.4% and 98.2%, respectively. Simultaneously, the Gaussian multi-peak fitting algorithm was exploited to calculate peak intensity from mixed peaks, which can improve the accuracy of biomolecular abundance calculations. The accuracy of Gaussian multi-peak fitting was above 0.9, and the results of the analysis of variance (ANOVA) measurements for the lag phase, log phase, and stationary phase of <i>E. coli</i> growth demonstrated highly significant levels, indicating that the intracellular biomolecular abundance detection was consistent with the classical cell growth law. These results suggest the great potential of the combination of microbial intracellular abundance, Raman spectra analysis, target extraction, and multimodel fitting as a method for microbial fermentation engineering.https://www.mdpi.com/1420-3049/29/1/157biomolecular abundance detectionfermentative microorganismsRaman spectratarget extractionmultimodel fitting |
| spellingShingle | Xinli Li Suyi Li Qingyi Wu Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting Molecules biomolecular abundance detection fermentative microorganisms Raman spectra target extraction multimodel fitting |
| title | Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting |
| title_full | Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting |
| title_fullStr | Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting |
| title_full_unstemmed | Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting |
| title_short | Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting |
| title_sort | non invasive detection of biomolecular abundance from fermentative microorganisms via raman spectra combined with target extraction and multimodel fitting |
| topic | biomolecular abundance detection fermentative microorganisms Raman spectra target extraction multimodel fitting |
| url | https://www.mdpi.com/1420-3049/29/1/157 |
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