Salbreiter Markus, Frempong Sandra Baaba, Even Sabrina, Wagenhaus Annette, Girnus Sophie, Rösch Petra, Popp Jürgen
Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany.
InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany.
Molecules. 2024 Dec 17;29(24):5956. doi: 10.3390/molecules29245956.
The rapid and precise identification of microorganisms is essential in environmental science, pharmaceuticals, food safety, and medical diagnostics. Raman spectroscopy, valued for its ability to provide detailed chemical and structural information, has gained significant traction in these fields, especially with the adoption of various excitation wavelengths and tailored optical setups. The choice of wavelength and setup in Raman spectroscopy is influenced by factors such as applicability, cost, and whether bulk or single-cell analysis is performed, each impacting sensitivity and specificity in bacterial detection. In this study, we investigate the potential of different excitation wavelengths for bacterial identification, utilizing a mock culture composed of six bacterial species: three Gram-positive (, , and ) and three Gram-negative (, , and ). To improve bacterial classification, we applied machine learning models to analyze and extract unique spectral features from Raman data. The results indicate that the choice of excitation wavelength significantly influences the bacterial spectra obtained, thereby impacting the accuracy and effectiveness of the subsequent classification results.
微生物的快速精确鉴定在环境科学、制药、食品安全和医学诊断中至关重要。拉曼光谱因其能够提供详细的化学和结构信息而受到重视，在这些领域已获得显著关注，特别是随着各种激发波长的采用和定制光学装置的应用。拉曼光谱中波长和装置的选择受到诸如适用性、成本以及是进行批量分析还是单细胞分析等因素的影响，每种因素都会影响细菌检测中的灵敏度和特异性。在本研究中，我们利用由六种细菌组成的模拟培养物来研究不同激发波长用于细菌鉴定的潜力：三种革兰氏阳性菌（、和）和三种革兰氏阴性菌（、和）。为了改进细菌分类，我们应用机器学习模型来分析和从拉曼数据中提取独特的光谱特征。结果表明，激发波长的选择显著影响所获得的细菌光谱，从而影响后续分类结果的准确性和有效性。
