Abstract：
Rapid identification of bacterial species in patient samples is essential for the treatment of infectious diseases and the economics of health care. In this study, we investigated an algorithm to improve the accuracy of bacterial species identification with fluorescence spectroscopy based on autofluorescence from bacteria, and excitation wavelengths suitable for identification. The diagnostic accuracy of each algorithm for ten bacterial species was verified in a machine learning classifier algorithm. The three machine learning algorithms with the highest diagnostic accuracy, extra tree (ET), logistic regression (LR), and multilayer perceptron (MLP), were used to determine the number and wavelength of excitation wavelengths suitable for the diagnosis of bacterial species. The key excitation wavelengths for the diagnosis of bacterial species were 280 nm, 300 nm, 380 nm, and 480 nm, with 280 nm being the most important. The median diagnostic accuracy was equivalent to that of 200 excitation wavelengths when two excitation wavelengths were used for ET and LR, and three excitation wavelengths for MLP. These results demonstrate that there is an optimum wavelength range of excitation wavelengths required for spectroscopic measurement of bacterial autofluorescence for bacterial species identification, and that measurement of only a few wavelengths in this range is sufficient to achieve sufficient accuracy for diagnosis of bacterial species.
摘要：
快速鉴定患者样品中的细菌种类对于传染病的治疗和医疗保健的经济学至关重要。在这项研究中,我们研究了一种基于来自细菌的自发荧光的荧光光谱法提高细菌种类识别准确性的算法，和激发波长适合识别。在机器学习分类器算法中验证了每种算法对10种细菌的诊断准确性。具有最高诊断准确性的三种机器学习算法，额外的树（ET），逻辑回归(LR)，和多层感知器(MLP)，用于确定适合于细菌种类诊断的激发波长的数量和波长。诊断细菌种类的关键激发波长为280nm,300nm,380nm,和480纳米，280纳米是最重要的。当两个激发波长用于ET和LR时，中值诊断精度相当于200个激发波长，和MLP的三个激发波长。这些结果表明，有一个最佳波长范围的激发波长所需的光谱测量的细菌自发荧光的细菌物种鉴定，并且仅测量该范围内的几个波长就足以达到诊断细菌物种的足够准确性。
