State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Laser Spectroscopy, Shanxi University, Taiyuan, China.
Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, China.
J Biophotonics. 2024 Nov;17(11):e202400332. doi: 10.1002/jbio.202400332. Epub 2024 Sep 20.
Bacteria are the primary cause of infectious diseases, making rapid and accurate identification crucial for timely pathogen diagnosis and disease control. However, traditional identification techniques such as polymerase chain reaction and loop-mediated isothermal amplification are complex, time-consuming, and pose infection risks. This study explores remote (~3 m) bacterial identification using laser-induced breakdown spectroscopy (LIBS) with a Cassegrain reflective telescope. Principal component analysis (PCA) was employed to reduce the dimensionality of the LIBS spectral data, and the accuracy of support vector machine (SVM) and Random Forest (RF) algorithms was compared. Multiple repeated experiments showed that the RF model achieved a classification accuracy, recall, precision, and F1-score of 99.81%, 99.80%, 99.79%, and 0.9979, respectively, outperforming the SVM model and providing more accurate remote bacterial identification. The method based on laser-induced plasma spectroscopy and machine learning has broad application prospects, supporting noncontact disease diagnosis, improving public health, and advancing medical research and technological development.
细菌是传染病的主要原因，因此快速准确地识别病原体对于及时进行疾病诊断和控制至关重要。然而，聚合酶链反应和环介导等温扩增等传统鉴定技术复杂、耗时且存在感染风险。本研究探索了使用带有卡塞格林反射望远镜的激光诱导击穿光谱（LIBS）进行远程（~3m）细菌识别。主成分分析（PCA）用于降低 LIBS 光谱数据的维度，并比较了支持向量机（SVM）和随机森林（RF）算法的准确性。多次重复实验表明，RF 模型的分类准确率、召回率、精度和 F1 得分为 99.81%、99.80%、99.79%和 0.9979，分别优于 SVM 模型，提供了更准确的远程细菌识别。基于激光诱导等离子体光谱和机器学习的方法具有广阔的应用前景，支持非接触式疾病诊断，改善公共卫生，并推动医学研究和技术发展。
