PDF(Pubmed)
Abstract：
Sensitively detecting hazardous and suspected bioaerosols is crucial for safeguarding public health. The potential impact of pollen on identifying bacterial species through fluorescence spectra should not be overlooked. Before the analysis, the spectrum underwent preprocessing steps, including normalization, multivariate scattering correction, and Savitzky-Golay smoothing. Additionally, the spectrum was transformed using difference, standard normal variable, and fast Fourier transform techniques. A random forest algorithm was employed for the classification and identification of 31 different types of samples. The fast Fourier transform improved the classification accuracy of the sample excitation-emission matrix fluorescence spectrum data by 9.2%, resulting in an accuracy of 89.24%. The harmful substances, including Staphylococcus aureus, ricin, beta-bungarotoxin, and Staphylococcal enterotoxin B, were clearly distinguished. The spectral data transformation and classification algorithm effectively eliminated the interference of pollen on other components. Furthermore, a classification and recognition model based on spectral feature transformation was established, demonstrating excellent application potential in detecting hazardous substances and protecting public health. This study provided a solid foundation for the application of rapid detection methods for harmful bioaerosols.
摘要：
灵敏地检测危险和可疑的生物气溶胶对于保障公众健康至关重要。花粉对通过荧光光谱识别细菌物种的潜在影响不容忽视。在分析之前,光谱经过预处理步骤，包括规范化，多元散射校正，和Savitzky-Golay平滑。此外,使用差异转换光谱，标准正态变量，和快速傅里叶变换技术。采用随机森林算法对31种不同类型的样本进行分类和识别。快速傅里叶变换将样品激发-发射矩阵荧光光谱数据的分类精度提高了9.2%,结果准确率为89.24%。有害物质,包括金黄色葡萄球菌,蓖麻毒素,β-银环蛇毒素,和葡萄球菌肠毒素B,被明确区分。光谱数据变换和分类算法有效地消除了花粉对其他成分的干扰。此外，建立了基于光谱特征变换的分类识别模型,在检测有害物质和保护公众健康方面具有出色的应用潜力。本研究为有害生物气溶胶快速检测方法的应用奠定了坚实的基础。
