State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China.
Molecules. 2024 Jun 21;29(13):2966. doi: 10.3390/molecules29132966.
The rapid and sensitive detection of pathogenic and suspicious bioaerosols are essential for public health protection. The impact of pollen on the identification of bacterial species by Raman and Fourier-Transform Infrared (FTIR) spectra cannot be overlooked. The spectral features of the fourteen class samples were preprocessed and extracted by machine learning algorithms to serve as input data for training purposes. The two types of spectral data were classified using classification models. The partial least squares discriminant analysis (PLS-DA) model achieved classification accuracies of 78.57% and 92.85%, respectively. The Raman spectral data were accurately classified by the support vector machine (SVM) algorithm, with a 100% accuracy rate. The two spectra and their fusion data were correctly classified with 100% accuracy by the random forest (RF) algorithm. The spectral processed algorithms investigated provide an efficient method for eliminating the impact of pollen interference.
快速、灵敏地检测致病和可疑的生物气溶胶对于保护公众健康至关重要。花粉对拉曼和傅里叶变换红外（FTIR）光谱鉴定细菌种类的影响不容忽视。通过机器学习算法对十四类样本的光谱特征进行预处理和提取，作为训练目的的输入数据。使用分类模型对两种类型的光谱数据进行分类。偏最小二乘判别分析（PLS-DA）模型分别实现了 78.57%和 92.85%的分类准确率。支持向量机（SVM）算法对拉曼光谱数据进行了准确分类，准确率达到 100%。随机森林（RF）算法对两种光谱及其融合数据的分类准确率均为 100%。研究中所采用的光谱处理算法为消除花粉干扰的影响提供了一种有效的方法。
