Biological aerosols include viruses, bacteria and related agglomerates, as well as fungi, pollen, plant and animal debris. The diffusion of bioaerosol particles will seriously affect the development of agriculture and the health of animals and human beings, and even water circulation and climate. In recent years, using laser-induced intrinsic fluorescence, researchers have constructed various optical instruments to enable real-time monitoring of particulate matter in the air. The carefully designed devices use multiple excitation wavelengths and fluorescence channels, so they enable aerosol identification with multiple parameters, but exhibit drawbacks of high cost and bulky appearance. As for portable devices, there is usually a single-wavelength excitation light source to excite the fluorescence signal, and the discrimination between fluorescent and non-fluorescent particles is achieved in most devices. However, it is still a challenge to achieve discrimination between different bioaerosol types with portable devices. In our previous work, we developed a fluorescent particle counter and there was a great linear consistency between the number of fluorescent particles measured by the counter and the number of bacteria measured by the culture method. In the practical application of this counter, some fluorescent interfering particles in air possibly trigger false alarms. In this work, we propose a method for preliminary differentiation of aerosol particle types based on normalized fluorescence, and apply it to the fluorescent particle counter, which will effectively reduce the false alarm rate of the instrument while monitoring biological aerosols.

We designed a fluorescent particle counter with a 405 nm laser diode as the light source and lens group to collimate and focus the excitation light beam. The optical axis of the excitation light path is perpendicular to the optical axis of the fluorescence receiving light path, and there is a photosensitive zone near their intersection. The airflow brings the aerosol particles to the photosensitive zone. The laser light will be scattered by the particles to produce elastic scattered light, and there will also be intrinsic fluorescence excited with biological particles. The scattered light is separated from the fluorescence using a dichroic mirror and received by two photomultiplier tubes (PMTs). The ratio of the fluorescence pulse amplitude of each particle signal to its corresponding scattered light pulse amplitude is calculated to obtain the normalized fluorescence value (Fig. 2).

Polystyrene latex (PSL) microspheres, phosphate buffer solution (PBS), and fluorescent microspheres were used as detection objects to evaluate the instrument’s ability to classify fluorescent and non-fluorescent particles. From the experimental results, we found that there are almost no fluorescent particles in PSL microspheres and PBS, and the proportion of fluorescent particles in the total number of particles tends to be close to 0, while the fluorescent microspheres exhibit fluorescent particles with >84% of the total number of particles (Table 1). Therefore, the instrument can effectively distinguish between fluorescent and non-fluorescent particles. Seven samples including Bacillus subtilis, Escherichia coli, riboflavin, B800 fluorescent microspheres, cigarette smoke, vehicle exhaust, and general office ambient air, were tested using the instrument, and the results of 12 groups of samples with different concentrations were recorded. The normalized fluorescence values of each group of fluorescence-scattering pulses were calculated and divided into four intervals, and the number of fluorescent particles in each interval and their proportions to the total fluorescent particles were calculated. The results show that the fluorescence particle numbers of different kinds of aerosol samples have different distributions in the four intervals, so the classification of aerosols can be achieved by detecting and calculating the normalized fluorescence values of each sample (Fig. 3). The obtained fluorescence particle numbers of the different samples within the four intervals were visualized using principal component analysis (PCA). With a summed contribution of 75.32%, the first two principal components (PC1 and PC2) of different aerosol samples already contain most of the information and show a good clustering effect on the graph (Fig. 4). The classification results could be more visually presented with three-dimensional score plot (Fig. 5).

In this paper, we proposed a method to classify aerosol particles using normalized fluorescence signals, which was applied to a fluorescent particle counter with a 405 nm laser diode as the excitation light source, allowing preliminary classification of different aerosol particles such as bacteria, riboflavin, cigarette smoke, vehicle exhaust, and fluorescent microspheres. The normalized values of the fluorescence signals of individual particles on the corresponding scattered light signals were divided into four intervals and counted in each of the four intervals. The experimental results were visualized using PCA. This method can be applied to the identification and early warning of microbial particles, and reduce the interference of non-biological fluorescent particles in the environment.