InPollen analyzerIn China, optical imaging technology captures three-dimensional particle morphology data through high-resolution cameras, combined with digital holographic imaging and fluorescence spectroscopy analysis, to achieve precise classification and recognition of pollen particles. The specific implementation method is as follows:
1. Digital holographic imaging: holographic capture of three-dimensional shape data
Technical principle:
Digital holographic imaging utilizes laser interferometry to record the three-dimensional morphological information of pollen particles. After the laser beam irradiates the pollen, a hologram formed by the scattered light is captured by a dual 90 ° high-resolution camera (such as the SwisensPoleno series), and then the three-dimensional morphology of the particles (size, circumference, shape, etc.) is reconstructed by simulating the optical diffraction process through a computer.
Advantage:
High resolution: capable of distinguishing particles up to 0.5 μ m in size, distinguishing pollen with similar morphology but different types (such as birch and alder pollen).
3D reconstruction: By using multi angle imaging, the occlusion problem of traditional 2D imaging is eliminated, and the recognition accuracy is improved.
case
The SwisensPoleno Jupiter model achieves a pollen classification accuracy of 96% through digital holographic imaging combined with deep learning algorithms.
2. Fluorescence spectroscopy analysis: identification of chemical components through "molecular fingerprinting"
Technical principle:
Using a multi wavelength excitation light source (such as 280/365/405 nm) to irradiate pollen particles and excite their internal chemical substances (such as chlorophyll and aromatic compounds) to emit fluorescence. Fluorescence spectra were detected using a silicon photomultiplier tube (SiPM) to analyze the chemical composition of particles.
Advantage:
High specificity: can distinguish particles with similar morphology but different chemical compositions (such as birch pollen and poplar pollen).
Anti interference: Filter non biological particles (such as dust and fibers) through fluorescence threshold to improve data purity.
case
The SwisensPoleno series improves classification accuracy to 99.2% through fluorescence spectroscopy analysis, supporting coverage of particle sizes ranging from 0.5-300 μ m (including pollen, spores, and PM2.5).
3. Multimodal fusion: cross validation of morphological and chemical features
Technical principle:
Integrating the morphological data of digital holographic imaging with the chemical data of fluorescence spectra, and establishing a particle feature library through machine learning algorithms such as supervised learning and deep learning to achieve automated classification.
Advantage:
High robustness: A single technology may be affected by environmental interference (such as humidity affecting particle size measurement), while multimodal fusion can cross validate results and improve reliability.
Dynamic learning: Through the "variant recognition" mechanism, particles that differ from the database by more than a threshold are marked and uploaded to the cloud for expert correction, continuously optimizing the model.
case
Sauvageat et al. used the SwisensPoleno monitor to distinguish between pollen and non pollen particles with an accuracy of 96% through elastic light scattering, fluorescence images, and digital holographic images, and achieved an accuracy of 90% in identifying six types of pollen.
4. Environmental adaptability optimization of optical imaging technology
Protective design:
IP65 protection level: prevents dust and rainwater from entering the detection chamber, avoiding sample cross contamination.
Constant temperature control: The temperature of the detection chamber is maintained stable through a heating module to avoid pollen cracking caused by low temperatures or particle adhesion caused by high humidity.
Extreme environment testing:
Low temperature stability: The continuous operation stability at -20 ° C reaches 99.8%, with a data error rate of<1% (such as Helsing base station equipment).
High flow rate sampling: At a sampling flow rate of 40 L/min, the time resolution reaches 10 minutes and supports dynamic concentration monitoring.
5. Data output and application of optical imaging technology
Real time data display:
Particle concentration: Real time display of pollen concentration in the air in units of particles/m ³.
Classification information: Supports genus/family level classification (such as birch family, grasses).
Shape parameters: Output three-dimensional data such as particle size, circumference, and shape.
Fluorescence spectrum: Display the fluorescence lifetime spectrum of the chemical composition of particles.
Application Scenario:
Allergy Warning: Provides minute level warnings for asthma patients, reducing response time from traditional 7 days to 1 hour.
Agricultural pollination: Optimize orchard pollination efficiency and distinguish between bees' preference for pollen and non target pollen.
Climate research: provide high-precision bioaerosol data for sensitive areas such as the Qinghai Tibet Plateau and the North China Plain, and strengthen the ability to predict climate.