SprayQuantAI® (SQA for short) is an advanced, AI-assisted method for the measurement and characterization of particles and droplets in sprays. Individual light scattering signals are analyzed using artificial intelligence to determine the physical properties of the particles. SprayQuantAI® enables a statistically representative evaluation of individual particles and thus allows continuous real-time monitoring of the entire spray process with integration times.
The preliminary work on SprayQuantAI® began as early as 2021 based on experimental measurement data [1]. In these studies, light scattering signals from paint droplets generated by a rotary atomizer were analyzed. In a subsequent step, simulation-based studies were conducted and a further scientific publication was presented [2, 3], in which SprayQuantAI® was introduced as the basis for a more compact and cost-effective measurement instrument for particle characterization in flows. The technological foundation is the TSTOF method [4], whose instruments are also known under the brand names SpraySpy® or ParticleTensorAI®.
In a further application, published in [5], SprayQuantAI® was successfully used to determine the refractive index of individual droplets in a flow. The most recently documented application is described in [4] and demonstrates the use of SprayQuantAI® for the characterization of milk droplets during the spray drying process. Further publications are in preparation.
SprayQuantAI® can be deployed as a software update in combination with existing TSTOF instruments such as SpraySpy®. In addition, we also provide customized hardware solutions tailored to the specific requirements of your application
References
[1] Schaefer, W., Tropea, C., Wigger, G., & Eierhoff, D. (2021). Spray measurements with the time-shift technique. Measurement Science and Technology, 32(10), 105202. https://doi.org/10.1088/1361-6501/ac0467
[2] Schaefer, W., & Li, L. (2024). Particle characterization by analyzing light scattering signals with a machine learning approach. Applied Optics, 63(29), 7701. https://doi.org/10.1364/AO.531346
[3] Schäfer, W., & Li, L. (2024). Particle characterization by analyzing light scattering signals with a machine learning approach. In Proceedings of the 16th Triennial International Conference on Liquid Atomization and Spray Systems (ICLASS 2024).
[4] Schaefer, W., Li, L., Stegmann, P., & Terada, M. (2026). Technical report on the TSTOF measurement method: Technical basics, historical development, and comparison with other laser-based measurement methods. Photonics, 13(1), 56. https://doi.org/10.3390/photonics13010056
[5] Schaefer, W. (2025). Refractive index determination of dynamic droplets in a flow by analyzing light scattering signals with a machine learning approach. In Proceedings of the 11th International Symposium on Turbulence, Heat and Mass Transfer, 1–8. https://doi.org/10.1615/THMT-25
[6] Doan, A. M. A., Schaefer, W., Chernoray, V., & Schäfer, W. (2025). Analysis of the light scattering of a colloid droplet on a Gaussian beam to determine the suspension concentration. In Proceedings of ILASS-Europe