YARMOUTH, ME - A new white paper by particle imaging authority Lew Brown of Fluid Imaging Technologies, Yarmouth, ME, documents how image quality affects the ability of imaging particle analyzers to produce accurate measurements and automatically recognize and classify different particles in a heterogeneous mixture. Reporting the results of two experiments conducted on the FlowCAM® particle imaging and analysis system, the white paper compares the measurements derived from sharp images to those derived from blurry images and concludes that deriving measurements from blurry or “fuzzy” images significantly reduces the accuracy of the resulting measurement and classification data. The FlowCAM automatically takes hi-resolution, digital images of microscopic particles and organisms, measures each one based on dozens of parameters in real time and automatically differentiates and classifies them for analysis.

In the first experiment, NIST traceable calibrated size beads (10µm ± .08µm) were imaged using two different flow cells to yield a group of sharp images and a group of blurry images. The sharp images averaged 9.81 µm by mean Equivalent Spherical Diameter (ESD) compared to 15.64 µm for the blurry images, which also yielded a higher standard deviation and coefficient of variability. In the second experiment, samples of Cosmarium algae were imaged to develop libraries of sharp and blurry images. Filtering with the FlowCAM’s automated pattern recognition feature documented a concentration of 1,029 particles/ml for the sharp images compared to a mere 197 particles/ml for the blurry images. Therefore, the white paper concludes that relying on blurry images results in undercounting of particles while also leading to false positives and false negatives.

The white paper is available free at http://www.fluidimaging.com/imaging-particle-analysis-white-papers.aspx.