Optimization of the Angle Measure Technique for image analytical sampling of particulate matter

Optimization of an industrial image analytical application which uses the Angle Measure Technique (AMT) as an integrated part of estimation of the quality parameter dust level for encapsulated enzyme granules has been carried out. The principal AMT results from this study can be generalized to all similar types of segregating particulate matter. The first part of the study addresses pixel-sampling strategies within one image prior to application of the Angle Measure Technique, where the unfolded image is regarded as a one-dimensional object. Optimal parameters for the AMT algorithm is found to be systematic or stratified sampling of 500+ initiating points, unfolded in a novel spiral- or snake-wise fashion from the fluorescent image; this new unfolding procedure eliminates certain algorithmic “wrap around” artifacts which have not been sufficiently in need of rectification until now. The second part is directed towards delineating optimal image analytical sampling of a series of such images, i.e., optimization of image process sampling. The minimum number of images needed to uphold a sufficiently good online estimate of the QC parameter in question is found to be corresponding to a relatively high sampling rate of 15-20%, related to an intrinsic measurement error level of a mandated analytical reference method. Increasing the number of image increments included in the image analytical sample primarily affects the accuracy of the estimates, whereas precision is much less affected, attesting to a satisfactory stability of the image analytical method developed, which is now under industrial implementation.