Conventional automated clinical diagnostic testing systems have various process monitoring functions (e.g., optical sensors, pressure sensors, thermistors). These monitors check the integrity of instrument function, but most of those tools are limited to indirect sensing and do not directly monitor the critical elements of correct assay processing. This study examines the use of a new machine vision tool, PrecisionVision Technology, to directly monitor critical assay processing steps.
Incorporating the new process monitoring includes insertion of imaging technologies with software algorithms. Combinations of placement, backlighting and multiple algorithms were studied to demonstrate capability of direct measurements of sample within pipette tips and reaction steps in the vessels. Four applications were pursued:
- Sample volume monitoring: Image and software algorithms measure the distance from bottom of tip to sample meniscus using pixels and then convert measurement to volume
- Total reaction volume monitoring: Image and software algorithms measure the distance from bottom of vessel to reaction meniscus using pixels and then convert measurement to volume
- Residual volume monitoring: Image and software algorithms execute pattern matching and convert to residual volume
- Particle retention monitoring: Image and software algorithms execute measurement of gray-scale gradient and convert to particle concentration
Summary of accuracy and capability of each of the 4 applications are listed below:
- Sample volume detection range was demonstrated to be 2 to 100 µL with ± 5% accuracy capability
- Reaction volume detection range was demonstrated to be 50 to 250 µL with ± 5% accuracy capability
- Residual volume detection was demonstrated with a minimum volume of 15 µL capability
- Particle retention range of 40-100% retention was demonstrated with ± 5% accuracy capability
This study confirms the performance of PrecisionVision technology for direct measurement of various sample reaction volumes. Proactive and direct assessment will potentially permit future immunoassay systems to notify users of processing errors, permitting earlier detection and resolution and lowering risk that erroneous but believable results will be reported.