Curve prediction in automated analyses in routine laboratories

Area of Science:

• Clinical Chemistry
• Analytical Chemistry
• Biomedical Engineering

Background:

• First- and second-generation AutoAnalyzers exhibit hydraulic-induced signal lag and exponential deformations.
• These hydraulic factors limit achievable sampling rates due to sample interaction.
• Accurate signal processing is crucial for reliable automated clinical analysis.

Purpose of the Study:

• To digitally compensate for exponential signal deformations in AutoAnalyzer channels.
• To increase the sampling rate of automated clinical chemistry analyses.
• To evaluate the effectiveness of digital curve regeneration in routine laboratory settings.

Main Methods:

• Utilized a digital approach with a Hewlett-Packard 2100A computer for curve regeneration.
• Applied the method to Technicon Flame IV modules.
• Compensated for exponential deformation in sodium, potassium, chloride, and carbon dioxide channels.

Main Results:

• Successfully compensated for exponential signal deformation over one year of routine laboratory use.
• Achieved a sampling rate of 138 samples per hour, with potential for higher rates.
• Demonstrated reduced sample and reagent consumption as a secondary benefit.

Conclusions:

• Digital curve regeneration effectively corrects hydraulic-induced signal deformations in AutoAnalyzers.
• This technique significantly enhances analysis rates and operational efficiency.
• The method offers practical benefits including reduced reagent and sample usage in clinical laboratories.