Somatic cell count (SCC) is a critical indicator of milk quality and udder health. Accurately evaluating the performance of a somatic cell counter ensures reliable results in dairy diagnostics. This guide outlines the core performance metrics and practical evaluation methods for somatic cell counters.

1. Core Performance Indicators

1.1 Repeatability and How to Evaluate It

Repeatability refers to the consistency of results when the same sample is tested multiple times. A high-quality somatic cell counter should produce minimal variation across repeated measurements. Our instruments achieve:

• CV (Coefficient of Variation) ≤ 5% for samples at 500,000 cells/mL
• CV ≤ 7% for samples at 200,000 cells/mL

Note: Because somatic cells are not perfectly uniformly distributed in milk, occasional outliers may occur, but overall performance should consistently meet the above thresholds.

Test Protocol

1. Use 5 detection chips to test the same sample tube.
2. Run 4 replicates per chip — 20 replicates total.
3. Calculate the mean and CV across all 20 measurements.

Key operating notes: Warm the sample to 40°C to dissolve fat globules, then vortex or shake thoroughly before testing to ensure homogeneity.

Raw data recording and auto-calculation templates are available in the accompanying Excel attachment.

1.2 Accuracy and How to Evaluate It

Per international standards, accuracy is assessed by comparing results against a reference method. While microscopic cell counting is the gold standard, it is technically demanding. In practice, comparison against flow cytometry instruments from FOSS or Bentley — which offer high repeatability due to larger sample volumes — is the preferred approach.

Key Considerations for Comparative Testing

1. Sample stability: Minimize the time interval between measurements on the reference instrument and the instrument under evaluation.
2. Sample preparation: Warm samples to 40°C to melt fat, then vortex to homogenize before sampling. This reduces variability caused by fat layering and uneven cell distribution.
3. Concentration range coverage: Use samples spanning a wide range of SCC concentrations. For bovine milk, include at least 10 samples ranging from 0 to 2,000,000 cells/mL.

Perform linear regression between the test instrument readings and the reference instrument readings. Evaluate:

• Correlation coefficient (R²): The closer to 1, the higher the accuracy.
• Slope of the regression line: Reflects systematic bias, which can be corrected through calibration.

1.3 Evaluating Accuracy Without a Reference Instrument

Not every user has access to a FOSS or Bentley analyzer. In this case, a dilution linearity method can be used to assess instrument performance:

1. Prepare a high-count sample (e.g., from a mastitic cow or goat) and a low-count sample (e.g., from a healthy animal or commercially homogenized milk with low SCC).
2. Mix the two samples at the following volume ratios to create a concentration gradient:

Perform linear regression between the measured values and the theoretical relative concentrations. The correlation coefficient should be ≥ 0.98 — the closer to 1, the better the linearity and accuracy.

2. Additional Performance Considerations

2.1 Imaging Quality

Our somatic cell counters use direct microscopic imaging for cell counting. Raw images can be reviewed on-device or exported to a computer for inspection. A reliable count can be confirmed when:

• Staining is clear and well-defined.
• Cell counting markers are correctly placed.

This visual verification provides an additional layer of confidence — seeing is believing.

2.2 Inter-Instrument Consistency

For more rigorous quality assurance, inter-instrument consistency should be evaluated. The same sample or counting slide should yield consistent results across different units of the same instrument model.

It is worth noting that even industry-leading instruments such as FOSS and Bentley exhibit inter-instrument variation — sometimes differing by up to 10% overall, with larger discrepancies at high SCC values. This is a recognized challenge across the industry.

3. Appendix

A Performance Evaluation Excel Template is available to support data recording and automated calculation of CV, linear regression, and correlation coefficients.

📩 Request the Free Performance Evaluation Excel Template

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