Calibration Graph
Presented by HIRA
Calibration Graph
A calibration curve is used to determine the concentration of
an unknown sample, to calculate the limit of detection, and the
limit of quantitation. The curve is created from the
instrumental response to a set of standard samples at a range
of concentrations.
Purpose of Calibration Graphs
•Verification of Instrument Accuracy: Ensures that the testing equipment provides precise and
repeatable results.
•Data Correlation: Links raw instrument output (e.g., voltages or counts) to standardized physical
units (e.g., N/mm² for stress or microstrain for deformation).
•Correction of Errors: Identifies and corrects systematic errors in measurements.
Components of Calibration Graph
•X-Axis (Independent Variable): Known or standard reference values (e.g., weights for force
calibration, or strain gauge outputs for deformation).
•Y-Axis (Dependent Variable): Instrument readings or measured outputs.
•Calibration Curve: The relationship plotted between the X and Y axes, typically linear but could
be nonlinear for some instruments.
Steps to Create a Calibration Graph
(a) Selection of Reference Standards
•Use traceable reference standards (e.g., NIST-certified weights for force calibration).
•Ensure the standards are more accurate than the instrument being calibrated.
(b) Data Collection
•Apply known inputs to the testing instrument (e.g., known weights or strain levels).
•Record the corresponding instrument output readings.
(c) Plotting the Data
•Plot the reference values (X-axis) against the instrument readings (Y-axis).
•Use software or manual graphing methods to visualize the relationship.
(d) Curve Fitting
•Fit a straight line (or polynomial curve for nonlinear systems) through the data points.
•The equation of the curve (e.g., y=mx+cy = mx + cy=mx+c) represents the calibration relationship.
(e) Calculation of Errors
•Compute deviations between the measured values and the reference values.
•Determine metrics like root mean square error (RMSE) or standard deviation.
Applications of Calibration Graphs
•Materials Testing:
• Calibrating load cells in tensile testing machines.
• Converting strain gauge readings to strain values.
•Analytical Chemistry:
• Determining unknown concentrations in spectrophotometry.
•Environmental Monitoring:
• Calibrating sensors for air quality, temperature, or humidity.
•Medical Devices:
• Blood pressure monitors or glucose sensors.
Advantages of calibration graphs:
•Visual Representation: They provide a clear visual representation of how
well a measurement or prediction model aligns with actual values.
•Accuracy Assessment: Calibration graphs help in evaluating the accuracy
and reliability of measurements or predictions by comparing observed
values with expected values.
•Quality Control: They are useful in quality control processes to ensure
that instruments or models are calibrated correctly and produce reliable
results.
•Decision Making: Calibration graphs aid in decision-making processes by
providing insights into the precision and potential biases of measurement or
prediction systems.
