When Should a SCIO Sensor Bias Prediction be Done?

What is a SCIO Calibration File?

The SCIO NIR system uses calibration models developed by SCIO through the collection of grain samples from multiple global partners. These models are developed from NIR spectral data and validated against laboratory reference measurements and established NIR standards.   The calibration models are deployed to the system as a calibration file, which is stored directly on each tablet and is uniquely matched to its corresponding SCIO NIR sensor.

Factory calibrations files are currently available for wheat, barley, corn, soybeans, and canola.

Calibration adjustment is generally not required. However, a bias correction or offset adjustment may be applied when aligning SCIO NIR predictions with those from a known reference NIR instrument or when minimizing systematic differences between multiple SCIO NIR sensors.

What is SCIO Bias Prediction?

Bias Prediction applies a bias correction to the SCIO NIR sensor's predictions, so they more closely match results from trusted reference instruments, such as benchtop NIR analyzers, or from reference analytical measurements. This compensates for systematic instrument differences and is used when alignment with a known NIR standard or reference method is required.

Why might you need to adjust a Bias Prediction?

• Reason 1: Match Multiple Sensors
  When using several SCIO sensors, applying a bias correction helps improve consistency among devices
• Reason 2: Match Benchtop or Reference analytical measurements
  When field measurements need to align with a laboratory-grade or reference NIR instrument, a bias prediction can be applied so that SCIO predictions more closely match the reference instrument's results.
• Reason 3: Sensor Replacement or Upgrade
  If a sensor is replaced, these steps help ensure consistent results and continuity with previously collected data.

How often should the Bias adjustment be done?

• At the start of a new research season.
• When sensors are newly deployed or after repairs.

What are the benefits of adjusting the Bias Prediction?

• Ensures accurate and consistent NIR predictions.
• Improves alignment with laboratory and reference NIR measurements.
• Enables uniformity across devices, operators, and time periods.
• Supports continuity of historical data when sensors are replaced.
• Allows comparable and pooled datasets across multiple SCIO NIR sensors.
• Provides confidence in research-quality and operational results.

Need step-by-step instructions?
Refer to the Mirus H3 User Guide
SCIO Calibration and Offset Recalibration Instructions (link to manual section)