Calibration transfer employing univariate correction and robust regression.

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Chemometrics

Background:

• Calibration transfer is crucial for applying analytical models across different instruments.
• Existing methods often struggle with spectral windows or full spectra, limiting their application.
• Targeted instruments monitoring specific variables require efficient calibration transfer solutions.

Purpose of the Study:

• To develop and validate a novel calibration transfer method for isolated spectral variables.
• To enhance the robustness and accuracy of spectral data transfer between instruments.
• To provide a valuable tool for application-targeted spectroscopic instruments.

Main Methods:

• A univariate procedure corrects secondary instrument spectral measurements.
• A robust regression technique builds a model with low sensitivity to correction residuals.
• The method was tested on near-infrared spectrometric determination of gasoline and corn composition.

Main Results:

• The proposed method demonstrated superior calibration transfer performance compared to piecewise direct standardization (PDS).
• Accurate determination of specific mass, research octane number, naphthenes, moisture, and oil was achieved.
• The method proved effective for instruments focusing on limited spectral variables.

Conclusions:

• The new calibration transfer method offers significant advantages for isolated spectral variable analysis.
• It provides a robust and accurate alternative to existing techniques, especially for targeted applications.
• This approach enhances the utility of spectroscopic instruments in diverse analytical fields.