[Calibration transfer without standards for spectral analysis based on stability competitive adaptive reweighted...

Area of Science:

• Analytical Chemistry
• Spectroscopy

Context:

• Calibration transfer is crucial for applying analytical methods across different instruments.
• Near-infrared (NIR) spectroscopy is widely used but susceptible to variations in measurement parameters and instrument differences.
• Accurate calibration transfer is essential for reliable data analysis in diverse settings.

Purpose:

• To introduce a novel calibration transfer method, Stability Competitive Adaptive Reweighted Sampling (SCARS).
• To improve the accuracy and stability of calibration transfer in near-infrared (NIR) analysis.
• To address challenges posed by instrument variability and measurement parameter influences.

Summary:

• The proposed SCARS method utilizes a stability index (absolute regression coefficient divided by its standard deviation) to select important and robust wavelength variables.
• It effectively reduces or eliminates differences in spectral responses between instruments or measurement conditions.
• Spectral data compression is incorporated to enhance the stability and efficiency of the calibration transfer process.
• The method was validated using NIR analysis of corn samples analyzed on different spectrometers.

Impact:

• SCARS demonstrated superior analytical accuracy compared to other methods like OSC, MCUVE, and CARS for NIR calibration transfer.
• The technique successfully corrects for inter-instrument variations, leading to improved analytical performance.
• Effective spectroscopic data compression simplifies and optimizes the calibration transfer workflow.
• This advancement has the potential to enhance the reliability and applicability of NIR spectroscopy in various industrial and research applications.