A Model-Based Temperature-Prediction Method by Temperature-Induced Spectral Variation and Correction of the...

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Chemometrics

Background:

• Temperature fluctuations can introduce nonlinear effects in spectral data, complicating analysis.
• Existing methods like continuous piecewise direct standardization (CPDS) have limitations in handling these variations.
• Accurate spectral correction is crucial for reliable quantitative analysis in various chemical applications.

Purpose of the Study:

• To propose a new model-based method for predicting and correcting temperature-induced spectral variations.
• To reduce the nonlinear effects of temperature on spectral data.
• To develop a more user-friendly method for spectral correction in real-world applications.

Main Methods:

• A temperature prediction model was developed using training samples.
• The developed model was used to predict the temperatures of test samples.
• A subsequent correction model was applied to mitigate nonlinear spectral effects attributed to temperature variations.

Main Results:

• The proposed method demonstrated efficiency in temperature correction for spectral data.
• Experimental validation was performed using a water-ethanol mixture and a ternary mixture.
• The method showed superior performance compared to the continuous piecewise direct standardization (CPDS) method.

Conclusions:

• The proposed model-based method is effective for temperature prediction and spectral correction.
• The method eliminates the need for prior knowledge of test sample temperatures, enhancing practical usability.
• This technique offers a more efficient and accessible solution for managing temperature-related spectral interferences.