Etemadi regression in chemometrics: Reliability-based procedures for modeling and forecasting.

Area of Science:

• Chemometrics
• Chemical analysis
• Process optimization

Background:

• Predictive models in chemical analysis require high generalizability, a challenge for current chemometrics.
• Existing models primarily use accuracy-based strategies, minimizing training data errors.
• Reliability-based approaches, like the Etemadi approach, show promise but are underutilized in chemometrics.

Purpose of the Study:

• To address the gap in chemometric modeling by incorporating reliability into forecasting procedures.
• To propose a novel risk-based modeling strategy that enhances model generalizability and stability.
• To quantify the impact of reliability versus accuracy on generalizability and uncertainty modeling.

Main Methods:

• Development of a general design structure using an optimal reliability-based parameter estimation process.
• Introduction of a risk-based modeling strategy to minimize performance variation across different experimental conditions.
• Empirical evaluation across diverse fields including Pharmacology, Biochemistry, and Geochemistry.

Main Results:

• Reliability-based models demonstrated superior performance over accuracy-based models in 78.95% of evaluated cases.
• Significant improvements observed in Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).
• Statistical analysis confirmed reliability's greater impact on generalizability compared to accuracy.

Conclusions:

• Reliability-based modeling offers a robust alternative to conventional accuracy-based methods in chemometrics.
• The proposed strategy enhances forecast stability and generalizability for chemical laboratory experiments.
• Integrating reliability is crucial for advancing chemometric model performance and uncertainty quantification.