An Efficient Elastic Net with Regression Coefficients Method for Variable Selection of Spectrum Data.

Area of Science:

• Chemometrics
• Data Science
• Spectroscopy

Background:

• Spectrum data quality prediction is challenged by noise and collinearity.
• Effective variable selection is crucial for robust chemometric models.

Purpose of the Study:

• To propose an efficient variable selection method for spectrum data.
• To enhance the stability, feasibility, and interpretability of quality prediction models.
• To mitigate overfitting in spectrum-based predictive modeling.

Main Methods:

• Development of the Elastic Net with Regression Coefficients (Enet-BETA) method.
• Application of elastic net for significant variable selection from spectrum data.
• Integration of hypothesis testing for further model simplification and process insight.

Main Results:

• Enet-BETA effectively selects significant variables, improving model interpretability.
• The method enhances model stability and feasibility compared to traditional approaches.
• Enet-BETA demonstrates superior prediction performance and model interpretability in experimental validation.
• Overfitting is reduced through the elimination of redundant variables.

Conclusions:

• Enet-BETA offers a robust and interpretable approach for spectrum data quality prediction.
• The proposed method addresses key challenges of noise and collinearity in spectral data analysis.
• Enet-BETA provides a valuable tool for improving the accuracy and understanding of predictive models in spectroscopy.