Reevaluating feature importance in machine learning for food authentication: Addressing bias and enhancing methodological rigor

Background: Bhat et al. (2025) highlight the significant role of artificial intelligence (AI) and machine learning (ML) in food authentication through advanced algorithms that analyze large datasets for patterns associated with food fraud. Objective: This paper aims to critically assess the approach of Bhat et al., with a specific focus on model-based feature importance and the biases related to traditional machine learning methods. Methods: The paper distinguishes between machine learning target predictions and feature importances, advocating for the rigorous application of robust statistical techniques, including Spearman's correlation and pvalues, to accurately reveal genuine associations among variables. Results: The analysis emphasizes the necessity for researchers to comprehend the foundational principles of AI and ML to avoid misapplication of these technologies. Conclusion: The paper recommends integrating both nonparametric and nonlinear methods to effectively reduce bias and improve the reliability of feature importance assessments in food authentication.