Addressing Bias in machine learning feature importance for food quality assessment

Li et al. (2025) highlighted Random Forest's (RF) high accuracy and SHapley Additive exPlanations (SHAP)-derived feature importance for almond deterioration. However, concerns persist regarding the reliability of these interpretations, as high predictive accuracy doesn't guarantee valid feature rankings due to inherent biases in tree-based models, further amplified by SHAP's model dependency. To mitigate this, integrating robust statistical methods such as Spearman's rho, Kendall's tau, Total correlation and Effective transfer entropy is crucial for unbiased assessment. This combined approach ensures a more reliable evaluation of key indicators. Future research should prioritize methodologies combining machine learning with rigorous statistical validation for more interpretable and trustworthy insights in complex biological systems. This integrated approach holds significant promise for improving the reliability of feature importance evaluations, leading to more trustworthy insights applicable to food science and chemistry fields.