A Hierarchical Multi-Stage Machine-Learning Study on Predicting Oil and Salt in Chinese Restaurant Dishes for...

The rise in dining out has created a demand for scalable methods for estimating the oil, salt, and sodium content in restaurant meals. These preparation-dependent components are challenging to measure consistently.

A hierarchical multi-stage prediction model was developed to estimate five dish-level targets: added cooking oil, salt, salt-containing seasonings, sodium from salt-containing seasonings, and total sodium from salt and seasonings in 8,455 Chinese dishes from 192 restaurants across six survey sites in China. The model performance was evaluated using standard regression metrics and task-specific indicators, including zero-use identification, tolerance-based accuracy, multiplicative consistency, symmetric mean absolute percentage error (SMAPE), Spearman's rank correlation, and discrete intake-level classification.

The stage-wise light gradient-boosting machine (LightGBM) model outperformed the linear regression, random forest, and multi-task LightGBM models for all five targets. The log-scale coefficient of determination (R²) values ranged from 0.224 for salt to 0.570 for salt-containing seasonings. On the original scale, strict tolerance-based accuracy was limited, with 22%-37% of the predictions meeting the tolerance band. Multiplicative and rank-based performances were stronger: 40% to 73% of the predictions were within two-fold of the observed value, and the Spearman correlations ranged from 0.486 to 0.771. The predicted means were lower than the observed means, indicating a conservative estimation and limited suitability for uncalibrated mean estimation.

The model can support the preliminary estimation, relative comparison, and screening of restaurant dishes for nutrition surveillance. Therefore, it should not be interpreted as a precise dish-level measurement tool without further calibration.