Optuna-optimized stacking models for predicting total phenolic content in NFC compound juices based on routine physicochemical attributes: Interpretability analysis via SHAP
- DOI
- 10.1016/j.foodchem.2026.150904
- Published
- 2026-10
- Container
- Food Chemistry
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.foodchem.2026.150904,
title = {Optuna-optimized stacking models for predicting total phenolic content in NFC compound juices based on routine physicochemical attributes: Interpretability analysis via SHAP},
author = {Fangchen Ding and Rili Zha and Juan Francisco García-Martín and Weijie Lan},
year = {2026},
journal = {Food Chemistry},
doi = {10.1016/j.foodchem.2026.150904},
url = {https://doi.org/10.1016/j.foodchem.2026.150904}
}RIS
TY - JOUR TI - Optuna-optimized stacking models for predicting total phenolic content in NFC compound juices based on routine physicochemical attributes: Interpretability analysis via SHAP AU - Fangchen Ding AU - Rili Zha AU - Juan Francisco García-Martín AU - Weijie Lan PY - 2026 JO - Food Chemistry DO - 10.1016/j.foodchem.2026.150904 UR - https://doi.org/10.1016/j.foodchem.2026.150904 ER -
APA
Ding, F., Zha, R., García-Martín, J. F., & Lan, W. (2026). Optuna-optimized stacking models for predicting total phenolic content in NFC compound juices based on routine physicochemical attributes: Interpretability analysis via SHAP. Food Chemistry. https://doi.org/10.1016/j.foodchem.2026.150904
Source records
- crossref · retrieved 2026-09-26T08:07:44.546Z