Near-infrared prediction of tannin content in walnut kernels using wavelet transform combined with interpretable machine learning models
The study demonstrates that combining mathematical transformations with wavelet transform can effectively improve the prediction accuracy of models for tannin content in walnut kernels. The RF model based on processed spectral data showed strong performance, indicating its potential for rapid and non-destructive tannin quantification. The use of SHAP algorithm further enhances model interpretability. These findings provide a valuable reference for the accurate prediction of tannin content in walnut kernels and may support quality control in walnut production and processing.
Authors
- Yonghao Chen
- Mian Muhammad Ahmed (ORCID: https://orcid.org/0009-0001-4496-048X)
- Shiwei Wang (ORCID: https://orcid.org/0000-0002-6836-8229)
- Qiang Jin (ORCID: https://orcid.org/0000-0001-8065-3543)
- Zhe Guo (ORCID: https://orcid.org/0000-0002-5713-120X)
- Lin Luo
- Jiangnan Qin
- Liping Chen
- Qiuhao Xia
- Rui Zhang
- Yerhazi Yerzati
Institutions
- Xinjiang Agricultural University (CN)
- Xinjiang Production and Construction Corps (CN)
- Agriculture and Forestry University (NP)
- Tarim University (CN)
- EES Research (United Kingdom) (GB)
- Academy of Agricultural and Forestry Sciences (RO)
- Xinjiang Academy of Forestry (CN)
- Research Institute of Forestry (CN)
- Beijing Academy of Agricultural and Forestry Sciences (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2026-02-06
- DOI
- https://doi.org/10.3389/fpls.2026.1746869
- Primary Topic
- Spectroscopy and Chemometric Analyses
- Type
- article
- Field-Weighted Citation Impact
- 0.00