Fermentation modeling and machine learning for flavor prediction in low-sodium radish paocai with potassium chloride substitution.

Journal: NPJ science of food
Published Date: Jul 28, 2025

Abstract

To address the challenge of high sodium in paocai, this study evaluated the partial substitution of NaCl with KCl during radish paocai fermentation, focusing on microbial kinetics and flavor. The methodology integrated microbial growth modeling with comprehensive flavor analysis (HS-SPME-GC-MS, HS-GC-IMS, E-tongue) and Random Forest (RF) machine learning. Substituting 30% NaCl with KCl (K30) significantly increased mannitol and glutamic acid, enhancing desirable fresh, sweet, and umami tastes. RF modeling identified erucin, 1-hexanol, 3-methylbutan-1-ol, and 2-methoxy-4-vinylphenol as potential key aroma compounds. The K30 treatment also improved the aroma profile by increasing volatile compounds associated with cabbage, fruit, and sweet notes. Conclusively, sensory analysis confirmed that K30 paocai exhibited superior sourness, sweetness, umami, and overall acceptability. These findings support that 30% NaCl substitution with KCl is a valuable strategy for producing low-sodium radish paocai with an enhanced flavor profile, offering a practical framework for sodium reduction across traditionally fermented foods.

Authors

  • Yaxin Li
    College of Automotive Engineering, Jilin University, Changchun, People's Republic of China.
  • Yunjing Gu
    School of Food Engineering, Yantai Key Laboratory of Nanoscience and Technology for Prepared Food, Yantai Engineering Research Center of Food Green Processing and Quality Control, Ludong University, Yantai, China.
  • Weiye Cheng
    School of Food Engineering, Yantai Key Laboratory of Nanoscience and Technology for Prepared Food, Yantai Engineering Research Center of Food Green Processing and Quality Control, Ludong University, Yantai, China.
  • Zifan Li
    Key Laboratory of Flexible Electronics (KLOFE) and Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing 211816, China.
  • Xiru Zhang
    School of Food Engineering, Yantai Key Laboratory of Nanoscience and Technology for Prepared Food, Yantai Engineering Research Center of Food Green Processing and Quality Control, Ludong University, Yantai, China.
  • Yaran Zhao
    School of Food Engineering, Yantai Key Laboratory of Nanoscience and Technology for Prepared Food, Yantai Engineering Research Center of Food Green Processing and Quality Control, Ludong University, Yantai, China.
  • Kanghee Ko
    Department of Food Engineering, Mokpo National University, Jeonnam, Republic of Korea.
  • Wenli Liu
    Beijing Center for Physical and Chemical Analysis, Beijing 100094, PR China; Beijing Engineering Technology Research Centre of Gene Sequencing and Gene Function Analysis, Beijing 100094, PR China.
  • Xiaoping Liu
  • Huamin Li
    Applied Mathematics Program, Yale University, New Haven, CT 06511, USA.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals