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Updated: Jan 25, 2026

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Interpretable Machine Learning Integrates Flavor Classification with Source Attribution for Low-Salt Soy Sauce

Xinyun Zhou1, Ting Guo1, Tongquan Li2

  • 1State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Food Nutrition and Safety, Ministry of Education, College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin 300457, China.

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|January 23, 2026
PubMed
Summary
This summary is machine-generated.

Developing low-salt soy sauce (LSS) with enhanced flavor is possible. An integrated triomics model identified key volatiles and microbes, guiding the creation of a healthier LSS with desirable sensory profiles.

Keywords:
MMVecXGBoostlow-salt soy saucemachine learningsensory–volatiles–microbiota map

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Area of Science:

  • Food Science
  • Fermentation Technology
  • Computational Biology

Background:

  • Low-salt soy sauce (LSS) offers health benefits but often lacks the complex flavor of traditional high-salt soy sauce (HSS) and koikuchi soy sauce (KSS).
  • Flavor defects in LSS limit consumer acceptance and market potential.
  • Targeted flavor enhancement is crucial for improving LSS quality.

Purpose of the Study:

  • To develop an integrated triomics model for guiding the flavor enhancement of LSS.
  • To identify key volatile compounds and microbial communities responsible for soy sauce flavor profiles.
  • To create an optimized LSS with improved sensory characteristics.

Main Methods:

  • Integration of sensory analysis, volatile compound analysis (GC-MS/GC-O), and microbiota profiling (16S rRNA sequencing).
  • Development and application of an XGBoost-SHAP-MMVec computational model to link sensory attributes, volatiles, and microbes.
  • Fermentation trials using a defined microbial consortium for LSS optimization.

Main Results:

  • Identification of 29 odor-active volatiles contributing to six key sensory attributes (sauce, rancid, fruit, roast, flower, grain).
  • The XGBoost-SHAP model accurately predicted flavor contributors (>0.90 accuracy).
  • A five-genus microbial consortium (Lactiplantibacillus, Staphylococcus, Bacillus, Zygosaccharomyces, Aspergillus) was identified and validated for flavor profile generation.
  • Model-guided optimization resulted in an enhanced LSS with retained desirable aromas and incorporated HSS/KSS-like notes.

Conclusions:

  • The integrated triomics approach and computational model provide a powerful strategy for targeted flavor enhancement in fermented foods like LSS.
  • Understanding the interplay between microbiota and volatiles is key to overcoming flavor challenges in reduced-salt products.
  • This research enables the development of healthier fermented foods without compromising sensory quality.