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Multi-quality attributes prediction and process parameter optimization of liquid-smoked rainbow trout by different
Junpeng Zeng1, Jingyi Luo1, Yu Song1
1State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, No. 1299, Sansha Road, Qingdao, Shandong Province 266404, China.
Food Research International (Ottawa, Ont.)
|November 4, 2025
Summary
Machine learning models accurately predicted liquid-smoked rainbow trout quality. Optimized processing parameters yielded superior sensory and nutritional profiles compared to commercial products, offering a data-driven approach for aquatic food manufacturing.
Area of Science:
- Food Science and Technology
- Machine Learning Applications in Food Processing
- Aquatic Product Quality Assessment
Background:
- Optimizing liquid-smoked rainbow trout (LSRT) quality attributes is crucial for market competitiveness.
- Traditional methods for process optimization may lack the precision required for complex quality attribute prediction.
- Machine learning (ML) offers potential for advanced prediction and optimization in food manufacturing.
Purpose of the Study:
- To predict key quality attributes of LSRT using four ML models integrated with a genetic algorithm (GA).
- To optimize LSRT processing parameters for enhanced sensory properties, thiobarbituric acid reactive substances (TBARS), and astaxanthin (AST) content.
- To establish a data-driven framework for intelligent aquatic food manufacturing.
Main Methods:
- A Box-Behnken design was employed to link process parameters (salt concentration, smoking liquid concentration, temperature, time) with quality attributes.
- Four ML models, including back-propagation artificial neural network (BP-ANN), were evaluated for predictive accuracy.
- A genetic algorithm (GA) was utilized to optimize the identified process parameters.
Main Results:
- BP-ANN demonstrated the highest predictive accuracy (R² = 0.953, RMSE = 0.204), outperforming traditional response surface methodology.
- Optimized parameters (salt 1.1%, liquid 0.93‰, temp 42°C, time 3.5h) yielded LSRT with superior sensory scores, higher AST, and polyunsaturated fatty acids, and lower TBARS.
- Volatile compound analysis confirmed a balanced flavor profile in the optimized LSRT.
Conclusions:
- The hybrid BP-ANN-GA model effectively predicts LSRT quality attributes and optimizes processing parameters.
- Optimized LSRT exhibits enhanced quality compared to commercial smoked Salmonidae products, indicating strong market potential.
- This study provides a novel, data-driven framework for intelligent aquatic food manufacturing and quality control.
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