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通过不同的机器学习模型与遗传算法相结合,对液体烟雾彩虹鱼的多质量属性预测和过程参数优化进行了预测

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

概括

机器学习模型准确地预测了液体烟雾彩虹鱼的质量. 与商业产品相比,优化的加工参数产生了优越的感觉和营养特征,为水生食品制造提供了数据驱动的方法.

科学领域:

食品科学与技术食品科学与技术
机器学习在食品加工中的应用
水产品质量评估水产品质量评估

背景情况:

优化液体吸烟彩虹鱼 (LSRT) 的质量属性对于市场竞争力至关重要.
传统的流程优化方法可能缺乏复杂的质量属性预测所需的精度.
机器学习 (ML) 为食品制造业的先进预测和优化提供了潜力.

研究的目的:

通过使用与遗传算法 (GA) 集成的四种ML模型预测LSRT的关键质量属性.
为了优化LSRT处理参数以增强感官特性,甲酸反应性物质 (TBARS) 和阿斯丁 (AST) 含量.
为智能水生食品制造建立数据驱动的框架.

主要方法:

使用Box-Behnken设计,将工艺参数 (盐度,吸烟液度,温度,时间) 与质量属性联系起来.
包括反向传播人工神经网络 (BP-ANN) 在内的四个ML模型被评估了预测准确性.
使用遗传算法 (GA) 来优化识别的过程参数.

主要成果:

BP-ANN显示出最高的预测准确度 (R2 = 0.953,RMSE = 0.204),超过了传统的响应表面方法.
优化的参数 (盐1.1%,液体0.93‰,温度42°C,时间3.5h) 产生了具有优越感官得分的LSRT,更高的AST和多不和脂肪酸,以及较低的TBARS.
挥发性化合物分析证实了优化的LSRT中平衡的风味特征.

结论:

混合BP-ANN-GA模型有效地预测LSRT质量属性并优化处理参数.
优化的LSRT与商业吸烟的鱼类产品相比,具有更高的质量,这表明市场潜力强.
这项研究为智能水生食品制造和质量控制提供了一个新的数据驱动框架.

关键词:

阿斯塔克桑丁是一种阿斯塔克桑丁.反向传播的人工神经网络遗传算法遗传算法遗传算法液体吸烟的彩虹鱼是什么意思机器学习机器学习质量质量质量质量质量质量质量质量.

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