基于微流化加工条件的甘汁质量特征的智能建模
Ayon Tarafdar1,2, Barjinder Pal Kaur1
1Food Engineering Lab, Department of Food Engineering, National Institute of Food Technology Entrepreneurship and Management, Kundli, Sonipat, Haryana 131 028 India.
Journal of food science and technology
|October 14, 2024
概括
人工神经网络 (ANN) 模型在微流体化后准确预测了甘汁的质量. GDX+tansigmoid模型实现了96%的准确性,优化了加工参数以提高果汁特性.
科学领域:
- 食品科学与技术 食品科学与技术
- 生物技术是生物技术.
- 农业工程 农业工程
背景情况:
- 微流体化是一种新兴的加工食品技术,如甘汁.
- 微流化参数对甘汁质量的影响尚未得到广泛研究.
- 预测建模可以优化食品加工,确保产品质量.
研究的目的:
- 探索使用不同的人工神经网络 (ANN) 架构来预测微流化下甘汁的质量.
- 研究不同微流化压力和循环对甘果汁关键特性的影响.
- 确定最佳的ANN模型来预测甘汁的质量属性.
主要方法:
- 采用了各种ANN基础设施,具有不同的隐藏层激活功能 (tansigmoid,logsigmoid) 和学习算法 (LM,GDX).
- 测试了具有不同隐藏层神经元 (HLNs) 的模型,以预测多个质量参数,包括颜色,含量,糖,酶活动,微生物数量,粒子大小,感官得分和沉积率.
- 评估训练和测试数据集的预测准确性,以选择表现最佳的模型.
主要成果:
- 一些ANN模型,包括LM+logsigmoid,GDX+logsigmoid和GDX+tansigmoid,实现了超过90%的预测准确度.
- GDX+tansigmoid模型显示出最高的预测准确性 (91.7%的训练,96%的测试),隐藏层神经元数量较少 (10个HLN).
- 这种模型有效地预测了经过微流体化处理的甘汁的各种质量特征.
结论:
- 在微流体化下,ANN是预测甘汁质量的有效工具.
- 该GDX+tansigmoid ANN模型提供了一种可靠的方法来优化甘汁的微流化参数.
- 这种预测方法可以指导加工条件以达到所需的果汁质量属性.
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