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基于人工神经网络的应用,预测炸面团扭曲中的烯胺含量
Xinyu Wu1, Haiyang Yan1, Yue Cao1
1College of Food Science and Engineering, Jilin University, Changchun, China.
Food chemistry: X
|December 5, 2024
概括
这项研究使用人工神经网络 (ANN) 引入了炸面团扭曲中的烯胺的预测模型. 该模型根据油炸参数准确估计烯胺含量,为传统检测方法提供更快的替代方案.
科学领域:
- 食品化学 食品化学
- 分析化学 分析化学
- 计算化学计算化学
背景情况:
- 热加工食品中烯胺的形成,如油炸面团扭曲是一个重大问题.
- 传统的检测方法 (LC-MS,HPLC) 是资源密集型的,需要替代方法.
研究的目的:
- 开发和验证一个预测模型,用于炸面团扭曲中的烯胺含量.
- 利用由遗传算法优化的人工神经网络 (ANN) 来进行烯胺预测.
主要方法:
- 建立了一个反向传播的人工神经网络 (BP-ANN) 模型.
- 使用多种群遗传算法优化了BP-ANN参数.
- 输入变量包括油炸温度,酸值和颜色差异;输出是烯胺含量.
主要成果:
- 烯胺含量与温度有显著的相关性 (P < 0.01),与酸值和颜色差异有中等的相关性 (P < 0.05).
- 优化的BP-ANN模型使用了Levenberg-Marquardt算法,具有5个隐藏的神经元和0.005的学习率,表现出强大的预测性能.
- 测试和验证组的相关系数 (R) 分别为0.9640和0.8999,表明了良好的适配和近似能力.
结论:
- 开发的BP-ANN模型准确地预测了炸面团扭曲中的烯胺含量.
- 这种计算方法为检测烯胺的传统分析方法提供了一个有希望的,高效的替代方案.
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