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一个优化的双向长期短期记忆模型,基于对奶粉中蛋白质含量的高光谱分析

Wenjing Zhang^1,2, Heru Xue¹, Xinhua Jiang^1,2

¹College of Computer and Information Engineering Inner Mongolia Agricultural University Saihan District, Hohhot China.

Food science & nutrition

|January 13, 2025

概括

准确检测奶粉蛋白质对于乳制品质量和消费者健康至关重要. 使用超频谱数据和鱼优化算法 (WOA) 的新型深度学习模型显著提高了预测准确性.

科学领域:

* 农业科学农业科学
* 分析化学分析化学
* 数据科学数据科学

背景情况:

* 奶粉蛋白质含量是关键的营养和质量指标.
*目前的检测方法可能很慢,缺乏准确性,影响乳制品行业和消费者健康.
* 需要更快,更准确的方法来分析牛奶蛋白.

研究的目的:

* 开发一种快速而准确的方法来检测奶粉蛋白质含量.
* 探索超光谱数据和深度学习用于非破坏性分析的应用.
* 优化深度学习模型以提高预测性能.

主要方法:

*使用超光谱数据和化学成分作为独立变量.
*数据预处理涉及以平均值为中心 (MC) 方法.
*特征波长选择使用竞争性自适应重量化采样 (CARS) 和非信息变量消除 (UVE).
*采用了双向长期短期记忆 (BiLSTM) 网络与注意力机制.
* 用鱼优化算法 (WOA) 来进行超参数优化.

主要成果:

* WOA-BiLSTM-Attention模型实现了0.9975的确定系数 (R2) 和0.0337.7的根平均平方预测误差 (RMSEP).
*与BiLSTM-Attention模型相比,这表示R2增加了0.799%,RMSEP下降了56.5%.
*优化模型在预测奶粉蛋白质含量方面表现出高准确性.

结论:

* 开发的WOA-BiLSTM-Attention模型提供了一种可靠和准确的方法,用于确定奶粉中的非破坏性蛋白质含量.
*这种深度学习方法比现有方法提供了显著的改进,支持乳制品行业.
* 该研究为在食品质量分析中应用先进算法奠定了理论基础.

关键词:

在BiLSTM-Attention模型中.优化 WOA 的优化.深度学习是一种深度学习.非破坏性测试是指非破坏性测试.蛋白质含量蛋白质含量蛋白质含量频谱分析是一种分析.

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