深度学习增强的感应合等离子体原子发射光谱仪用于绿茶来源和等级的多变量认证
Yiqing Sun1, Xiaocheng Song1, Siyuan Yang1
1Institute of Eco-Environmental Forensics, School of Environmental Science and Engineering, Shandong University, Qingdao 266237, China.
Food research international (Ottawa, Ont.)
|September 9, 2025
概括
这项研究引入了一种使用金属元素和反向传播神经网络 (BPNN) 的新方法,同时识别绿茶的来源和品质. 这种方法提供了一种可靠的方式来验证有价值的茶叶产品的真实性.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 食品科学 食品科学 食品科学
背景情况:
- 绿茶的市场价值在很大程度上受其来源和品质的影响.
- 同时验证绿茶的原产地和品级是一个重大挑战.
- 准确的身份验证对于消费者信任和公平贸易至关重要.
研究的目的:
- 开发一种可靠的方法,同时识别绿茶的原产地和品级.
- 为高价值绿茶产品建立可靠的认证策略.
- 为了比较不同分析和机器学习技术的有效性.
主要方法:
- 诱导合等离子体原子发射光谱法 (ICP-AES) 用于绿茶样品的多元件分析.
- 逆向传播神经网络 (BPNN) 用于同时分类茶叶的来源和等级.
- 沙普利添加式解释 (SHAP) 用于模型解释和与主要组件分析 (PCA) 的比较.
主要成果:
- BPNN在原产地识别方面获得了92.59%的准确性,在等级分类方面达到88.89%的准确性.
- 在分类茶叶等级方面,SHAP分析表明PCA的表现优于PCA.
- 结合的ICP-AES和BPNN策略在同时身份验证方面被证明是有效的.
结论:
- 拟议的方法提供了一个简单,可靠和准确的方法来验证绿茶的真实性.
- 这种技术可以帮助确保绿茶市场的质量和完整性.
- 该研究强调了将元素分析与先进的机器学习集成为食品认证的潜力.
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