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提高食品质量分析:人工神经网络在现代分析技术中的变革性作用
Rajni Sharma1, Manisha Agarwal1, Baljinder Singh1
1Department of Biochemistry, School of Basic Sciences, Central University of Punjab, Bathinda, 151401, India.
Critical reviews in analytical chemistry
|May 16, 2025
概括
人工神经网络 (ANN) 为食品质量分析和安全提供先进的解决方案. 将ANN与分析技术相结合,可以提高食品的真实性,优于传统方法.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 人工智能的人工智能
背景情况:
- 消费者对高质量,安全食品的需求推动了对强有力的食品分析和检查的需求.
- 传统的机器学习难以处理复杂,高维的食品数据,限制了真实性和质量评估的准确性.
- 人工神经网络 (ANN) 为解释复杂的食品数据集提供了先进的解决方案.
研究的目的:
- 审查最新的人工神经网络 (ANN) 模型及其对食品质量预测的影响.
- 探索各种分析技术与食品认证的ANN的整合.
- 讨论食品分析中传统化学测量方法的局限性.
主要方法:
- 对食品分析中ANN的最新文献进行系统审查.
- 对食品认证的联合分析技术和ANN应用的分析.
- 评估传统的化学测量方法及其缺点.
主要成果:
- 当ANN与分析技术相结合时,可以有效地应对食品真实性和质量方面的挑战.
- 与传统的化学测量相比,ANN在学习数据特征方面表现出更好的能力.
- 多种ANN类型和便携式光谱仪的融合显示了未来研究的巨大潜力.
结论:
- 网络提供了强大的工具来提高食品质量和安全分析.
- 将ANN与分析技术相结合,为准确的食品认证提供了一个有希望的方法.
- 未来的研究应该专注于开发先进的ANN模型和便携式分析设备.
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