基于拉曼光谱,化学测量和机器学习的乳制品定量识别技术
Zheng-Yong Zhang1, Jian-Sheng Su1, Huan-Ming Xiong2
1School of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China.
Molecules (Basel, Switzerland)
|January 25, 2025
概括
拉曼光谱技术使用光谱数据和预处理技术快速推进乳制品分析. 机器学习和深度学习等先进的方法提供了改善的定量分析和乳制品样本的歧视.
科学领域:
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
- 频谱学是一种光谱学.
背景情况:
- 对于乳制品表征和定量分析的拉曼光谱技术已经迅速发展.
- 现在的光谱数据包括传统的拉曼光谱和二维相关性光谱,提供了丰富的组成信息.
研究的目的:
- 审查和总结乳制品分析拉曼光谱的最新进展.
- 讨论各种光谱预处理和定量评估方法.
- 突出该领域新出现的趋势和挑战.
主要方法:
- 使用传统和二维相关性拉曼光谱进行分析.
- 应用光谱预处理技术,如规范化,波纹消噪和特征提取.
- 采用定量评估方法,包括相似度测量和机器学习算法.
主要成果:
- 预处理方法与定量技术相结合,提高了样本分化和预测准确度.
- 类似性测量对于小样本批次的定量歧视是有效的.
- 机器学习算法,特别是深度学习,显示出对具有足够数据的智能歧视的承诺.
结论:
- 拉曼光谱为乳制品分析提供了强大的工具,数据处理和算法的不断进步.
- 深度学习和数据融合等先进技术的整合为未来的机遇和挑战带来了挑战.
- 需要进一步的研究,以充分利用这些技术进行全面的乳制品分析.
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