乙醇度的确定在Baijiu通过图表规范的PCA和随机基于森林的拉曼光谱
Zhenhao Chen1, Zhuangwei Shi2, Jianchen Zi2
1Academy for Engineering and Technology, Fudan University, No. 220 Handan Rd., Shanghai 200433, China.
这项研究引入了一种新的机器学习方法,使用拉曼光谱来快速准确地测量江的乙醇度. 该方法通过提高数据的准确性和稳定性来加强饮料行业的质量控制.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 传统的中国烈酒Baijiu具有重要的文化和经济价值.
- 精确的乙醇度测定对于baijiu质量控制至关重要.
- 目前的拉曼光谱法在生物化学分析的准确性和稳定性方面存在局限性.
研究的目的:
- 开发一种先进的方法,使用拉曼光谱来精确地确定baijiu中的乙醇度.
- 增强用于光谱数据分析的化学测量工具的灵活性和稳定性.
- 为了改善baijiu制造业的工业质量控制过程.
主要方法:
- 整合图形规则化主要组件分析 (图形规则化PCA) 以减少维度和数据稳定.
- 应用随机森林集合学习框架,以改善光谱数据表示.
- 开发一个单一的回归模型协议,使用多种类型的乙醇度训练套件进行多种类型的baijiu分析.
主要成果:
- 拟议的方法实现了0.415%的平均平均百分比误差 (MAPE) 在三种类型的baijiu.u.中确定乙醇度.
- 合并的图形规范化PCA和随机森林方法显著降低了光谱数据的不稳定性.
- 与现有方法相比,开发的协议显示出更高的准确性和稳定性.
结论:
- 这种新方法提供了一个非常准确和强大的解决方案,用于在baijiu.u.中测量乙醇度.
- 这种技术具有显著的潜力,可以促进白行业的质量控制.
- 这些发现验证了将先进的化学测量和机器学习技术用于光谱分析的有效性.
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