异常值的拒绝和显著的解释变量选择对于皮诺特黑葡萄酒软传感器开发的重要性
Jingxian An1, David I Wilson2, Rebecca C Deed1
1The University of Auckland, New Zealand.
Current research in food science
|May 30, 2023
概括
这项研究简化了葡萄酒质量控制,将必要的化学分析从12个关键参数减少到四个关键参数. 这大大减少了成本和时间,使先进的感官分析成为常规行业使用的实用.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 葡萄酒学 葡萄酒学
背景情况:
- 葡萄酒的质量严重依赖于感官属性,这给客观量化带来了挑战.
- 目前用于葡萄酒感官分析的软传感器模型需要许多化学参数 (≥12),由于成本和时间限制实际应用.
研究的目的:
- 开发用于葡萄酒质量评估的简化软传感器模型,使用减少的一组化学参数.
- 显著降低与葡萄酒感官分析相关的分析成本和劳动力.
主要方法:
- 利用诸如Box图,Tucker-1图和主要组件分析 (PCA) 等统计工具进行数据可视化和模型改进.
- 开发回归和分类模型,以使用最小数量的化学输入来预测多个感官属性.
主要成果:
- 为预测35个感官属性 (R2 > 0.6) 的回归模型确定了四个关键化学参数 (总醇,总宁,A520nmHCl和pH).
- 确定了四个关键化学参数 (A280nmHCl,A520nmHCl,化学年龄和pH) 用于预测35种感官属性 (准确度>70%) 的分类模型.
- 实现了显著的成本降低:56%的回归和83%的分类模型.
结论:
- 减少的化学参数集使得精确且具有成本效益的葡萄酒的感官质量映射成为可能.
- 开发的软传感器模型适用于葡萄酒行业的常规质量控制.
- 这种方法增强了先进的分析技术在酒中的实际应用.
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