酒精饮料的地理和品种原产地差异化通过FT-拉曼光谱和先进的数据处理策略之间的关联
Ariana Raluca Hategan1,2, Maria David1,2, Camelia Berghian-Grosan1
1National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania.
Food chemistry: X
|December 25, 2023
概括
FT-拉曼光谱学有效地使用先进的统计和机器学习模型来区分水果烈酒. 该技术准确地识别了罗马尼亚蒸的植物和地理来源,准确度超过90%.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 频谱学是一种光谱学.
背景情况:
- 水果烈酒需要可靠的方法来验证产地真实性.
- 光谱技术为快速和非破坏性分析提供了潜力.
- 根据原料和来源区分蒸对于质量控制和打击欺诈至关重要.
研究的目的:
- 评估FT-拉曼光谱法在分辨果酒精中的有效性.
- 开发和比较使用部分最小平方差分分析 (PLS-DA) 和支持向量机器 (SVM) 的差异化模型.
- 评估模型根据植物和地理来源分类蒸物的能力.
主要方法:
- 在86个罗马尼亚蒸样本上使用FT-拉曼光谱学.
- 应用了八种预处理技术和特征选择来优化数据.
- 使用PLS-DA和SVM开发并验证了分类模型.
主要成果:
- 无论是PLS-DA还是SVM模型,都实现了超过90%的分类准确度.
- 这些模型成功地根据原材料 (梅子,果,梨,葡萄) 和原产地区分了蒸.
- 预处理和特征选择增强了光谱数据的歧视力.
结论:
- FT-拉曼光谱与化学测量方法相结合,是鉴定果酒精品质的高效工具.
- 开发的模型为确定蒸品的植物和地理来源提供了一个强大的框架.
- 这种方法为水果烈酒行业的质量保证提供了有价值的解决方案.
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