融合H NMR和拉曼实验数据,以改进葡萄酒识别模型
Ariana Raluca Hategan1, Maria David1, Adrian Pirnau2
1National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania; Faculty of Physics, Babeș-Bolyai University, Kogălniceanu 1, 400084 Cluj-Napoca, Romania.
Food chemistry
|July 2, 2024
概括
人工智能 (AI) 模型使用融合的1H NMR和拉曼数据准确地按品种,年份和来源分类葡萄酒. 这种采用支持矢量机器 (SVM) 的人工智能方法,证明优于单个数据源用于葡萄酒识别.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 人工智能的人工智能
背景情况:
- 葡萄酒认证对于质量控制和预防欺诈至关重要.
- 像1H NMR和拉曼等光谱技术提供了丰富的化学信息.
- 整合多个数据源可以提高分析模型的性能.
研究的目的:
- 使用人工智能 (AI) 开发先进的葡萄酒识别模型.
- 为了评估1H NMR和拉曼光谱的中级数据融合的有效性.
- 根据品种,年份和地理来源对葡萄酒样本进行分类.
主要方法:
- 应用监督机器学习,特别是支持矢量机器 (SVM).
- 利用特征选择算法来管理高维的融合数据.
- 采用了1H NMR和拉曼光谱数据的中级数据融合.
主要成果:
- 通过交叉验证和独立的测试集实现了高达100%的分类准确性.
- 证明了1H NMR和拉曼数据相比单个来源数据的优越性.
- 确定了葡萄酒分类标准的关键区分标记.
结论:
- 1H NMR和拉曼光谱与人工智能的数据融合为葡萄酒认证提供了一个强大的工具.
- 开发的SVM模型有效地根据品种,年份和原产地区分葡萄酒.
- 这种人工智能驱动的方法提高了葡萄酒识别的准确性和可靠性.
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