通过瓶子和机器学习建模,通过非侵入性光谱学提高酒的真实性,质量和控制评估
Natalie Harris1, Claudia Gonzalez Viejo1, Jiaying Zhang1
1Digital Agriculture, Food and Wine Research Group, School of Agriculture, Food and Ecosystem Science, Faculty of Science, The University of Melbourne, Melbourne, Victoria, Australia.
Journal of food science
|January 20, 2025
概括
这项研究引入了一种新的方法,使用近红外光谱和机器学习来验证酒的真实性,并通过瓶子评估其质量,打击欺诈和确保来源.
科学领域:
- 食品科学与技术 食品科学与技术
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 由于假冒和造,酒精饮料欺诈的经济影响日益增加.
- 饮料行业需要使用非破坏性分析方法进行质量控制和认证.
研究的目的:
- 开发一种使用近红外 (NIR) 光谱和机器学习 (ML) 的方法,通过瓶子进行酒认证和质量评估.
- 为了预测发酵类型,感官描述符和酒中的挥发性芳香化合物.
- 为酒商和零售商提供一个工具,以确保质量和防止欺诈.
主要方法:
- 利用近红外 (NIR) 光谱学 (1596-2396nm) 对25个商业酒样本进行了非破坏性分析.
- 使用经过培训的小组成员的定量描述性分析和气色谱-质谱学 (GC-MS) 来获得基准真相数据.
- 开发了使用NIR吸收值来预测酒特性的人工神经网络 (ANN) 模型.
主要成果:
- 在预测发酵类型 (99%),感官描述 (R=0.92) 和挥发性化合物 (R=0.94) 中获得了高精度.
- 模型在新样本上部署时表现出强大的性能 (95%的发酵类型,R=0.83的感觉描述器).
- 该方法允许通过瓶子进行分析,保持样本的完整性.
结论:
- 接近红外光谱与机器学习建模相结合,为酒的真实性和质量评估提供了快速,准确和非破坏性的方法.
- 这项技术可以帮助打击欺诈,确保来源,并在运输和储存期间监控质量.
- 扩展潜力通过进一步的模型培训来评估额外的质量特征,如物理化学参数和起源.
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