质谱成像用于植物性食品的空间成分分类
Mudita Vats1, Bryn Flinders1, Theodoros Visvikis1
1Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands.
Journal of the American Society for Mass Spectrometry
|December 7, 2024
概括
质谱成像 (MSI) 和机器学习模型可以映射汉堡的成分. 这项技术可以检测肉类改,并区分汉堡类型,确保食品质量和消费者的安全.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 生物技术是生物技术.
背景情况:
- 质谱成像 (MSI) 可以生成复杂样品的分子图.
- 汉堡饼干,无论是肉类还是植物性,都是复杂的矩阵,需要详细分析.
- 了解成分分布是质量控制和消费者满意度的关键.
研究的目的:
- 开发和应用MSI技术进行汉堡分析.
- 利用机器学习来分类汉堡的成分和检测伪造.
- 区分各种汉堡食谱,并识别单个成分.
主要方法:
- 准备好的汉堡样品和蔬菜成分的薄片.
- 进行了矩阵辅助激光脱/电离 (MALDI) 和脱电喷电离 (DESI) MSI.
- 处理MSI数据以构建三个机器学习模型.
主要成果:
- 成功检测到蔬菜汉堡中的肉类改.
- 在蔬菜汉堡矩阵中准确识别单个成分.
- 从不同制造商和配方的汉堡之间进行有效的歧视.
结论:
- 与机器学习相结合的MSI提供了全面的汉堡特征.
- 这种方法解决了食品行业和消费者对食品真实性和质量的关键问题.
- 证明了食品产品分析中先进分析技术的潜力.
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