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比较评估激光电离和iKnife与快速蒸发电离质谱和机器学习相结合,用于Larimichthys crocea的地理认证

Weibo Lu¹, Honghai Wang¹, Lijun Ge¹

¹Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, China.

|July 25, 2024

概括

使用激光辅助快速蒸发电离质谱 (LA-REIMS) 和机器学习 (ML) 的新方法准确地验证了大黄 (LYC) 的地理来源. 这项技术支持海鲜行业的可追溯性.

科学领域:

分析化学分析化学
食品科学食品科学食品科学
生物技术是生物技术.

背景情况:

鱼 (Larimichthys crocea,简称LYC) 是一个经济上重要的海洋鱼类.
不准确的LYC地理标签可能会对消费者信任和市场价值产生负面影响.
建立可靠的原产地认证方法对于水产品行业至关重要.

研究的目的:

开发和验证一种新的方法来确定Larimichthys crocer.的地理真实性.
为了比较激光辅助快速蒸发电离质谱法 (LA-REIMS) 与其他技术的有效性.
创建一个强大的机器学习 (ML) 模型,用于准确的来源追踪.

主要方法:

开发一个激光辅助的快速蒸发电离质谱仪 (LA-REIMS) 系统用于样本分析.
来自中国六个不同的地理区域的LYC样本的分析.
应用六个机器学习分类器用于地理起源建模.
优化超参数和特征工程,用于模型增强.

主要成果:

由于减少热损伤,自动化和易于使用,LA-REIMS方法在iKnife上表现出优越性.
分析确定了798个不同地理来源的特征性离子.
Chi2(15%) -KNN模型实现了高精度,训练精度为98.4 ± 0.9%,测试精度为98.5 ± 1.4%.
开发的方法证明了LYC起源追踪的快速,准确和智能.

结论:

通过LA-REIMS/ML方法,提供了一种有效的解决方案,用于验证Larimichthys crocea的地理来源.
这项技术为加强海鲜行业内可追溯性系统提供了巨大的潜力.
这些发现支持开发水产品的强有力的质量控制和消费者保护措施.

关键词:

拉里米希斯·克罗塞亚 (Larimichthys crocea) 是一个鱼.机器学习机器学习快速蒸发性电离质谱法质谱学可追溯性可追溯性

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