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作为标志着鱼新鲜度状态的指标的挥发性标记物,通过volatolomics和机器学习识别

Qian Hu¹, Bingwu Zhou², Guoping Li¹

¹Chinese Academy of Quality and Inspection & Testing, Beijing 100176, People's Republic of China; Key Laboratory of Food Authenticity Identification, State Administration for Market Regulation, Beijing 100176, People's Republic of China.

Food research international (Ottawa, Ont.)

|November 21, 2025

概括

这项研究确定了关键的挥发性化合物,如甲基醇,醇和酸,这些化合物表明鱼的新鲜度下降. 这些标记为客观评估储存期间的鱼质量提供了一种新方法.

科学领域:

食品科学食品科学食品科学
分析化学分析化学
代谢学代谢学代谢学

背景情况:

鱼的新鲜度对于质量和消费者接受至关重要.
与鱼腐烂相关的挥发性化合物和代谢途径尚不清楚,尤其是在过渡到低新鲜度期间.

研究的目的:

在冷藏储存期间系统识别鱼新鲜度的挥发性标记物.
为了描述鱼腐烂所涉及的代谢途径.
开发一个客观的方法来评估鱼的质量.

主要方法:

头部空间固相微提取与气色谱质谱学 (HS-SPME-GC-MS) 结合使用,用于分析挥发性化合物.
随机森林机器学习和多变量统计分析 (ANOVA,OPLS-DA,O2PLS,MLR) 用于识别和验证标记化合物.
鱼样本被存储在4°C,并分为新鲜,不新鲜和坏坏的阶段.

主要成果:

最初确定了189种明显不同的挥发性化合物.
随机森林建模选择了40个具有高预测准确度的潜在标记 (AUC = 0.988).
甲基糖,醇和酸被证实是新鲜度降解的强有力的标志物.
关键的破坏途径包括烯降解,氨酸代谢,硫代谢,糖解,酸盐代谢和氨酸降解.

结论:

一个基于volatolomics的战略有效地确定了鱼的关键挥发性损坏指标.
甲基醇,醇和酸作为评估鱼新鲜度的可靠标志物.
这种方法为客观的鱼质量评估提供了有价值的替代方案.

关键词:

新鲜感新鲜感机器学习是机器学习.鱼鱼鱼鱼是一种损坏指数是指损坏指数在新鲜的状态下.伏拉托洛米克斯 (Volatolomics) 是一种飞行学.

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