多变量气体传感器电子鼻子系统与PARAFAC和机器学习建模,用于量化和分类渔具的影响.
Vinie Lee Silva-Alvarado1, Jaime Lloret1
1Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Carrer del Paranimf 1, 46730 Grao de Gandia, Valencia, Spain.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
一个电子鼻子 (E-nose) 传感器可以追踪海鲜的渔具来源. 这种方法准确地识别了捕捞方法,揭示了每个具对鱼类质量的影响.
科学领域:
- 海洋生物学 海洋生物学
- 食品科学 食品科学 食品科学
- 传感器技术 传感器技术
背景情况:
- 海鲜质量受到捕捞方法,处理和压力的影响.
- 传统的可追溯性方法在准确反映渔具的影响方面面临挑战.
研究的目的:
- 开发和验证一种用于量化渔具对海鲜质量影响的创新方法.
- 通过使用E-鼻子传感器和先进的分析技术,对Sparus aurata的原产地渔具进行分类.
主要方法:
- 使用一个E-鼻子传感器捕获Sparus aurata的挥发性概况.
- 应用并行因素分析 (PARAFAC) 用于影响量化.
- 用机器学习 (ML) 模型对渔具进行分类.
主要成果:
- 水产养殖和鱼捕捞方法对鱼类的影响最大.
- 长绳渔业的偏差是最低的.
- 亚空间KNN模型在验证中实现了97.14%的准确性,在测试轮分类中达到98.08%.
结论:
- 与PARAFAC和ML相结合的E-鼻子传感器提供了高精度的鱼类可追溯性.
- 这种方法可以有效地识别导致鱼类质量变化的渔具.
- 传感器响应配置文件为每个捕捞方法提供独特的签名.
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