基于微型光谱传感器的猪肉中可生存总数的非破坏性智能检测
Jiewen Zuo1, Yankun Peng1, Yongyu Li1
1College of Engineering, China Agricultural University, Beijing 100083, China.
Food research international (Ottawa, Ont.)
|November 27, 2024
概括
一个使用可见/近红外光谱和机器学习的新型便携式设备可以准确预测猪肉腐烂. 这项技术可以实时监测总活力计数 (TVC),以确保运输和储存期间的肉类新鲜度.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 微生物腐烂会对肉类的新鲜度产生重大影响,因此需要在复杂的供应链中实时监控.
- 目前用于评估肉类腐烂的方法往往耗时,不适合在现场应用.
研究的目的:
- 开发和验证一个便携式可见/近红外 (Vis/NIR) 光谱装置,以快速地在现场预测猪肉的总可生存数量 (TVC).
- 优化光谱数据处理和机器学习算法,以准确预测损坏情况.
主要方法:
- 使用便携式Vis/NIR光谱仪从猪肉样本中获取光谱数据.
- 评估了各种光谱预处理技术,包括分辨率间隔校正和标准正常变化.
- 机器学习算法,特别是间隔随机青 - 部分最小平方回归 (iRF-PLSR),用于特征波长选择和模型开发.
主要成果:
- 优化的全波长模型实现了0.918的预测相关系数 (RP).
- 简化的iRF-PLSR模型表现出卓越的预测性能,RP = 0.948,将模型复杂度降低了85.45%.
- 便携式设备显示的结果与复杂的检测系统可比,用于预测肉类TVC.
结论:
- 一个便携式的Vis/NIR光谱系统与先进的数据处理相结合,为实时监测猪肉损坏提供了一个可行的解决方案.
- 这项技术可以提高整个肉类供应链的食品安全和质量控制.
- 开发的方法为TVC的传统微生物学测试提供了快速而准确的替代方案.
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