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一种非破坏性的氨基基基猪肉新鲜度检测方法,使用UV-DOAS和光谱重建配合神经网络
1School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
Food chemistry
|December 19, 2025
概括
一种新方法使用紫外差光学吸收光谱和神经网络来检测超微量氨,这是猪肉腐败的关键指标. 这一进步克服了以前的测量限制,使得更准确的损坏检测成为可能.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 氨 (NH3) 是猪肉恶化的关键指标.
- 目前基于氨的检测方法受到超微量测量挑战的限制.
- 准确而敏感的氨检测对于食品安全和质量控制至关重要.
研究的目的:
- 开发一种新的,高度灵敏的超微量氨测量方法.
- 克服现有的氨检测技术在猪肉腐烂分析的局限性.
- 提高基于氨的食品质量评估的准确性和可靠性.
主要方法:
- 利用紫外差光学吸收光谱法 (UV-DOAS) 进行光谱采集.
- 实施光谱重建以转换光谱特征并分离干扰信号.
- 根据重建的光谱数据,应用了一个神经网络来进行度反转.
主要成果:
- 取得了28.028,087.6ppb的超微量氨检测范围.
- 证明了高精度,平均绝对百分比误差 (MAPE) 为1.50%和R2为0.999.
- 成功提取了氨信号,并在不同温度 (10°C和30°C) 上启用了超痕量测量.
结论:
- 建议的UV-DOAS方法与神经网络光谱重建相结合,对于超微量氨测量是有效的.
- 这种新的方法克服了检测氨作为腐烂指标的先前局限性.
- 该方法显示了提高猪肉质量监测和食品安全应用的巨大潜力.
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