使用抗干扰色度传感器阵列与近红外光谱学相结合,对目标重金属进行定量和定性检测
Kexin Zhang1, Bridget Ama Kwadzokpui1, Selorm Yao-Say Solomon Adade2
1School of Food and Biological Engineering, Jiangsu University, Jiangsu 212013, PR China.
Food chemistry
|July 18, 2024
概括
一种新的色度传感器阵列 (CSA) 技术使用模式识别精确检测玉米油中的重金属. 殖民地优化-K-最近邻居 (ACO-KNN) 模型在识别这些污染物方面取得了超过90%的准确性.
科学领域:
- 分析化学 分析化学
- 传感器技术 传感器技术
- 食品安全 食品安全
背景情况:
- 玉米油中的重金属污染对健康构成重大风险.
- 准确可靠的检测方法对于确保食品安全至关重要.
- 现有的方法可能缺乏特异性或容易受到干扰.
研究的目的:
- 开发一种抗干扰色度传感器阵列 (CSA) 技术,用于检测玉米油中的重金属.
- 使用模式识别算法建立一个强大的识别模型.
- 评估不同算法的性能,用于定性和定量分析.
主要方法:
- 基于结合机制和可见颜色变化的CSA技术的开发.
- 使用定制的嗅觉可视化设备来获取光谱数据.
- 应用群优化-K-最近邻 (ACO-KNN) 和群优化-部分最小方形 (ACO-PLS) 算法用于模式识别.
主要成果:
- 在重金属识别方面,ACO-KNN模型表现出高精度 (90.28%校准,89.58%预测).
- ACO-PLS模型表现出卓越的稳定性,预测中位平方误差 (RMSEP) 最低.
- 达到 (Pb) 和 (Hg) 的检测 (LOD) 和定量 (LOQ) 的低极限.
结论:
- 开发的CSA技术与模式识别相结合,为检测玉米油中的重金属提供了一个有希望的方法.
- ACO-KNN和ACO-PLS模型为准确和稳定的分析提供了有效的工具.
- 这种方法有助于加强食品安全监测和质量控制.
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