集成的表面增强拉曼光谱与LASSO用于快速定量分析类植物中甲酸盐残留物
Zhoujun Guo1, Aihua Xiong2, Shirong Ai1
1College of Software, Jiangxi Agricultural University, Nanchang, 330045, People's Republic of China.
Talanta
|December 30, 2025
概括
这项研究开发了一种快速方法,使用表面增强的拉曼光谱 (SERS) 和先进的数据分析来检测类果中的PRO残留物. 优化的技术可确保准确可靠地检测有害的真菌杀菌剂水平.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 食品安全 食品安全
背景情况:
- 普罗克洛拉兹 (PRO) 是一种用于类作物收获后储存的杀菌剂.
- 过度的PRO残留物对人类健康构成风险.
- 需要快速可靠的PRO检测方法.
研究的目的:
- 开发一种快速而准确的检测方法,用于果中的PRO残留物.
- 将表面增强的拉曼光谱 (SERS) 与特征优化算法集成在一起.
- 提高PRO残留分析的效率和可靠性.
主要方法:
- 准备黄金纳米粒子 (AuNPs) 用于SERS.
- 将四种特征优化算法应用于SERS谱的应用.
- 构建和比较部分最小平方 (PLS) 和支向量机 (SVM) 模型.
- 优化使用LASSO进行功能选择.
主要成果:
- AuNP表现出极好的增强性,稳定性和可重复性.
- 基于优化特征峰值的PLS和SVM模型优于单峰模型.
- 拉索选择了11个关键峰值 (1.46%的完整SERS) 为一个最佳的SVM模型.
- 每个样品在15分钟内达到0.012mg/kg的最低检测极限.
- 验证的方法显示了高精度,可靠性和抗干扰能力.
结论:
- 集成的SERS和特征优化策略为果中PRO检测提供了灵敏和快速的方法.
- 优化了 LASSO 的 SVM 模型提供了卓越的预测性能和模型简化.
- 开发的方法符合检测果中的甲酸盐残留物的实际要求,确保食品安全.
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