Food safety is a growing concern in recent years. This work presents the design of a simple and sensitive method for predicting 2,4-D (2,4-dichlorophenoxyacetic acid) residue levels in green tea extract employing surface-enhanced Raman spectroscopy (SERS) coupled uninformative variable elimination-partial least squares (UVE-PLS). Herein, SERS active citrate functionalized silver nanoparticles (AgNPs) with enhancement factor 1.51 × 10⁸ was used to prepare cellulose paper (common office) templated SERS sensor for acquiring SERS spectra of 2,4-D. The principle of the work was based on the interaction between 2,4-D and citrate group of AgNPs via chlorine atoms in the concentration range 1.0 × 10^-4 to 1.0 × 10³ µg/g. Three different wavenumber selection chemometric algorithms were studied comparatively to build an optimum calibration model, among them UVE-PLS showed enhanced performance as evident from the RPD value of 6.01 and Rp = 0.9864. Under optimized experimental condition proposed paper-based SERS sensor exhibited detection limit and RSD of 1.0 × 10^-4 µg/g and < 5%, respectively. In addition, the validation results by HPLC method were satisfactory (p > 0.05).

近年来，食品安全日益受到关注。这项工作提出了一种简单而灵敏的方法的设计，该方法采用表面增强拉曼光谱（SERS）耦合无信息变量消除-最小二乘最小二乘法来预测绿茶提取物中2,4-D（2,4-二氯苯氧基乙酸）的残留量（ UVE-PLS）。在本文中，使用具有增强因子1.51×10 ^8的SERS活性柠檬酸盐官能化银纳米颗粒（AgNPs）制备纤维素纸（公共办公室）模板化SERS传感器，以获取2,4-D的SERS光谱。该工作原理是基于2,4-D与柠檬酸基团的AgNPs通过浓度范围为1.0×10 ^-4至1.0×10 ^3的氯原子的相互作用。微克/克。比较研究了三种不同的波数选择化学计量算法，以建立最佳校准模型，其中UVE-PLS显示出增强的性能，从RPD值6.01和Rp = 0.9864可以明显看出。在优化的实验条件下，提出的纸基SERS传感器的检出限和RSD分别为1.0×10 ^-4 µg / g和<5％。此外，HPLC方法的验证结果令人满意（p> 0.05）。