Au@Ag nanoflowers based SERS coupled chemometric algorithms for determination of organochlorine pesticides in milk

In this study, SERS coupled with chemometric algorithms like PLS (partial least square), Si-PLS (synergy intervalPLS), GA-PLS (genetic algorithm-PLS) and UVE-PLS (uninformative variable elimination-PLS) were applied for the detection of 2,4-D and imidacloprid in milk. The fabricated gold and silver core-shell nanoflowers (Au@Ag NFs) were used as SERS nanosensor which exhibited strong signal over the concentration range of 1.0 x 10(-3) to 1.0 x 10(2) ng/mL. Performances of the models were evaluated based on the attained correlation coefficients of the prediction (R-p) and calibration (R-c), root mean square error in calibration (RMSECV) and prediction (RMSEP), and the ratio of sample standard deviation to RMSEP (RPD). The models exhibited improved results in the order of PLS < Si-PLS < GA-PLS < UVE-PLS. The UVE-PLS model showed superior performance, yielded R-c = 0.9801 and R-p = 0.9878 with RPD = 6.32 for 2,4-D, and R-c = 0.9732 and R-p = 0.9726 with RPD = 4.30 for imidacloprid. The strategy could be employed for safety and quality monitoring of milk.

Automated summary

SERS combined with chemometric algorithms was used for the detection of 2,4-D and imidacloprid in milk. The UVE-PLS model showed superior performance, indicating its potential for safety and quality monitoring of milk.