Rapid quantitative analysis of acetamiprid residue in crude palm oil using SERS coupled with random frog (RF) algorithm

Abstract

Acetamiprid (ACE) is a neuroactive insecticide similar to nicotine. ACE can cause neurotoxicity, immunotoxicity, and hepatotoxicity. This study explored the feasibility of using Surface-enhanced Raman spectroscopy (SERS) sensor and random frog (RF) algorithm to rapidly detect ACE in crude palm oil (CPO) within 400 – 1800 cm− 1 Raman peak. ACE levels varied from 5 to 100 ng/g. Successive projections algorithm – PLS (SPA- PLS), random frog-partial least squares – PLS (RF-PLS), and uninformative variable elimination-partial least squares (UVE-PLS) were used to develop quantitative models for ACE prediction after the data was pretreated with standard normal variate (SNV). The RF-PLS model provided superior results with Rc, Rp, RMSECV and RMSEP values of 0.990, 0.989, 5.17 and 6.95, respectively, with recovery rates of 93.89 – 108.32%. The findings demonstrate the enormous potential of the proposed SERS sensor in combination with RF-PLS for the rapid detection of ACE residues in CPO.