Rapid quantitative analysis of acetamiprid residue in crude palm oil using SERS coupled with random frog (RF) algorithm
Selorm Yao‐Say Solomon Adade, Hao Lin, Nana Adwoa Nkuma Johnson, Zhu Afang, Zeyu Chen, Suleiman A. Haruna, John‐Nelson Ekumah, Akwasi Akomeah Agyekum, Huanhuan Li, Quansheng Chen
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.