SERS-based sensor coupled with multivariate models for rapid detection of palm oil adulteration with Sudan II and IV dyes

Recent reports of adulteration using Sudan dyes have raised consumer and regulatory concerns regarding the safety of edible crude palm oil (CPO). This research developed a novel Hollow Au@Ag Nanoflower (NF) to detect Sudan II and IV dyes in CPO. Different concentrations of the Sudan dyes were combined with a Surface-enhanced Raman spectroscopy (SERS) sensor that yielded a strong SERS signal logarithmically with increasing concentrations of the dyes ranging from 0.005 to 4.0 ppm. Competitive Adaptive Reweighted Sampling (CARS- PLS), Genetic algorithm – PLS (GA-PLS), and bootstrapping soft shrinkage-PLS (BOSS-PLS) were used to develop quantitative models for Sudan dyes prediction. The CARS-PLS model performed best for Sudan II and IV, with Rc values of 0.9921 and 0.9846, respectively, and real sample recovery rates of 98.79–104.49% and 94.37–109.59%. The results show that the SERS sensor combined with chemometrics has great potential to detect trace Sudan dyes in CPO rapidly. •Sudan dyes were successfully detected in CPO using SERS combined with chemometrics.•Hollow Au@Ag NFs SERS substrate was used to detect Sudan dyes in CPO.•CARS-PLS performed better than the other three models examined.•Fabricated Au@Ag NFs sensor comparable to HPLC in detecting CPO adulteration.