Abstract
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.
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