Rapid Detection Of Chemical Contaminants In Crude Palm Oil Using Surface-enhanced Raman Spectroscopy

Crude palm oil(CPO),a widely-used edible tropical vegetable oil,is popular in food processing,industrial production,and other areas due to its unique nutritional value and low cost.However,quality and safety concerns have drawn significant attention as the CPO market continues to expand,with issues such as illegal food additives,pesticide residues,and mycotoxins contamination posing significant health risks to consumers.Strengthening market food supervision and testing is crucial to address these health concerns.China has developed standard technical methods for the qualitative identification and quantitative detection of hazardous components in food.However,these conventional detection technologies still have some obvious limitations,including expensive instrumentation and reagents,complex operation,and long testing time.Therefore,it is necessary to develop more efficient detection methods.Surface-enhanced Raman spectroscopy(SERS)has emerged as an ideal alternative detection method due to its sensor diversity,ultra-high sensitivity,and unique fingerprint characteristics.This study used SERS and chemometrics techniques to develop an efficient,rapid,economical,non-destructive,sensitive,selective,and portable detection method,successfully detecting trace hazards(food additives,pesticides,and mycotoxins)in CPO.The main research content includes:1.Research on Dye Adulteration in CPO based on SERS and qualitative identification models: In response to the problems of long detection time and expensive equipment maintenance costs in traditional methods for detecting dye adulteration in CPO.Focusing on Sudan II and Sudan IV dyes,the study utilized a SERS sensor combined with chemometrics as an effective alternative method for rapidly and accurately detecting Sudan dyes in CPO.First,hollow gold and silver nanoflowers were synthesized as enhancing substrates and then incubated with CPO samples containing different concentrations(0.005ppm–4 ppm)of Sudan dyes.SERS spectra were collected,and principal component analysis was used to identify Sudan II and IV in CPO,with contribution rates as high as 99.88% and99.90%,respectively.Additionally,linear discriminant analysis(LDA)and K nearest neighbor(KNN)algorithms demonstrated high detection rates for Sudan II and IV dyes in CPO.The developed Au@Ag SERS sensor could detect Sudan II and IV dye concentrations as low as 0.0028 ppm and 0.0019 ppm,respectively.The results indicate that the rapid,sensitive,and low-cost SERS technology developed in this study provides a significant research foundation for safety detection in CPO and other edible oils,with the potential for widespread adoption in the future.2.Prediction of multiple dye adulteration in CPO using SERS combined with multivariate models: This study addressed the challenge of qualitative and quantitative detection of multiple dyes in CPO by focusing on four Sudan dyes(I-IV)and using SERS technology for their simultaneous quantitative detection in CPO.First,bimetallic gold-silver core-shell nanoflowers were synthesized as SERS-enhancing substrates.These substrates were mixed with the four Sudan dyes(I-IV)in CPO,and SERS spectra were collected.The Partial Least Squares(PLS),Ant Colony Optimization-Partial Least Squares(ACO-PLS)and Genetic Algorithm-Partial Least Squares(GA-PLS)models were used to construct quantitative relationship models between SERS spectra and the concentration of the four Sudan Red dyes.The results showed that the calibration coefficient(Rc)values for the GAPLS model were 0.9844,0.9865,0.9884,and 0.9888 for Sudan I,II,III,and IV,respectively.Furthermore,the real sample recoveries for Sudan I,II,III,and IV ranged from 88.0 to113.0%,92.0 to 103.0%,91.2 to 98.5% and 97.0 to 109.5%,respectively.These results demonstrate that the rapid,sensitive,and low-cost SERS technology developed in this study provides a crucial research foundation for the safety detection of CPO and other edible oils,with the potential for widespread adoption in the future.3.Research on pesticide residue detection in CPO based on SERS combined with chemometrics: In response to the high demand and fast detection requirements for pesticide residue detection in CPO.This study focused on detecting pesticide residues in CPO,specifically acetamiprid(ACE),using surface-enhanced Raman spectroscopy(SERS)combined with chemometrics.Silver nanoparticles were used as the enhancement substrate and mixed with ACE samples at different concentrations(5-100 ng/g).Three different quantitative models,including Successive projection algorithm-partial least squares(SPAPLS),random frog-partial least squares(RF-PLS),and uninformative variable eliminationpartial least squares(UVE-PLS),were used to develop robust prediction algorithms for the quantitative detection of SERS intensity and ACE concentration.The results showed that the RF-PLS model exhibited the best prediction performance,with an Rc value of 0.990,a prediction coefficient(Rp)value of 0.989,an RMSECV(cross-validation root mean square error)value of 4.74,and an RMSEP(prediction root mean square error)value of 5.17.Finally,based on the spiked recovery experiment,the recovery rate of the RF-PLS model ranged from93.89% to 108.32%,indicating that this method can be used for the quantitative detection of ACE residue.4.Mycotoxins detection in crude palm oil(CPO)using SERS combined with chemometrics: Specifically,the study focused on detecting aflatoxin B1(AFB1)in CPO using a combination of Qu ECh ERS(quick,easy,cheap,effective,rugged,and safe)extraction method and SERS to construct a quantitative prediction model.First,gold nanoparticles(Au NPs)were synthesized as the SERS signal substrate and incubated with AFB1 spiked CPO samples of different concentrations after extraction.The SERS signal was collected under optimal conditions,and a bootstrap soft shrinkage-partial least squares(BOSS-PLS)predictive model was established based on signal intensity and target concentration of the SERS signals.The model demonstrated excellent predictive performance with an Rc of 0.9929,an RMSECV of 0.204 ng/g,and a performance deviation ratio(RPD)of 3.93.In addition,the study also calculated an LOD value of 0.0095 ng/g,which was comparable to liquid chromatography-tandem mass spectrometry(LC-MS/MS),demonstrating the excellent extraction performance of the Qu ECh ERS method.The results demonstrate the potential of Au NPs and the Qu ECh ERS technique combined with chemometrics for AFB1 detection in CPO.This study shows that the developed method can be used to monitor the quality of CPO products,effectively preventing AFB1 contamination in the food industry.Furthermore,this method helps strengthen food safety guarantees and protect public health.5.Detection of multiple mycotoxins in crude palm oil(CPO)based on SERS combined with qualitative identification models: This study aimed to detect multiple mycotoxins that may exist in CPO using a combination of dispersive solid-phase extraction(d-SPE)and SERS techniques.Aflatoxin B1(AFB1)and ochratoxin A(OTA)were the target mycotoxins.A modified d-SPE method was used to extract and concentrate the target mycotoxins from spiked samples.Gold and silver core-shell nanoparticles are synthesized as SERS-enhancing substrates and incubated with the extracted concentrate,after which the SERS signal is collected.Principal component analysis(PCA),linear discriminant analysis(LDA),and K nearest neighbor(KNN)models were built based on the recorded SERS intensity.The results showed that both the LDA and KNN models achieved perfect classification and prediction rates of 100%.Additionally,LC-MS/MS validation showed that the improved d-SPE had a good extraction effect,with an LOD value for AFB1 as low as0.0086 ng/g and an LOD value for OTA as low as 0.0089 ng/g,which were significantly lower than the specified detection limit.These findings indicate that this method has practical potential in food safety monitoring and provides a promising alternative method for traditional analysis techniques to rapidly,reliably,and economically detect mycotoxins in crude palm oil,ensuring food safety and protecting public health.This study developed novel SERS sensors to detect Sudan dye adulteration,pesticide residues,and mycotoxin contamination in crude palm oil(CPO).These sensors exhibited high selectivity,sensitivity,reproducibility,and stability,providing accurate and reliable results.The findings confirm the significant potential of the developed SERS sensor combined with chemometric analysis for the practical detection and prediction of Sudan dyes,pesticide residues,and mycotoxins in crude CPO.As a result,the developed SERS sensor offers a new detection method that is more convenient and promising than traditional detection methods,which are time-consuming,costly,and environmentally unfriendly.The SERS sensors effectively simplify the detection of chemical contaminants in crude CPO,facilitates precise monitoring processes,and help prevent and control food contamination.