Rapid Detection Of Mercury And Arsenic In Tea Using Surface-enhanced Raman Scattering

The tea plant(Camellia sinensis)is one of the largest cultivated cash crops around the world.Its processed form(leaves or powder)is majorly consumed as a beverage and also utilized as a raw material in several industries due to its high medicinal and health beneficial properties.However,tea can be contaminated by heavy metals mainly through industrialization and agricultural activities.Heavy metals are toxic and dangerous food contaminants that pose health risks to living organisms even at trace levels.Due to this,there must be the absence of heavy metals in tea to enhance its safety,quality and safeguard against harmful health implications.Concerning this,the rapid inspection and assessment of heavy metals in tea have become a critical issue in terms of ensuring food security,the safety of consumers and the improvement of business productivity.Thus,to provide a remedy for these concerns,this research study attempted the deployment of surface-enhanced Raman scattering(SERS)technology as a fast,simple,low cost,non-destructive,sensitive,selective,and portable detection method for the fabrication of different unique SERS nanosensors for detection,quantification and prediction of heavy metals in tea coupled with chemometric algorithms.This study is divided into two groups-two different labelled SERS-based nanosensors for mercury(Hg)and total arsenic(As)detection and label-free SERS-based nanosensor for total arsenic(As)detection.The principal achievements of this study are summarized as follows:1.Chemometrics coupled 4-Aminothiophenol labelled Ag-Au alloy(Ag-Au/4-ATP)SERS off-signal nanosensor for quantitative detection of mercury in tea.The SERS enhancement property of 4-aminothiophenol(4-ATP)Raman active reporter was employed as a signal turn off approach functionalized on Ag-Au alloyed nanoparticle to firstly detect Hg²⁺in standard solutions and spiked tea samples.Different chemometric algorithms were applied on the acquired SERS and inductively coupled plasma-mass spectrometry(ICP-MS)chemical reference data to select effective wavelengths and spectral variables in order to develop models to predict the Hg²⁺.Results indicated that Ag-Au/4-ATP SERS sensor combined with ant colony optimization partial least squares(ACO-PLS)exhibited the best correlation efficient and minimum errors for Hg²⁺standard solutions(Rc=0.984,Rp=0.974,RMSEC=0.157?g/m L,RMSEP=0.211?g/m L)and spiked tea samples(Rc=0.979,Rp=0.963,RMSEC=0.181?g/g and RMSEP=0.210?g/g).The limit of detection of the proposed sensor was 4.12×10^-7?g/m L for Hg²⁺standard solutions and2.83×10^-5?g/g for Hg²⁺spiked tea samples.High stability and reproducibility with relative standard deviation of 1.14%and 0.84%were detected.The potent strong relationship between the SERS sensor and the chemical reference method encourages the application of the developed chemometrics coupled SERS system for future monitoring and evaluation of Hg²⁺in tea.2.A fabricated 3-Aminobenzeneboronic acid labelled Ag(Ag/ABBA)SERS-based system for detection and prediction of total arsenic in tea combined with chemometric algorithms.A simple synthesis procedure by modifying ABBA on Ag nanoflowers as SERS substrate was introduced and combined with chemometric algorithms for detection and prediction of total arsenic standard solutions and total arsenic in tea.The detection mechanism was associated with the detachment of the ABBA from the surface of the Ag and the chemical coordination between the arsenic and the Ag nanoparticles.The recorded SERS data was comparatively processed with different chemometric algorithms to screen out the most useful wavelengths and variables from the spectral data to achieve efficient and reliable prediction models.The results revealed that competitive adaptive reweighted sampling partial least squares(CARS-PLS)algorithm executed on the spectral data,accurately predicted the total arsenic information in the reference chemical data with higher correlation coefficient(R)results in the prediction set for both the total arsenic standard concentrations(Rp=0.952)and spiked tea samples(Rp=0.975).The recorded limit of detection values was 5.60×10^-3?g/m L and 2.73×10^-2?g/g respectively for the total arsenic standard concentrations and spiked tea samples.The developed Ag/ABBA nanosensor presented good reproducibility,stability,and good recoveries in tea samples within the ranges of 83.84-109.53%.This demonstrated that the Ag/ABBA SERS fused CARS-PLS method is a sensitive and robust quantitative technique and could be deployed for future applications.3.Development of SERS-based label-free cuprous oxide/silver(Cu2O/Ag)fused Chemometrics nanosensor for rapid detection of total Arsenic in tea.This total As detection and prediction approach was achieved for the first time with a portable Raman spectroscopy using Cu2O/Ag SERS substrate combined with quantitative chemometric algorithms.Varying total As spiked tea concentrations were mixed with the Cu2O/Ag SERS nanoprobe for the SERS detection.Quantitative models were established for predicting the total As in tea by comparatively applying chemometric algorithms.Amongst the algorithms,competitive adaptive reweighted sampling partial least squares(CARS-PLS)optimized the most effective spectral variables to efficiently predict the total As in tea with higher correlation coefficient value(Rp=0.994),very low root means square error(RMSEP=0.050?g/g)in the prediction set and recorded the highest RPD value of 8.819.The proposed nanoprobe achieved a lower detection limit of 5.61×10^-3?g/g,excellent selectivity,satisfactory reproducibility,and stability.No significant difference was recorded when the performance of the Cu2O/Ag total As SERS sensor was compared with inductively coupled plasma mass spectrometry(ICP-MS)method.Therefore,this developed Cu2O/Ag coupled chemometrics SERS sensing method could be used to efficiently determine,quantify,and predict total As in tea promote monitoring of heavy metal contaminants.In this study,labelled SERS sensors were first developed to detect mercury and arsenic separately in tea,which demonstrated high selectivity,sensitivity,reproducibility,and stability.In addition,to further cut down cost,increase the heavy metal absorption efficiency and prolong the detection shelf life of the sensor,a label-free SERS sensor was fabricated to detect arsenic in tea which exhibited good detection performance.The developed SERS sensors combined with chemometrics have demonstrated great potential for practical detection and prediction of heavy metals in tea.Therefore,these fabricated SERS sensors can replace the existing traditional detection methods to help in the control and prevention of heavy metals in food and the environment.