Rapid Quantification Of Selected Pesticides Residue Levels In Tea Using Surface-enhanced Raman Spectroscopy

Tea is considered as the most widely consumed non-alcoholic beverage in the world,obtained from the leaves of Camellia sinensis L.which has plenty of medicinal properties and also used as a raw material of manifold industries.The consumption of tea and its derived product are being increased rapidly every day.In addition,as a medicinal beverage tea is needed to be free from different food contaminants like pesticides otherwise it can inversely affect human health.Consequently,rapid monitoring of safety indicators like pesticides residue has become crucial towards the safeguarding of human being as well as businesses.Mostly employed safety monitoring techniques for tea are time-consuming,labor-intensive,prolonged sample pretreatment procedure,sophisticated and expensive,which increases the lack of interest for monitoring of safety indicator resulting in farmers can be incurred high losses due to the rejection and human health can also be threatened.This study focuses on the development of novel SERS?Surface-enhanced Raman spectroscopy?based rapid monitoring techniques of some selected pesticides residue levels in tea and their comparison towards safety and quality assurance.The whole study initially divided into two groups-bio-nanosensor for acetamiprid?AC?and chemometric algorithm coupled label-free nanosensor.The label-free nanosensors again subdivided into two groups-single analyte detection sensor individually for AC,imidacloprid,2,4-D and simultaneous detection nanosensor for AC,2,4-D;and simple SPE sample pretreatment process adopted for all sensor to remove color and other interfering substances.The major achievements of this study are summarized in bellow:1.SERS based fast prediction of imidacloprid in tea applying silver nanostructure?AgNF?coupled multivariate calibration.Highly roughened AgNF as SERS label-free nanosensor combined with multivariate calibration such as PLS?partial least square?,Si-PLS?synergy interval-PLS?,Bi-PLS?back interval-PLS?and GA-PLS?genetic algorithm-PLS?was first attempted to predict imidacloprid residue levels in spiked green tea samples as a safety indicator.Surfactant influenced wet chemical method used fabricated AgNF was flower-like and highly sensitive to imidacloprid which can generate strong SERS signal of imidacloprid after adsorption on AgNF while illuminated with leaser by the electromagnetic enhancement and a linear relationship was observed between the peak intensity and concentration over the range 1.0×10³to 1.0×10^-4?g/g.GA-PLS model achieved superior result in their prediction among the four models based on the value of RPD?ratio performance deviation?and model result increases in the order to PLS<Bi-PLS<Si-PLS<GA-PLS.The test set of GA-PLS model yielded Rp?correlation coefficient?=0.9702 and RPD=4.95.The fabricated sensor achieved LOD of 4.55×10^-5?g/g for imidacloprid and precision analysis yielded RSD?4.50%,suggested that the fabricated SERS nanosensor coupled GAPLS model may be employed for the fast prediction and quantification of imidacloprid residue levels in tea.2.Fabricated spherical shaped AgNPs SERS nanosensor for prediction of 2,4-D?2,4-dichlorophenoxyacetic acid?residue levels in spiked tea.SERS active AgNPs?silver nanoparticles?were synthesized by hydrothermal method and chemometric algorithms like PLS,GA-PLS,ACO-PLS?ant colony–PLS?and CARS-PLS?competitive adaptive reweighted sampling–PLS?were comparatively studied to build an optimum prediction model for 2,4-D as a safety indicator.The fabricated SERS nanosensor exhibited strong SERS peak of 2,4-D at 391 cm^-1 over the concentration range 1.0×10^-4 to 1.0×10³?g/g which is conceivably due to the interaction between the chlorine atom of 2,4-D and AgNPs.Built-models performances were evaluated in accordance with attained correlation coefficients of the prediction?Rp?and calibration?Rc?;root mean square error in calibration?RMSECV?and prediction?RMSEP?,and RPD,the model result improved in the order of PLS<ACO-PLS<GA-PLS<CARS-PLS.CARS-PLS showed superior performance among them based on the evaluation parameter which yielded Rc=0.9939 and Rp=0.9931 with RPD=6.62.Under optimized experimental condition,the build nanosensor exhibited detection limit and RSD of 2.88×10^-5?g/g and<5%,respectively.Subsequently,the method was validated with HPLC and recovery result of the two systems showed insignificant differences?p>0.05?.Achieved results imply SERS combined to CARS-PLS using AgNPs nanosensor could be employed for rapid prediction and quantification of 2,4-D residue labels in tea for safety and quality monitoring.3.rGO-NS SERS nanosensor coupled GA-PLS for monitoring of AC residue levels in tea.Reduced graphene oxide?rGO?supported novel metal nanocomposite materials have currently flourished as potential nanomaterials towards the monitoring of safety and quality in food on account of the unique structure of rGO which can favor the aromatic molecule through the hydrophobic and?--?interaction.rGO supported gold-nano-star?rGO-NS?nano-composite nanosensor was fabricated via seed mediated hydrothermal and wet chemical method as SERS active nanosensor and employed for the acquisition of AC SERS spectrum from green tea extract.rGO-NS nano-composite nanosensor generates strong SERS signal for AC while it landed on and made a linear relationship between the SERS signal intensity and the concentration of AC ranging from 1.0×10^-4 to 1.0×10³?g/g signifying the potentiality of rGO-NS nano-composite nanosensor to sense AC in green tea.A robust quantitative prediction model for AC was established by employing GA-PLS algorithm on rGO-NS nano-composite nanosensor acquired SERS spectra of AC.The GA-PLS model achieved correlation coefficient value,Rc=0.9772 and Rp=0.9757 in its calibration and prediction set,respectively.The recovery result of 97.06%to 115.88%was obtained from the spiked real sample applying the proposed method and RSD value of<5.98%in repeatability and reproducibility analysis indicating the proposed rGO-NS SERS nanosensor coupled GA-PLS model has good accuracy and precision to predict AC residue levels in green tea.In addition,the calculated LOD for AC was 2.13×10^-5?g/g using the developed method.Therefore,the overall result corroborated the SERS active rGO-NS nanosensor coupled GA-PLS model could be deployed for the prediction of AC residue levels in green tea for safety assurance.4.Simultaneous SERS based detection of AC and 2,4-D residue levels in tea using Ag@Au coupled GA-PLS model.SERS based multi-analyte detection in food matrix is a big challenge owing to the interference of different micro and macro nutrients as well as pigments.For minimizing this limitation,a novel SERS based platform for simultaneous detection of two pesticides in a single step has been developed coupled with solid phase extraction?SPE?and variable selection chemometric algorithm to extend the applications of SERS.Au@Ag?gold core silver shell nanoparticles?was synthesized through hydrothermal method as SERS active substrate to fabricate Au@Ag nanosensor for rapid and sensitive monitoring of SERS signal of AC and 2,4-D in green tea extract.To remove the potential interfering substances from with and without spiked AC and 2,4-D extract of tea were percolated through the SPE cartage.The elute was introduced to the Au@Ag nanosensor and generated strong SERS signal for AC and 2,4-D both in single and mixed condition over the concentration range 1.0×10^-4 to 1.0×10³?g/g.The GA-PLS algorithm exhibited a linear calibration model,and Rp values of 0.9943 and 0.9923 with RPD values of 6.53and 6.23,respectively for AC and 2,4-D in prediction model reveal the robustness and stability of the build model.The LODs of 2.63×10^-5?g/g for AC and 4.15×10^-5?g/g for 2,4-D were achieved for the fabricated nanosensor.The method was validated successfully in terms of student's t-and F-test at 95%confidence with satisfactory recovery percentages in both spiking conditions.An RSD value?4.85%suggesting the potential of the developed nanosensor coupled GA-PLS algorithm for quick detection of pesticides residue in green tea.5.SERS based novel magnetic bio-nanosensor for ultra-sensitive detection of AC in tea.To obtain optimal SERS substrate,silver core gold shell nanoparticles?Ag@Au CSNPs?has successfully been fabricated via in situ hydrothermal seeds growth method,which was modified with Raman molecule 4,4?-dipyridyl?DP?and thiolated AC aptamer to build signal probe;and thiolated AC aptamer immobilized Fe₃O₄@Au core-shell nanoparticles?Fe₃O₄@Au CSNPs?used as capturing probe for AC.Owing to the specific binding capacity of aptamer,capture probe bind with the target AC molecule when it present in the detection solution,afterwards,the same AC molecule can also bind to the signal probe whenever it introduced into the system and form a sandwich structure detection system.The concentration of AC was determined via the signal enhancement of DP peak at 1290 cm^-1.The bio-nanosensor exhibited a good linear relationship between the SERS signal of signal probe and the concentration of AC ranging from 1.0×10^-5 to10?g/g.The LOD was calculated to be 5.894×10^-6?g/g and the RSD value of?4.93%in precision analysis pointed to robustness of this bio-nanosensor.In addition,the result of unknown spiked concentration detected by the proposed sensor was validated by the HPLC and the difference of the recovery result between two methods was not significant?p>0.05?.Moreover,the specificity of the developed bio-nanosensor was determined by detecting similar structure pesticides.The overall result demonstrated that proposed bio-nanosensor could be employed for the detection of AC residue levels in tea toward safety and quality monitoring.