Research Articles

Permanent URI for this collectionhttps://atuspace.atu.edu.gh/handle/123456789/42

Browse

Now showing 1 - 20 of 20

Advances in nondestructive methods for meat quality and safety monitoring.
(Materials Science and Engineering:, 2019) Kutsanedzie, F. Y.; Guo, Z.; Chen, Q.
Meat is highly perishable and poses health threats when its quality and safety is unmonitored. Chemical methods of quality and safety determination are expensive, time-consuming and lack real-time monitoring applicability. Nondestructive techniques have been reported as antidotes to these constraints. This paper assessed the potential of nondestructive techniques such as near-infrared spectroscopy, hyperspectral imaging, multispectral imaging, e-nose, and their data fusion, all combined with algorithms for quality monitoring of pork, beef, and chicken, the most consumed meat sources in the world. These techniques combined with data processing applications may offer a panacea for realtime industrial meat quality and safety monitoring.
Feasibility study on nondestructively sensing meat's freshness using light scattering imaging technique.
(Meat Science, 2016) Li, H.; Sun, X.; Pan, W.; Kutsanedzie, F. Y.; Zhao, J.; Chen, Q.
Rich nutrient matrix meat is the first-choice source of animal protein for many people all over the world, but it is also highly susceptible to spoilage due to chemical and microbiological activities. In this work, we attempted the feasibility study of rapidly and nondestructively sensing meat's freshness using a light scattering technique. First, we developed the light scattering system for image acquisition. Next, texture analysis was used for extracting characteristic variables from the region of interest (ROI) of a scattering image. Finally, a novel classification algorithm adaptive boosting orthogonal linear discriminant analysis (AdaBoost–OLDA) was proposed for modeling, and compared with two classical classification algorithms linear discriminant analysis (LDA) and support vector machine (SVM). Experimental results showed that classification results by AdaBoost–OLDA algorithm are superior to LDA and SVM algorithms, and eventually achieved 100% classification rate in the calibration and prediction sets. This work demonstrates that the developed light scattering technique has the potential in noninvasively sensing meat's freshness.
Highly sensitive and label-free determination of thiram residue using surface-enhanced raman spectroscopy (SERS) coupled with paper-based microfluidics.
(Analytical Methods, 2017) Zhu, J.; Chen, Q.; Kutsanedzie, F. Y.; Yang, M.; Ouyang, Q.; Jiang, H.
In this study, a paper-based microfluidic surface-enhanced Raman spectroscopy (SERS) device was employed for the determination of trace level thiram. The paper-based microfluidic device was fabricated by cutting a hydrophilic region which had been printed on the filter paper and then pasting it onto sellotape. The Au@Ag nanoparticles (NPs) were synthesized with a 30 nm Au core and 7 nm Ag shell and used as the SERS probe. The synthesized nanoparticles were dropped in one of the sample adding zones of the paper-based microfluidics and the thiram solution was dropped in another one. The solutions flowed through their own channels by capillary action and mixed together in the reaction chamber. The optimization studies on the use of paper-based microfluidic devices are discussed. In SERS measurements, the intensity of the peak at 1143 cm−1 was highly sensitive, and so it was chosen as an ideal peak for the quantitative analysis of the concentration of thiram solution. The limit of detection (LOD) of thiram was as low as 1.0 × 10−9 mol L−1, and the relative standard deviation (RSD) results analyzed at 10 random spots in the SERS measurement area were all below 10%. The recovery values of thiram in adulterated tea samples were from 95% to 110%. All these results suggest that this proposed method is a prospective candidate for trace level thiram detection.
Hydrothermal fabrication of MoS2/reduced graphene oxide nanohybrid composite for the electrochemical sensing of Hg (II) in green tea.
(Materials Today: Proceedings, 2022) Annavaram, V.; Somala, A. R.; Chen, Q.; Kutsanedzie, F. Y.; Agyekum, A. A.; Zareef, M.; Hassan, M. M.
Heavy metal contamination is a standout among the most genuine ecological issues: toxicity, persistence, bioaccumulation, and biomagnification through food chains. The present work aims at the synthesis of abundant, fast-sensing electrochemical sensors MoS2 and MoS2@rGO composite by the hydrothermal method to develop electrochemical sensors for the detection of Mercury (Hg-II). The synthesized material was characterized and conformed to a hierarchical spherical sponge-like structure with a high surface-to-volume ratio. The electrochemical sensor conditions were observed at ambient conditions to detect Hg (II) (0.5, 1, 1.5, 2, 2.5, 3, 3.5 µm L−1 was used) and the results showed very promisingly. The limit of detection (LOD) was found to be 2.0 × 10−7 µg/mL for MoS2, 1.22 × 10−8 µg/mL for composite. The heavy metals were spiked in green tea extract to observe the sensor ability of the material. The sensor ability for the material for real-time detection of green tea was found to be LOD-2.12 × 10−7 µg/mL (MoS2) and 1.21 × 10−9 µg/mL (MoS2@rGO).
In situ cocoa beans quality grading by near-infrared-chemodyes systems.
(Analytical Methods, 2017) Kutsanedzie, F. Y.; Chen, Q.; Sun, H.; Cheng, W.
Fermentation level is a key bean quality indicator in the cocoa industry. Colorimetric sensor e-nose (CS e-nose) and an innovatively designed near infrared chemo-intermediary-dyes spectra technique (NIR-CDS) combined with four chemometric algorithms – extreme machine learning (ELM), support vector machine (SVM), linear discriminant analysis (LDA) and k- nearest neighbor (k-NN), were applied to classify 90 sampled cocoa beans into three quality grades – fully fermented, partially fermented and non-fermented. CS e-nose (89% ≤ Rp ≤ 94%) and NIR-CDS (85% ≤ Rp ≤ 94%) achieved comparable classification rates; with the systems data cluster analysis yielding cophenetic correlation coefficients of (0.85 - 0.89). Both systems combined with SVM and ELM achieved high classification rate (Rp = 94%) and could be applied to cocoa bean quality classification on in-situ and nondestructive basis. This novel NIR-CDS technique proved a pragmatic approach for the selection of sensitive chemo-dyes used in the fabrication of e-nose colorimetric sensor array compared with the hitherto trial-and-error method, which is time-consuming and dye-wasteful. The technique could also be deployed in near-infrared systems for the detection of volatile (gaseous) compounds, which previously had been a limitation.
Mesoporous silica supported orderly-spaced gold nanoparticles SERS-based sensor for pesticides detection in food
(Elsevier Ltd, 2020) Xu, Y.; Kutsanedzie, F. Y. H.; Hassan, M.; Zhu, J.; Ahmad, W.; Li, H.; Chen, Q.
In this study, a novel sensor fabricated with compactly arranged gold nanoparticles (AuNPs) templated from mesoporous silica film (MSF) via air-water interface has been confirmed as a promising surface-enhanced Raman scattering (SERS) substrate for detecting trace levels of 2,4-dichlorophenoxyacetic acid (2,4-D), pymetrozine and thiamethoxam. The densely arranged AuNPs@MSF had an average AuNPs size of 5.15 nm with small nanogaps (<2nm) between AuNPs, and exhibited a high SERS performance. SERS spectra of pesticides were collected after their adsorption on the AuNPs@MSF. The results showed that the concentration of 2,4-D, pymetrozine and thiamethoxam gave a good linear relationship with SERS intensity. Moreover, the designed SERS-based sensor (AuNPs@MSF) was stable for 3 months with ca. 3% relative standard deviation (RSD) and was applied successfully for the analysis of 2,4-D extraction from both environmental and food samples. The proposed SERS-based sensor was further validated by HPLC and showed satisfactory result (p > 0.05).
Near infrared chemo-responsive dye intermediaries spectra-based in-situ quantification of volatile organic compounds.
(Sensors and Actuators B: Chemical, 2018) Kutsanedzie, F. Y.; Hao, L.; Yan, S.; Ouyang, Q.; Chen, Q.
Volatile organic compounds (VOCs) detection and measurement in materials with near infrared spectroscopy (NIRS) have been an unresolved constraint till date. This paper focused on the use of NIRS for rapid detection and quantification of pure VOCs (ethanol, ethyl acetate and acetic acid) in mixed VOCs via employing sensitive intermediary chemo-responsive dyes as capture probes, whose NIRS spectra were scanned, preprocessed and used to build partial least squares (PLS) prediction models. Average predicted rates based on the PLS-built prediction models for the pure VOCs in the mixed VOCs yielded 98.60 ± 17.41%. 78.26% of the pure VOCs prediction rates ranged between 85 and 114% and normally distributed. The high prediction rates achieved imply the technique may be deployed as a panacea to widen the usage scope of NIRS and e-nose based colorimetric sensors for rapid detection and quantification of VOCs content in materials which hitherto had been a constraint for both systems.
Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution
(Food Chemistry, 2018) Kutsanedzie, F. Y.; Chen, Q.; Hassan, M. M.; Yang, M.; Sun, H.; Rahman, M. H.
Total fungi count (TFC) is a quality indicator of cocoa beans when unmonitored leads to quality and safety problems. Fourier transform near infrared spectroscopy (FT-NIRS) combined with chemometric algorithms like partial least square (PLS); synergy interval-PLS (Si-PLS); synergy interval-genetic algorithm-PLS (Si-GAPLS); Ant colony optimization - PLS (ACO-PLS) and competitive-adaptive reweighted sampling-PLS (CARS-PLS) was employed to predict TFC in cocoa beans neat solution. Model results were evaluated using the correlation coefficients of the prediction (Rp) and calibration (Rc); root mean square error of prediction (RMSEP), and the ratio of sample standard deviation to RMSEP (RPD). The developed models performance yielded 0.951≤Rp≤0.975; and 3.15≤RPD≤4.32. The models' prediction stability improved in the order of PLS
A novel nanoscaled chemo dye–based sensor for the identification of volatile organic compounds during the mildewing process of stored wheat.
(Food Analytical Methods, 2019) Lin, H.; Kang, W.; Kutsanedzie, F. Y.; Chen, Q.
This work presents a novel colorimetric sensor based on nanoscaled chemo dyes which can detect inert volatile organic compounds (VOCs) during the mildewing process of stored wheat. 1-Octen-3-ol and 3-octanone were selected as the marked compounds by gas chromatography mass spectrometry (GC-MS) analysis. In this work, poly(styrene-co-acrylic acid) microbeads were prepared by soap-free emulsion copolymerisation. Boron-dipyrromethene dyes with PSA were fabricated as a novel sensor to obtain digital data before and after exposure to VOCs, and the correlation coefficients (R2) between the digital data and the concentration of VOCs were 0.8078 and 0.8324, respectively. And root mean square errors (RMSEs) were 3.05 g L−1 and 1.65 g L−1, respectively. The data based on the identification of mouldy wheat samples were processed by principal component analysis (PCA) and linear discriminant analysis (LDA). The optimal performance obtained for the LDA model was 83.33% in the prediction set and 90% in the calibration set.
An overview on the applications of typical non-linear algorithms coupled with NIR spectroscopy in food analysis
(Food Engineering Reviews, 2020) Zareef, M.; Chen, Q.; Hassan, M. M.; Arslan, M.; Hashim, M. M.; Ahmad, W.; Kutsanedzie, F. Y.; Agyekum, A. A.
Near-infrared (NIR) spectroscopy as a low-cost technique with its non-destructive fast nature, precision, control, accuracy, repeatability, and reproducibility has been extensively employed in most industries for food quality measurements. Its coupling to different modeling techniques has been identified as a way of improving the accuracy and robustness of non-destructive measurement of foodstuffs. This review provides an overview of the application of non-linear algorithms in food quality and safety specific to NIR spectroscopy. The review also provides in-depth knowledge about the principle of NIR spectroscopy along with different non-linear models such as artificial neural network (ANN), AdaBoost, local algorithm (LA), support vector machine (SVM), and extreme learning machine (ELM). Moreover, non-linear algorithms coupled with NIR spectroscopy for ensuring food quality and their future perspective has been discussed.
Portable spectroscopy system determination of acid value in peanut oil based on variables selection algorithms.
(Measurement, 2017) Yang, M.; Chen, Q.; Kutsanedzie, F. Y.; Yang, X.; Guo, Z.; Ouyang, Q.
The acid value (AV) is an essential parameter for the quality and safety evaluation of peanut oil. In this study, for efficiently and real-time monitor of acid value (AV) in peanut oil, a portable spectroscopy system was first developed and combined with variables selection algorithms to measure acid value (AV) in peanut oils. Developed portable spectroscopy system was applied for transmittance spectrum data acquisition after which partial least squares (PLS) and several variables selection algorithms synergy interval partial least square (Si-PLS), genetic algorithm (GA), genetic algorithm combined with Si-PLS namely GA-Si-PLS, ant colony optimization (ACO) algorithms were systemically studied and comparatively used for modeling. The performances of these models were evaluated according to correlation coefficients squared in the prediction set (RP) and root mean square error of prediction (RMSEP). The results showed that the variables selection methods could select more significant variables and improve the model performance, especially for the GA-Si-PLS model with the best performance than other variables selection algorithms with RP = 0.9426 and RMSEP = 0.2980. Finally, the paper draws a conclusion that the developed portable spectroscopy system combined with a suitable variables selection methods could be used for the simultaneous and rapid measurement of acid value in peanut oil.
Prediction of amino acids, caffeine, theaflavins and water extract in black tea using FT-NIR spectroscopy coupled chemometrics algorithms
(Analytical Methods, 2018) Zareef, M.; Chen, Q.; Ouyang, Q.; Kutsanedzie, F. Y.; Hassan, M. M.; Viswadevarayalu, A.; Wang, A.
Fourier transform near-infrared spectroscopy (FT-NIRS), coupled with chemometrics techniques, was performed as a fast analysis technique to assess the quality of various components in black tea. Four PLS models, namely partial least square (PLS), synergy interval PLS (Si-PLS), genetic algorithm PLS (GA-PLS) and backward interval PLS (Bi-PLS), were established as calibration models for the quantitative prediction of amino acids, caffeine, theaflavins and water extract. The results are reported based on the lower root mean square error of cross prediction (RMSEP) and the root mean square error of cross-validation (RMSECV) as well as their correlation coefficient (R2) in the prediction set (RP) and the calibration set (RC). In addition, on the basis of fewer frequency variables, GA-PLS was found to be the best technique for the quantification of amino acids and water extract and Bi-PLS was found to be the best technique for the quantitative analysis of caffeine and theaflavins in this study. It was observed that NIR spectroscopy can be successfully combined with various chemometric techniques for the rapid identification of the chemical composition of black tea. This study demonstrates that FT-NIR spectroscopy, combined with chemometrics (GA-PLS and Bi-PLS), has the best stability and generalization performance for black tea analysis.
Qualitative and quantitative analysis of chlorpyrifos residues in tea by surface-enhanced Raman spectroscopy (SERS) combined with chemometric models
(Lwt – Food Science and Technology, 2018) Zhu, J.; Agyekum, A. A.; Kutsanedzie, F. Y.; Li, H.; Chen, Q.; Ouyang, Q.; Jiang, H.
Surface-enhanced Raman spectroscopy (SERS) combined with chemometric models were employed to develop a rapid, low-cost, and sensitive method for qualitative and quantitative analysis of chlorpyrifos residues in tea. Au@Ag nanoparticles (NPs) with high enhancement factor were synthesized and coupled with chemometric algorithms for SERS measurements. K-nearest neighbors (KNN) classification models gave the best performance model with high classification rates (90.84–100.00%) achieved. For the quantification models for predicting chlorpyrifos contents, the genetic algorithm-partial least squares (GA-PLS) models and synergy interval partial least squares-genetic algorithm (siPLS-GA) models applied to standard normal variate transformation (SNV) preprocessed training and validation data set showed better prediction performances with excellent regression quality (slope = 0.98–1.00), higher correlation coefficient of determination (r2 = 0.96–0.98), and lower root-mean-square error of prediction (RMSEP = 0.29, 0.31) than other quantification models. Paired sample t-test exhibited no statistically significant difference between the reference values determined by GC-MS and the predicted values in most quantification models. The proposed method would be a more effective and powerful tool for classification and determination of chlorpyrifos (CPS) residues in tea samples.
Quantifying total viable count in pork meat using combined hyperspectral imaging and artificial olfaction techniques.
(Food Analytical Methods, 2016) Li, H.; Kutsanedzie, F. Y.; Zhao, J.; Chen, Q.
Total viable count (TVC) of bacteria is one of the most important indexes in evaluation of quality and safety of meat. This study attempts to quantify the TVC content in pork by combining two nondestructive sensing tools of hyperspectral imaging (HSI) and artificial olfaction system based on the colorimetric sensor array. First, data were acquired using HSI system and colorimetric sensors array, respectively. Then, the individual characteristic variables were extracted from each sensor. Next, principal component analysis (PCA) was used to achieve data fusion based on these characteristic variables from two different sensor data for further multivariate analysis. In developing the models, linear (PLS and stepwise MLR) and nonlinear (BPANN and SVMR) pattern recognition methods were comparatively employed, and they were optimized by cross-validation. Compared with other models, the SVMR model achieved the best result, and the optimum results were achieved with the root mean square error of prediction (RMSEP) = 2.9913 and the determination coefficient (R p ) = 0.9055 in the prediction set. The overall results showed that it has the potential in nondestructive detection of TVC content in pork meat by integrating two nondestructive sensing tools of HSI and colorimetric sensors with SVMR pattern recognition tool.
Rapid and nondestructive quantification of trimethylamine by FT-NIR coupled with chemometric techniques
(Food Analytical Methods, 2019) Agyekum, A. A.; Kutsanedzie, F. Y.; Mintah, B. K.; Annavaram, V.; Zareef, M.; Hassan, M. M.; Chen, Q.
This paper focused on the quick and nondestructive evaluation of trimethylamine (TMA-N) in fish storage which is sequent to its freshness, the key for controlling the quality and safety of fish products by combining Fourier transform near-infrared (FT-NIR) and chemometric techniques. Calibration models of fish freshness were established using three multivariate chemometric methods—partial least square (PLS), synergy interval PLS (Si-PLS), and genetic algorithm PLS (GA-PLS) for quantitative prediction of TMA-N in fish. Results of the developed model were estimated using the correlation coefficients of the prediction (Rp) and calibration (Rc); root mean square error of prediction (RMSEP) and the ratio of sample standard deviation to RMSEP (RPD). The established model’s performance achieved 0.943 ≤ Rp ≤ 0.977 and 4.25 ≤ RPD ≤ 4.30. The model’s prediction strength improved in the order PLS < Si-PLS < GA-PLS. GA-PLS significantly improved the prediction of TMA-N prediction with RMSEC = 5.08 and Rc = 98.28 for the calibration data whereas the prediction set gave an RMSEP = 5.10 and Rp = 97.70. FT-NIR spectroscopy combined with GA-PLS technique may be employed for rapid and non-invasive quantification of TMA-N in fish for monitoring safety and quality.
Rapid Pseudomonas species identification from chicken by integrating colorimetric sensors with near-infrared spectroscopy.
(Food Analytical Methods, 2018) Xu, Y.; Kutsanedzie, F. Y.; Sun, H.; Wang, M.; Chen, Q.; Guo, Z.; Wu, J.
Pseudomonas spp. are the dominant spoilage bacteria which can cause chicken spoilage. Some traditional detection methods are often unsuitable for their rapid real-time detection. Thus, in this paper, a fusion strategy based on colorimetric sensors and near-infrared spectroscopy was applied to rapidly identify Pseudomonas spp. in chicken. First, four different species of Pseudomonas—Pseudomonas gessardii, Pseudomonas psychrophila, Pseudomonas fragi, and Pseudomonas fluorescens—were isolated from putrid chicken, and then, the odor and spectral information of the Pseudomonas species and their mixture were obtained by colorimetric sensors and near-infrared spectroscopy, respectively. Thirty-six odor characteristic variables and 33 spectral characteristic variables were extracted from each technique and used for data fusion based on principal component analysis (PCA). Back-propagation artificial neural network (BP-ANN) was used to build identification model for the discrimination of the different Pseudomonas species. The results showed that the discrimination capability of the model based on data fusion was superior to that based on the two techniques independently, and eventually BP-ANN achieved 100% classification rate by cross-validation and 98.75% classification rate in predication set. This work indicates that the combination of colorimetric sensors and near-infrared spectroscopy is promising for the rapid identification of Pseudomonas species in chicken extract, and hence may be applied towards quality monitoring.
Ratiometric fluorescence detection of Cd2+ and Pb2+ by inner filter-based upconversion nanoparticle-dithizone nanosystem.
(Microchemical Journal, 2019) Chen, M.; Kutsanedzie, F. Y.; Cheng, W.; Li, H.; Chen, Q.
This paper reports a fluorescence sensor based on inner filter effect (IFE) between upconversion nanoparticles (UCNPs) and dithizone for the highly selective and sensitive detection of cadmium ion (Cd2+) and lead ion (Pb2+) in black tea. The fluorescence at 546 nm, 657 nm, 758 nm and 812 nm were obtained and applied as signal indicator upon upconversion nanoparticles excitation at the single wavelength of 980 nm. With the formation of UCNPs-dithizone mixed system pH at 8, the dithizone-Cd2+ complex increases with increasing in concentration of Cd2+, which cause the bathochromic shifting in absorption bands and an upconversion fluorescence (UCF) quenching at 546 nm; at pH 6, the absorption band of dithizone shows a blue shift with addition of Pb2+, leading an upconversion fluorescence recovering at 657 nm. However, in the presence/absence of Cd2+/Pb2+, the fluorescence at 758 nm and 812 nm were not influenced. This implies the Cd2+ and Pb2+ concentration could be monitored based on the fluorescence ratio I546/I758 and I657/I758 respectively. Under optimal condition, the fluorescence show a good linear within the ranges of 0.01 μM–1.0 μM for Cd2+; and 0.025 μM–1.0 μM for Pb2+, with a detection limit of 3.7 and 8.4 nM achieved. The method was applied for Cd2+ and Pb2+ in real sample (black tea and tap water) with recoveries of 99.6% to 108% and RSD value in the range of 0.96 to 1.23 for Cd2+, with recoveries of 96% to 103.2% and RSD value in the range of 0.98 to 1.27 for Pb2+.
rGO-NS SERS-based coupled chemometric prediction of acetamiprid residue in green tea.
(Journal of Food and Drug Analysis, 2019) Hassan, M. M.; Chen, Q.; Kutsanedzie, F. Y.; Li, H.; Zareef, M.; Xu, Y.; Agyekum, A. A.
Pesticide residue in food is of grave concern in recent years. In this paper, a rapid, sensitive, SERS (Surface-enhanced Raman scattering) active reduced-graphene-oxide-gold-nano-star (rGO-NS) nano-composite nanosensor was developed for the detection of acetamiprid (AC) residue in green tea. Different concentrations of AC combined with rGO-NS nano-composite electro-statically, yielded a strong SERS signal linearly with increasing concentration of AC ranging from 1.0 × 10-4 to 1.0 × 103 μg/mL indicating the potential of rGO-NS nano-composite to detect AC in green tea. Genetic algorithm-partial least squares regression (GA-PLS) algorithm was used to develop a quantitative model for AC residue prediction. The GA-PLS model achieved a correlation coefficient (Rc) of 0.9772 and recovery of the real sample of 97.06%-115.88% and RSD of 5.98% using the developed method. The overall results demonstrated that Raman spectroscopy combined with SERS active rGO-NS nano-composite could be utilized to determine AC residue in green tea to achieve quality and safety.
SERS-signal optimised AgNPs-plated-ZnO nanoflower-like structure synthesised for sensing applications
(Physics Letters A, 2019) Jiao, T.; Kutsanedzie, F. Y.; Xu, J.; Viswadevarayalu, A.; Hassan, M. M.; Li, H.; Chen, Q.
AgNPs-plated-ZnO nanoflower (NFs)-like structures (Ag@ZnO NFs) with optimised signals were synthesised via wet chemical method at different temperatures (50–80 °C). The enhancement factors (EFs) computed for the resultant Ag@ZnO ranged between 2.36–8.46×107 obtained at the different temperatures using 4-aminothiophenol (4-ATP). The achieved EF results indicate Ag@ZnO synthesised at 50 °C gave the best enhancement. It was therefore selected, characterised and used to fabricate a SERS-based nanosensor for the detection of 2,4-dichlorophenoxyacetic acid (2,4-D) with a limit of detection (LOD) of 2.87×10−3 μg/L realised.
Viswadevarayalu, A. (2019). Fast sensing of imidacloprid residue in tea using surface-enhanced Raman scattering by comparative multivariate calibration
(Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019) Chen, Q.; Hassan, M. M.; Xu, J.; Zareef, M.; Li, H.; Xu, Y.; Kutsanedzie, F. Y.; Viswadevarayalu, A.
This study focused on the fabrication of a rapid, highly sensitive and inexpensive technique for the quantification of imidacloprid residue in green tea, based on surface-enhanced Raman scattering (SERS) using highly roughned surface flower shaped silver nanostructure (as SERS substrate) coupled with the chemometrics algorithm. The basic principle of this method is imidacloprid yielded SERS signal after adsorption on Ag-NF under laser excitation by the electromagnetic enhancement and the intensity of the peak is proportional to the concentration ranging from 1.0 × 103 to 1.0 × 10-4 μg/mL. Among the models used, the GA-PLS (Genetic algorithm-partial least square) exhibited superiority to quantify imidacloprid residue in green tea. The model achieved Rp (correlation coefficient) of 0.9702 with RPD of 4.95% in the test set and RSD for precision recorded up to 4.50%. Therefore, the proposed sensor could be employed to quantify imidacloprid residue in green tea for the safeguarding of quality and human health.

Browse

Browsing Research Articles by Author "Chen, Q."