Research Articles

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Browsing Research Articles by Author "Agyekum, A. A."

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    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).
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    A nanosystem composed of upconversion nanoparticles and N, N-diethyl-p-phenylenediamine for fluorimetric determination of ferric ion.
    (Microchimica Acta, 2018) Chen, M.; Kutsanedzie, F. Y.; Cheng, W.; Agyekum, A. A.; Li, H.; Chen, Q
    A system composed of upconversion nanoparticles (UCNPs) and N,N-diethyl-p-phenylenediamine (EPA) is shown to be a useful probe for highly sensitive and selective fluorometric determination of ferric ion. The fluorescence of the UCNPs (under the 980 nm excitation) has peaks at 546, 657, 758 and 812 nm. EPA is readily oxidized by Fe(III) to generate a dye with a peak at 552 nm. This causes an inner filter effect on the fluorescence peaks at 546 nm, whereas the emissions at 657, 758 and 812 nm remained unchanged. Therefore, the iron concentration can be quantified by measurement of the ratio of fluorescence at 546 and 758. Under optimal condition, the ratio drops linearly in the 0.25 to 50 μM. Fe(III) concentration ranges, with a detection limit of 0.25 μM. The method is highly selective and was applied to the analysis of spiked samples (wastewater) where it gave recoveries of between 100.9 and 107.3%; and RSD values between 0.8 and 1.4%. Results are approximately the same as those obtained by AAS. Graphical abstract A method is presented for fluorimetric determination of Fe(III). Fe(III) reacts with N,N-diethyl-p-phenylenediamine (EPA) to generate EPA oxide. The fluorescence peaking at 546 nm is reduced in presence of oxidized EPA via an inner filter.
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    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.
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    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.
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    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.
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    Rapid Detection and Prediction of Norfloxacin in Fish Using Bimetallic Au@ Ag Nano-Based SERS Sensor Coupled Multivariate Calibration.
    (Food Analytical Methods, 2022) Agyekum, A. A.; Kutsanedzie, F. Y.; Mintah, B. K.; Annavaram, V.; Braimah, A. O.
    Norfloxacin is an antibiotic in the fluoroquinolone family licenced for use in animals. However, residues in animal products can have negative consequences for consumers. As a result, residue detection in various food matrices must be considered. Norfloxacin accumulates in animal-derived foods, causing deleterious consequences in humans such as foetal deformity, renal failure and drug resistance. A built-in SERS-Au@Ag nanosensor coupled with GA-PLS was used to rapidly detect norfloxacin in the specimen of the spiked fish muscles due to the threat to human lives. A detection limit of 2.36 × 10⁻⁵ μg/mL was realized in the spiked fish muscle sample for norfloxacin compared to the European Commission’s maximum threshold level of 100 μg/kg, indicating the sensor’s ability to detect and quantify norfloxacin at a relatively lower level. The recovery rates (RC) and coefficient of variation (CV) measured in the spiked fish muscle samples for norfloxacin analytes and their standard solutions were between 99.70–105.00% and 0.17–5.21%, respectively. The low CV values imply the reproducibility of the obtained data. The constructed model recorded residual predictive deviations (RPD) greater than three (3), demonstrating the robustness and resilience of the developed genetic algorithm-partial least squares (GA-PLS) model. GA-PLS-built models predicted all results within 4.07 s, which indicates the nanosensor’s ability to rapidly detect norfloxacin in fish to guarantee safety and public health. The SERS probe holds promise for rapid quantification of norfloxacin at microgram per milliliter level in fish to guarantee safety in commerce.
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    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.