Dataset Information

Applying novel economic simple green sample preparation procedures on natural and industrial specimens for chromatographic determination of insecticidal residues.

ABSTRACT: Spraying a tertiary blend of the insecticides (hexythiazox, imidacloprid, and thiamethoxam), on tomato fruits, is a routine in agriculture-attentive countries. A simple green sample preparation technique was developed and applied to the field samples. Specific HP-TLC and RP-HPLC methodologies are established to estimate the residual insecticides and applied to the prepared field specimens. In the planner chromatographic methodology, methanol:chloroform:glacial acetic acid:triethyl amine (8.5:1.5:0.2:0.1, v/v) is recommended as a mobile system. The other one is columnar chromatography; acetonitrile: water (20:80, v/v), pH 2.8, is recommended as a mobile system. The validation parameters were examined following the ICH rules. The means percentages and standard deviations of the accuracy of the HP-TLC method for the determined compounds were 99.66 ± 0.974, 99.41 ± 0.950, and 99.89 ± 0.983, correspondingly. The values were 99.24 ± 0.921, 99.69 ± 0.681, and 99.20 ± 0.692, correspondingly, when they were determined by the RP-HPLC method. The relative standard deviation percentages of the methods' repeatability and intermediate precision ranged from 0.389 to 0.920. Both methods were highly specific having resolution factors of ≥ 1.78 and selectivity factors of ≥ 1.71. They were applied to the field samples perfectly.

SUBMITTER: Hegazy AM

PROVIDER: S-EPMC10156696 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Applying novel economic simple green sample preparation procedures on natural and industrial specimens for chromatographic determination of insecticidal residues.

Hegazy Amira M AM Mahmoud Hamada M HM Elsayed Mohamed A MA Ali Nouruddin W NW Abdelfatah Rehab M RM

Scientific reports 20230503 1

Spraying a tertiary blend of the insecticides (hexythiazox, imidacloprid, and thiamethoxam), on tomato fruits, is a routine in agriculture-attentive countries. A simple green sample preparation technique was developed and applied to the field samples. Specific HP-TLC and RP-HPLC methodologies are established to estimate the residual insecticides and applied to the prepared field specimens. In the planner chromatographic methodology, methanol:chloroform:glacial acetic acid:triethyl amine (8.5:1.5 ...[more]

PMID: 37137923

Similar Datasets

Project description:BACKGROUND:Structure-based virtual screening techniques can help to identify new lead structures and complement other screening approaches in drug discovery. Prior to docking, the data (protein crystal structures and ligands) should be prepared with great attention to molecular and chemical details. RESULTS:Using a subset of 18 diverse targets from the recently introduced DEKOIS 2.0 benchmark set library, we found differences in the virtual screening performance of two popular docking tools (GOLD and Glide) when employing two different commercial packages (e.g. MOE and Maestro) for preparing input data. We systematically investigated the possible factors that can be responsible for the found differences in selected sets. For the Angiotensin-I-converting enzyme dataset, preparation of the bioactive molecules clearly exerted the highest influence on VS performance compared to preparation of the decoys or the target structure. The major contributing factors were different protonation states, molecular flexibility, and differences in the input conformation (particularly for cyclic moieties) of bioactives. In addition, score normalization strategies eliminated the biased docking scores shown by GOLD (ChemPLP) for the larger bioactives and produced a better performance. Generalizing these normalization strategies on the 18 DEKOIS 2.0 sets, improved the performances for the majority of GOLD (ChemPLP) docking, while it showed detrimental performances for the majority of Glide (SP) docking. CONCLUSIONS:In conclusion, we exemplify herein possible issues particularly during the preparation stage of molecular data and demonstrate to which extent these issues can cause perturbations in the virtual screening performance. We provide insights into what problems can occur and should be avoided, when generating benchmarks to characterize the virtual screening performance. Particularly, careful selection of an appropriate molecular preparation setup for the bioactive set and the use of score normalization for docking with GOLD (ChemPLP) appear to have a great importance for the screening performance. For virtual screening campaigns, we recommend to invest time and effort into including alternative preparation workflows into the generation of the master library, even at the cost of including multiple representations of each molecule. Graphical AbstractUsing DEKOIS 2.0 benchmark sets in structure-based virtual screening to probe the impact of molecular preparation and score normalization.

Project description:Synaptosomes are frequently used research objects in neurobiology studies focusing on synaptic transmission as they mimic several aspects of the physiological synaptic functions. They contain the whole apparatus for neurotransmission, the presynaptic nerve ending with synaptic vesicles, synaptic mitochondria and often a segment of the postsynaptic membrane along with the postsynaptic density is attached to its outer surface. As being artificial functional organelles, synaptosomes are viable for several hours, retain their activity, membrane potential, and capable to store, release, and reuptake neurotransmitters. Synaptosomes are ideal subjects for proteomic analysis. The recently available separation and protein detection techniques can cope with the reduced complexity of the organelle and enable the simultaneous qualitative and quantitative analysis of thousands of proteins shaping the structural and functional characteristics of the synapse. Synaptosomes are formed during the homogenization of nervous tissue in the isoosmotic milieu and can be isolated from the homogenate by various approaches. Each enrichment method has its own benefits and drawbacks and there is not a single method that is optimal for all research purposes. For a proper proteomic experiment, it is desirable to preserve the native synaptic structure during the isolation procedure and keep the degree of contamination from other organelles or cell types as low as possible. In this article, we examined five synaptosome isolation methods from a proteomic point of view by the means of electron microscopy, Western blot, and liquid chromatography-mass spectrometry to compare their efficiency in the isolation of synaptosomes and depletion of contaminating subcellular structures. In our study, the different isolation procedures led to a largely overlapping pool of proteins with a fairly similar distribution of presynaptic, active zone, synaptic vesicle, and postsynaptic proteins; however, discrete differences were noticeable in individual postsynaptic proteins and in the number of identified transmembrane proteins. Much pronounced variance was observed in the degree of contamination with mitochondrial and glial structures. Therefore, we suggest that in selecting the appropriate isolation method for any neuroproteomics experiment carried out on synaptosomes, the degree and sort/source of contamination should be considered as a primary aspect.

Project description:BackgroundChromatographic peakpicking continues to represent a significant bottleneck in automated LC-MS workflows. Uncontrolled false discovery rates and the lack of manually-calibrated quality metrics require researchers to visually evaluate individual peaks, requiring large amounts of time and breaking replicability. This problem is exacerbated in noisy environmental datasets and for novel separation methods such as hydrophilic interaction columns in metabolomics, creating a demand for a simple, intuitive, and robust metric of peak quality.ResultsHere, we manually labeled four HILIC oceanographic particulate metabolite datasets to assess the performance of individual peak quality metrics. We used these datasets to construct a predictive model calibrated to the likelihood that visual inspection by an MS expert would include a given mass feature in the downstream analysis. We implemented two novel peak quality metrics, a custom signal-to-noise metric and a test of similarity to a bell curve, both calculated from the raw data in the extracted ion chromatogram, and found that these outperformed existing measurements of peak quality. A simple logistic regression model built on two metrics reduced the fraction of false positives in the analysis from 70-80% down to 1-5% and showed minimal overfitting when applied to novel datasets. We then explored the implications of this quality thresholding on the conclusions obtained by the downstream analysis and found that while only 10% of the variance in the dataset could be explained by depth in the default output from the peakpicker, approximately 40% of the variance was explained when restricted to high-quality peaks alone.ConclusionsWe conclude that the poor performance of peakpicking algorithms significantly reduces the power of both univariate and multivariate statistical analyses to detect environmental differences. We demonstrate that simple models built on intuitive metrics and derived from the raw data are more robust and can outperform more complex models when applied to new data. Finally, we show that in properly curated datasets, depth is a major driver of variability in the marine microbial metabolome and identify several interesting metabolite trends for future investigation.

Dataset Information

Applying novel economic simple green sample preparation procedures on natural and industrial specimens for chromatographic determination of insecticidal residues.

Publications

Applying novel economic simple green sample preparation procedures on natural and industrial specimens for chromatographic determination of insecticidal residues.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets