Kinetic Spectrophotometric Method and Neural Network Model Application for the Quantitation of Epinephrine by Starch-capped AgNPs Sensor in Blood and Urine

Pargari, Maryam; Marahel, Farzaneh; Goodajdar, Bijan Mombeni

doi:10.1134/s1061934822040074

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…At present, SVM algorithm is widely used in classification applications, but it is still difficult to implement large-scale training samples. Decision tree algorithm uses the decision tree algorithm to realize the classification of network loans, ECG signals, and financial crisis prediction models, but the decision tree algorithm has the problems of pruning and overfitting under large data samples [16]. K-means algorithm uses K-means to realize the classification of text and plants, but the algorithm has the problem that the K value has a great impact on the classification effect but cannot be obtained accurately [17].…”

Section: Realization Of Identification Process and Results Outputmentioning

confidence: 99%

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Chen

2022

Journal of Sensors

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In order to solve the problems of low efficiency, high consumption of human and time resources, and low degree of intelligence in the current financial abnormal data detection system in computerized accounting, this paper proposes a financial abnormal data monitoring and analysis algorithm based on data mining and neural network. The analysis algorithm uses the method of data mining to process the original financial data, remove invalid information, retain valuable information, and standardize the data to solve the problem of large labor and time consumption. Then, neural network-related algorithms are used to identify the anomalies of standardized data, so as to realize the intelligent early warning of financial abnormal data. Compared with the financial audit algorithm in traditional accounting computerization, this algorithm has the advantages of high efficiency, low energy consumption, and high intelligence. The test results show that the classification accuracy of the proposed algorithm for abnormal data can reach more than 90%. It is proved that the algorithm is effective and improves the efficiency at the same time. The classification error rate of the classifier designed in this paper is 22.5%, and the accuracy rate is 77.5%. Both estimated and actual values represent the number of times, and there is no physical unit. The experiment shows that the main reason for the error rate is the delay of the inspection results of financial abnormalities. Through the example analysis, it can be concluded that the proposed intelligent analysis method of financial abnormal data based on deep learning has good effectiveness and accuracy and has a certain practical value.

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Section: Realization Of Identification Process and Results Outputmentioning

confidence: 99%

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Chen

2022

Journal of Sensors

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“…Furthermore, the previous colorimetric methods require a synthetic and a functionalization step for the preparation of metal NPs prior to use, which make the procedure tedious and time-consuming. 17 Hence, we have reported an "in situ" generation of Ag NPs for sensing of EP.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired from this, we have designed and synthesized a novel thiazole-based ligand, TBH , for the functionalization of Ag + ions, stabilization of the generated Ag NPs, and simultaneous colorimetric detection of EP. Furthermore, the previous colorimetric methods require a synthetic and a functionalization step for the preparation of metal NPs prior to use, which make the procedure tedious and time-consuming . Hence, we have reported an “in situ” generation of Ag NPs for sensing of EP.…”

Section: Introductionmentioning

confidence: 99%

Thiazole-Based Silver Ion Sensor for Sequential Colorimetric Visualization of Epinephrine in the Brain Tissues of an Alzheimer’s Disease Model of Mouse

Srivastava,

Kumar,

Kumar

et al. 2024

ACS Appl. Bio Mater.

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A thiazole-based probe, N′-((2-aminothiazol-5-yl)methylene)benzohydrazide (TBH), has been efficiently synthesized and characterized for the selective and sensitive detection of the neurotransmitter epinephrine (EP). The sensing strategy is based on the use of TBH for sequential colorimetric sensing of Ag + and EP via in situ formation of Ag nanoparticles (Ag NPs) from the TBH-Ag + complex. The generated Ag NPs lead to a bathochromic shift in absorption maximum and a change in color of the solution from light brown to reddish brown. TBH-Ag + shows remarkable selectivity toward EP versus other drugs, common cations, anions, and some biomolecules. Moreover, TBH-Ag + has a low detection limit for EP at 1.2 nM. The coordination of TBH-Ag + has been proposed based on Job's plot, Fourier transform infrared spectroscopy (FT-IR), high-resolution mass spectrometry (HRMS), 1 H NMR titration, X-ray photoelectron spectroscopy (XPS), energy-dispersive X-ray analysis (EDAX), and density functional theory (DFT) studies. The composition and morphology of the generated Ag NPs have been analyzed by XPS, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and dynamic light scattering (DLS). The proposed sensing mechanism for EP has been supported by XPS of Ag after the reaction. Further, the sensitivity of TBH-Ag + toward EP in brain tissues of an Alzheimer's disease model of mouse has been evaluated. A thorough comparison was done for evaluation of the proposed method.

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“…Similarly, fluorescent fingerprinting methods have been refined toward addition of fluorophores to samples [ 7 , 8 ]. A current trend in optical fingerprinting involves another dimension: taking into account a time course of color and fluorescent reactions [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. The use of the kinetic factor makes the fingerprinting technique more powerful.…”

Section: Introductionmentioning

confidence: 99%

A Reaction-Based Optical Fingerprinting Strategy for the Recognition of Fat-Soluble Samples: Discrimination of Motor Oils

Pypin,

Shik,

Stepanova

et al. 2023

Sensors

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Optical “fingerprints” are widely used for chemometrics-assisted recognition of samples of different types. An emerging trend in this area is the transition from obtaining “static” spectral data to reactions analyzed over time. Indicator reactions are usually carried out in aqueous solutions; in this study, we developed reactions that proceed in an organic solvent, thereby making it possible to recognize fat-soluble samples. In this capacity, we used 5W40, 10W40, and 5W30 motor oils from four manufacturers, with six samples in total. The procedure involved mixing a dye, sample, and reagents (HNO3, HCl, or tert-butyl hydroperoxide) in an ethanolic solution in a 96-well plate and measuring absorbance or near-infrared fluorescence intensity every several minutes for 20–55 min. The obtained photographic images were processed by linear discriminant analysis (LDA) and the k-nearest neighbors algorithm (kNN). Discrimination accuracy was evaluated by a validation procedure. A reaction of oxidation of a dye by nitric acid allowed us to recognize all six samples with 100% accuracy for LDA. Merging of data from the four reactions that did not provide complete discrimination ensured an accuracy of 93% for kNN. The newly developed indicator systems have good prospects for the discrimination of other fat-soluble samples. Overall, the results confirm the viability of the kinetics-based discrimination strategy.

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Kinetic Spectrophotometric Method and Neural Network Model Application for the Quantitation of Epinephrine by Starch-capped AgNPs Sensor in Blood and Urine

Cited by 7 publications

References 28 publications

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Thiazole-Based Silver Ion Sensor for Sequential Colorimetric Visualization of Epinephrine in the Brain Tissues of an Alzheimer’s Disease Model of Mouse

A Reaction-Based Optical Fingerprinting Strategy for the Recognition of Fat-Soluble Samples: Discrimination of Motor Oils

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