Comparison of various data analysis techniques applied for the classification of oligopeptides and amino acids by voltammetric electronic tongue

Głowacz, Klaudia; Wawrzyniak, Urszula E.; Ciosek-Skibińska, Patrycja

doi:10.1016/j.snb.2020.129354

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…[24] Classification of oligopeptides and amino acids by voltammetric electronic tongue. [25] The reviews [3,[26][27][28][29] are useful and interesting to study the application of artificial neural networks and other chemometric methods in the pharmaceutical industry.…”

Section: Pharmaceutical Analysismentioning

confidence: 99%

Application of Artificial Neural Networks for Solving Pharmaceutical Issues

Kaliuzhenko

Pushkarova²

2023

GoS

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Nowadays, modern analytical instruments allow producing great amounts of information for a large number of samples (objects) that can be analyzed in relatively short time. This leads to the availability of multivariate data matrices that require the use of mathematical and statistical procedures, in order to efficiently extract the maximum useful information from data. When processing the data obtained as a result of the chromatographic analysis and various spectroscopic methods, as well as sensory systems, such as the electronic nose and electronic tongue, one cannot avoid applying modern chemometric methods, e.g., pattern recognition and classification algorithms, discriminative analysis, and artificial neural networks.

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Section: Pharmaceutical Analysismentioning

confidence: 99%

Application of Artificial Neural Networks for Solving Pharmaceutical Issues

Kaliuzhenko

Pushkarova²

2023

GoS

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“…Since the catecholamines show the greatest stability at acidic pH and could be oxidized to a quinone species both in neutral and alkaline conditions, a similar mechanism is likely for both norepinephrine and epinephrine [31]. It is also well-known fact that glutathione reacts easily with quinone compounds, which might make the possible mechanism of interaction between catecholamine NTs and QDs-GSH more complex [35]. Although the fundamental investigation on the mechanism of interaction of bioanalytes with GSH capped QDs is beyond the scope of this study, a conclusion can be made that the described effects may be responsible for differ- neurotransmitters, which may be the basis for the detection of these compounds in pattern-based sensing approach.…”

Section: Excitation-emission Fluorescence Response Patterns Of Gsh Capped Quantum Dotsmentioning

confidence: 99%

Excitation-emission fluorescence matrix acquired from glutathione capped CdSeS/ZnS quantum dots in combination with chemometric tools for pattern-based sensing of neurotransmitters

2021

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The presented work concerns pattern-based sensing with quantum dots for the identification and quantification of neurotransmitters by means of excitation-emission fluorescence spectroscopy (2D fluorescence). In the framework of this study, glutathione capped CdSeS/ZnS nanocrystals were used as non-specific nanoreceptors capable of differentiated interaction with neurotransmitters. The pattern-based sensing with QDs was realized by using excitation-emission fluorescence spectroscopy to provide analyte-specific multidimensional optical information. These characteristic fluorescent response patterns were processed by unfolded partial least squares–discriminant analysis, showing that satisfactory identification of all investigated neurotransmitters: dopamine, norepinephrine, epinephrine, serotonin, GABA, and acetylcholine, can be achieved through the proposed sensing strategy. The impact of the considered fluorescence signal (datum, i.e. zeroth-order data acquired per sample; spectrum, i.e. first-order data acquired per sample; excitation-emission matrix, i.e. second-order data acquired per sample) on the sensing capability of glutathione capped QDs was also verified. The best performance parameters such as accuracy, precision, sensitivity, and specificity were obtained using excitation-emission matrices (88.9–93.3%, 0.93–0.95, 0.89–0.93, and 0.99–1.00, respectively). Thus, it was revealed that excitation-emission fluorescence spectroscopy may improve the recognition of neurotransmitters while using only one type of nanoreceptor. Furthermore, is was demonstrated that the proposed excitation-emission fluorescence spectroscopy assisted QD assay coupled with unfolded partial least squares regression can be successfully utilized for quantitative determination of catecholamine neurotransmitters at the micromolar concentration range with R2 in the range 0.916–0.987. Consequently, the proposed sensing strategy has the potential to significantly simplify the sensing element and to expand the pool of bioanalytes so far detectable with the use of QDs. Graphical abstract

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“…Our research group for the first time showed that QDs-assisted EEM fluorescence spectroscopy can be applied to recognize amino acids and several di- and tripeptides [ 6 ]. Then, we used a voltammetric chemical tongue to identify peptides within the group of Pro-Gly, carnosine, glutathione, Aβ 4-16 , and the α-factor N-terminus [ 20 ]. In our last paper, we showed that voltammetric tongue approach enabled recognition of sequence of Aβ 5-9 derivatives, which presents the same Cu(II)-His2 coordination mode, but with modified residues at first and third positions [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Quantum dots-based “chemical tongue” for the discrimination of short-length Aβ peptides

Głowacz,

Drozd,

Tokarska

et al. 2024

Microchim Acta

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A “chemical tongue” is proposed based on thiomalic acid-capped quantum dots (QDs) with signal enrichment provided by excitation-emission matrix (EEM) fluorescence spectroscopy for the determination of close structural analogs—short-length amyloid β (Aβ) peptides related to Alzheimer’s disease. Excellent discrimination is obtained by principal component analysis (PCA) for seven derivatives: Aβ1-16, Aβ4-16, Aβ4-9, Aβ5-16, Aβ5-12, Aβ5-9, Aβ12-16. Detection of Aβ4-16, Aβ4-16, and Aβ5-9 in binary and ternary mixtures performed by QDs-based chemical tongue using partial least squares-discriminant analysis (PLS-DA) provided perfect 100% accuracy for the two studied peptides (Aβ4-16 and Aβ4-16), while for the third one (Aβ5-9) it was slightly lower (97.9%). Successful detection of Aβ4-16 at 1 pmol/mL (1.6 ng/mL) suggests that the detection limit of the proposed method for short-length Aβ peptides can span nanomolar concentrations. This result is highly promising for the development of simple and efficient methods for sequence recognition in short-length peptides and better understanding of mechanisms at the QD-analyte interface. Graphical Abstract

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Comparison of various data analysis techniques applied for the classification of oligopeptides and amino acids by voltammetric electronic tongue

Cited by 7 publications

References 36 publications

Application of Artificial Neural Networks for Solving Pharmaceutical Issues

Application of Artificial Neural Networks for Solving Pharmaceutical Issues

Excitation-emission fluorescence matrix acquired from glutathione capped CdSeS/ZnS quantum dots in combination with chemometric tools for pattern-based sensing of neurotransmitters

Quantum dots-based “chemical tongue” for the discrimination of short-length Aβ peptides

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