Analytical Figures of Merit for Multisensor Arrays

Parastar, Hadi; Kirsanov, Dmitry

doi:10.1021/acssensors.9b02531

Cited by 20 publications

(12 citation statements)

References 39 publications

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“…Another important issue with chemometrics relates to the fact that at the moment, only a few reports are addressing the development of formal analytical figures of merit like sensitivity, selectivity, detection limits for sensors and sensor arrays employing multivariate calibration [ 81 , 82 ]. This lack of formal description hinders both the comparison of different results between each other and an appropriate metrological standardization of the corresponding biosensors and biosensor arrays, thus delaying a massive acceptance and introduction of such methods into routine laboratory practices.…”

Section: Future Perspectivesmentioning

confidence: 99%

Application of Chemometrics in Biosensing: A Brief Review

Martynko

Kirsanov

2020

Biosensors

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The field of biosensing is rapidly developing, and the number of novel sensor architectures and different sensing elements is growing fast. One of the most important features of all biosensors is their very high selectivity stemming from the use of bioreceptor recognition elements. The typical calibration of a biosensor requires simple univariate regression to relate a response value with an analyte concentration. Nevertheless, dealing with complex real-world sample matrices may sometimes lead to undesired interference effects from various components. This is where chemometric tools can do a good job in extracting relevant information, improving selectivity, circumventing a non-linearity in a response. This brief review aims to discuss the motivation for the application of chemometric tools in biosensing and provide some examples of such applications from the recent literature.

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Section: Future Perspectivesmentioning

confidence: 99%

Application of Chemometrics in Biosensing: A Brief Review

Martynko

Kirsanov

2020

Biosensors

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“…By optimizing linear combinations of many characteristic features or variables, the performance in data analysis can be strongly improved compared to UVA. Among many existing multivariate analysis methods, partial least squares (PLS) is one of the most commonly used first-order calibration methods for the prediction of operational conditions (e.g., concentrations in chemometrics), which is a dimensionality reduction technique used for regression modeling . Data is projected in a new subspace of latent variables, which are linear combinations of original features from the SPR curves.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…For simplicity, σ x can be assessed from the residual matrix 26 as the difference between the predicted value P from the univariate calibration model and the original value O, where in this case, P and O refer to the value of the RI. Similarly, in this particular case, γ is estimated as in ref 27.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

Gaussian Beam Shaping and Multivariate Analysis in Plasmonic Sensing

et al. 2020

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This work demonstrates a novel strategy to improve the sensing performance of a prism-coupled surface plasmon resonance system by Gaussian beam shaping and multivariate data analysis. The propagation of the beam along the optical system has been studied using the Gaussian beam approximation to design the incident beam such that the beam waist is aligned precisely and that stability is assured at the metal−dielectric interface. This renders a collimated incident beam, hence least angular dispersion, yielding a stronger and sharper plasmonic resonance. Moreover, we use the multivariate analysis method partial least squares that combines multiple features of the surface plasmon resonance curve and allows for a more precise analysis of the plasmonic response. Compared to univariate analysis, partial least squares improves typical sensing performance parameters remarkably. The combination of both aspects, beam shaping and multivariate analysis, overcomes current limitations of plasmonic detection systems. Thereby, we improve analytical sensitivity by a factor of 16, decrease the prediction error of the concentration of an unknown analyte by a factor of 11, and enhance resolution to the order of 5 × 10 −7 RIU in angular interrogation.

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“…An (electro)chemical reaction occurs within each sensor/recognition element that produces a signal, which is sent to a transducer and is processed afterwards using different statistical and multivariate data treatment techniques. In general, the final result is a conclusion about the quality of the sample [ 1 , 2 , 5 , 7 , 8 ] ( Figure 2 a). In the case of MSA, each sensor/recognition element also senses a target compound by (electro)chemical reaction occurring within the interface of the sensor surface and the analyzed sample.…”

Section: Principles Of Mss and Msamentioning

confidence: 99%

“…At the same time, one of the most common methods used for processing of data obtained by the MSSs during the analysis of solutions is chaos theory [ 11 ]. Recently, many other chemometric methods have been used for the extraction of the required information about a sample, according to the particular task at hand [ 8 , 12 ].…”

Section: Principles Of Mss and Msamentioning

confidence: 99%

Multisensor Systems and Arrays for Medical Applications Employing Naturally-Occurring Compounds and Materials

Pauliukaitė

Voitechovič

2020

Sensors

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The significant improvement of quality of life achieved over the last decades has stimulated the development of new approaches in medicine to take into account the personal needs of each patient. Precision medicine, providing healthcare customization, opens new horizons in the diagnosis, treatment and prevention of numerous diseases. As a consequence, there is a growing demand for novel analytical devices and methods capable of addressing the challenges of precision medicine. For example, various types of sensors or their arrays are highly suitable for simultaneous monitoring of multiple analytes in complex biological media in order to obtain more information about the health status of a patient or to follow the treatment process. Besides, the development of sustainable sensors based on natural chemicals allows reducing their environmental impact. This review is concerned with the application of such analytical platforms in various areas of medicine: analysis of body fluids, wearable sensors, drug manufacturing and screening. The importance and role of naturally-occurring compounds in the development of electrochemical multisensor systems and arrays are discussed.

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Analytical Figures of Merit for Multisensor Arrays

Cited by 20 publications

References 39 publications

Application of Chemometrics in Biosensing: A Brief Review

Application of Chemometrics in Biosensing: A Brief Review

Gaussian Beam Shaping and Multivariate Analysis in Plasmonic Sensing

Multisensor Systems and Arrays for Medical Applications Employing Naturally-Occurring Compounds and Materials

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