Abstract:In spite of a rapid growth of data processing software, that has allowed for a huge advancement in many fields of chemistry, some research issues still remain problematic. A standard example of a troublesome challenge is the analysis of multi-component mixtures. The classical approach to such a problem consists of separating each component from a sample and performing individual measurements. The advent of computers, however, gave rise to a relatively new domain of data processing – chemometry – focused on dec… Show more
“…5) is expected for the functions that are continuous and absolutely integrable (Eq. 4) upon N-periodic self-replication, 39 the Authors suggest extrapolating the signal according to the following expression, empirically developed by them for this purpose.Figure 3.Practical implementation of the Fourier transform to differentiation of the ultraviolet–visible (UV–Vis) absorption spectrum of anthracene (data taken from the previous work by Kałka and Turek 41 ). Prior to the calculation of the FFT, the original signal y (black solid line) has to be extrapolated y ex in such a way that it reaches zero value at its edge values (black dotted line).…”
Section: Resultsmentioning
confidence: 99%
“…Practical implementation of the Fourier transform to differentiation of the ultraviolet–visible (UV–Vis) absorption spectrum of anthracene (data taken from the previous work by Kałka and Turek 41 ). Prior to the calculation of the FFT, the original signal y (black solid line) has to be extrapolated y ex in such a way that it reaches zero value at its edge values (black dotted line).…”
Section: Resultsmentioning
confidence: 99%
“…The Fourier approach to spectral processing, as well as several other concepts, 3,33 namely, by Norris–Williams, 3,35 Savitzky–Golay, 9,32 and Whittaker (Eilers), 11,19 have been successfully implemented by the Authors in a form of two shared functional routines (see Supplemental Material) coded in the well-known Matlab environment that is particularly suitable for scientific calculations (and is thus expected to be also compatible with the open source GNU Octave software 40 ) and validated on the actual spectral datasets (e.g., Kałka and Turek 41 ). The first script is dedicated to smoothing of the signal (), while the other has been programmed with regard to the numerical derivative calculation ().…”
An elegant, well-established effective data filter concept, proposed originally by Abraham Savitzky and Marcel J.E. Golay, is undoubtedly a very effective tool, however not free from limitations and drawbacks. Despite the latter, over the years it has become a "monopolist” in many fields of spectra processing, claiming a "commercial" superiority over alternative approaches, which would potentially allow to obtain equivalent or in some cases even more reliable results. In order to show that basic operations performed on spectral datasets, like smoothing or differentiation, do not have to be equated to the application of the one particular single algorithm, several of such alternatives are briefly presented within this paper and discussed with regard to their practical realization. A special emphasis is put on the fast Fourier methodology (FFT), being widespread in the general domain of signal processing. Finally, a user-friendly Matlab routine, in which the outlined algorithms are implemented, is shared, so that one can select and apply the technique of spectral data processing more adequate for their individual requirements without the need to code it prior to use.
“…5) is expected for the functions that are continuous and absolutely integrable (Eq. 4) upon N-periodic self-replication, 39 the Authors suggest extrapolating the signal according to the following expression, empirically developed by them for this purpose.Figure 3.Practical implementation of the Fourier transform to differentiation of the ultraviolet–visible (UV–Vis) absorption spectrum of anthracene (data taken from the previous work by Kałka and Turek 41 ). Prior to the calculation of the FFT, the original signal y (black solid line) has to be extrapolated y ex in such a way that it reaches zero value at its edge values (black dotted line).…”
Section: Resultsmentioning
confidence: 99%
“…Practical implementation of the Fourier transform to differentiation of the ultraviolet–visible (UV–Vis) absorption spectrum of anthracene (data taken from the previous work by Kałka and Turek 41 ). Prior to the calculation of the FFT, the original signal y (black solid line) has to be extrapolated y ex in such a way that it reaches zero value at its edge values (black dotted line).…”
Section: Resultsmentioning
confidence: 99%
“…The Fourier approach to spectral processing, as well as several other concepts, 3,33 namely, by Norris–Williams, 3,35 Savitzky–Golay, 9,32 and Whittaker (Eilers), 11,19 have been successfully implemented by the Authors in a form of two shared functional routines (see Supplemental Material) coded in the well-known Matlab environment that is particularly suitable for scientific calculations (and is thus expected to be also compatible with the open source GNU Octave software 40 ) and validated on the actual spectral datasets (e.g., Kałka and Turek 41 ). The first script is dedicated to smoothing of the signal (), while the other has been programmed with regard to the numerical derivative calculation ().…”
An elegant, well-established effective data filter concept, proposed originally by Abraham Savitzky and Marcel J.E. Golay, is undoubtedly a very effective tool, however not free from limitations and drawbacks. Despite the latter, over the years it has become a "monopolist” in many fields of spectra processing, claiming a "commercial" superiority over alternative approaches, which would potentially allow to obtain equivalent or in some cases even more reliable results. In order to show that basic operations performed on spectral datasets, like smoothing or differentiation, do not have to be equated to the application of the one particular single algorithm, several of such alternatives are briefly presented within this paper and discussed with regard to their practical realization. A special emphasis is put on the fast Fourier methodology (FFT), being widespread in the general domain of signal processing. Finally, a user-friendly Matlab routine, in which the outlined algorithms are implemented, is shared, so that one can select and apply the technique of spectral data processing more adequate for their individual requirements without the need to code it prior to use.
“…Conveniently, the use of mathematical and statistical methods, such as chemometric models, improves the acquisition of information through a PL data set and could easily circumvent possible selectivity issues. Additionally, these widely used methods could resolve the spectrum overlapping, allowing the combination of several fluorophores in the same probe [ 10 , 11 ].…”
The combination of multiple quantum dots (QDs) in a multi-emitter nanoprobe can be envisaged as a promising sensing scheme, as it enables obtaining a collective response of individual emitters towards a given analyte and allows for achieving specific analyte-response profiles. The processing of these profiles using adequate chemometric methods empowers a more sensitive, reliable and selective determination of the target analyte. In this work, we developed a kinetic fluorometric method consisting of a dual CdTe/AgInS2 quantum dots photoluminescence probe for the determination of acetylsalicylic acid (ASA). The fluorometric response was acquired as second-order time-based excitation/emission matrices that were subsequently processed using chemometric methods seeking to assure the second-order advantage. The data obtained in this work are considered second-order data as they have a three-dimensional size, I × J × K (where I represents the samples’ number, J the fluorescence emission wavelength while K represents the time). In order to select the most adequate chemometric method regarding the obtained data structure, different chemometric models were tested, namely unfolded partial least squares (U-PLS), N-way partial least squares (N-PLS), multilayer feed-forward neural networks (MLF-NNs) and radial basis function neural networks (RBF-NNs).
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