Peak Detection Algorithm Based on Second Derivative Properties for Two Dimensional Ion Mobility Spectrometry Signals

Slodzinski, Rafael; Hildebrand, Lars; Vautz, Wolfgang

doi:10.1007/978-3-642-34471-8_28

Cited by 11 publications

(11 citation statements)

References 21 publications

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“…Furthermore, the possibility of false positive responses in IMS determination may result in the presence of different compounds with a similar 1/ K 0 value. Peak detection algorithm for two‐dimensional analytical data based on derivative algorithm can minimized those limits and second‐derivative algorithm was most used . To increase the discrepancies of these two edible oils, first‐ and second‐derivative algorithm were involved, which were provided by the IMS work station software.…”

Section: Resultsmentioning

confidence: 99%

Rapid detection and separation of olive oil and Camellia oil based on ion mobility spectrometry fingerprints and chemometric models

Liu

et al. 2016

Euro J Lipid Sci & Tech

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A simple and rapid classification model for olive oil and camellia oil was proposed based on ion mobility spectrometry (IMS) fingerprints and chemometric Model (peak detection and random forest algorithm). Results indicated that IMS fingerprint spectra by second-derivative algorithm could completely separate 64 olive oil and 79 Camellia oil samples used in this study by simply calculating the peak area. Random forest algorithm was employed to establish discriminant model for olive oil adulterated by camellia oil. Simulated adulteration detection showed that the accuracy rate of discriminant model is 96.4% as 2 of 55 samples were identified as blending olive oil. All these results suggested that IMS could be an effective method to detect the adulterated olive oils by camellia oil. Practical applicationsCamellia oil is much similar to olive oil no matter in the physicochemical properties and fatty acid profiles. Thereby, olive oil has been one of the most frequent targets for the adulteration by camellia oil. This study aimed to provide a rapid method to detect and separate olive oil and camellia oil by a portable ion mobility spectrometry (IMS) device, by using fingerprints spectra, peak detection (first-derivative and secondderivative algorithm) and random forest algorithm. Results indicated that the classification and discriminant model established in this work was doable for the adulteration detection in the industry. Graphical abstractBased on fingerprints spectra, peak detection (first-derivative and second-derivative algorithm) and random forest algorithm, IMS detection is doable for the rapid detection and separation of olive oil and camellia oil.

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Section: Resultsmentioning

confidence: 99%

Rapid detection and separation of olive oil and Camellia oil based on ion mobility spectrometry fingerprints and chemometric models

Liu

et al. 2016

Euro J Lipid Sci & Tech

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“…derivatives (Arteaga-Falconi et al 2015;Slodzinski et al 2013) especially when considered in conjunction with the structure of p(C) itself. These results are not sensitive to correlations because p(C) does not depend upon whether the observed values are clustered or spread out in time or space.…”

Section: B the Definition And An Application Of The Statistical Heterogeneity Indexmentioning

confidence: 99%

On the Importance of Statistical Homogeneity to the Scaling of Rain

Jameson¹

2019

Journal of Atmospheric and Oceanic Technology

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Scaling studies of rainfall are important for the conversion of observations and numerical model outputs among all the various scales. Two common approaches for determining scaling relations are the Fourier transform of observations and the Fourier transform of a correlation function using the Wiener–Khintchine (WK) theorem. In both methods, the observations must be wide-sense statistically stationary (WSS) in time or wide-sense statistically spatially homogeneous (WSSH) in space so that the correlation function and power spectrum form a Fourier transform pair. The focus here is on developing an explicit understanding for the requirement. Statistically heterogeneous (either in space or time) data can produce serious scaling errors. This work shows that the effects of statistical heterogeneity appear as contributions from cross correlations among all of the distinct contributing rainfall components using either method so that the correlation function and its FFT do not form a transform pair. Moreover, the transform then also depends upon the time and location of the observations so that the “observed” power spectrum no longer represents a “universal” scaling function beyond the observations. An index of statistical heterogeneity (IXH) defined in previous work provides a way of determining whether or not a set of rain data may be considered to be WSS or WSSH. The greater IXH exceeds the null, the more likely the derived power spectrum should not be used for general scaling. Numerical simulations and some observations are used to demonstrate all of these findings.

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“…The first and most obvious one is to look for descending zero crossings of the first derivative. Sometimes, though, the peaks are hidden, so one must resort to looking at the second derivatives (e.g., Arteaga-Falconi et al 2015;Slodzinski et al 2013). Examples of the usefulness of this approach are illustrated below.…”

Section: B Examples Of the Component Detection Proceduresmentioning

confidence: 99%

On the Detection of Statistical Heterogeneity in Rain Measurements

Jameson¹,

Larsen

Kostinski

2018

Journal of Atmospheric and Oceanic Technology

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The application of the Wiener–Khintchine theorem for translating a readily measured correlation function into the variance spectrum, important for scale analyses and for scaling transformations of data, requires that the data be wide-sense homogeneous (stationary), that is, that the first and second moments of the probability distribution of the variable are the same at all times (stationarity) or at all locations (homogeneity) over the entire observed domain. This work provides a heuristic method independent of statistical models for evaluating whether a set of data in rain is wide-sense stationary (WSS). The alternative, statistical heterogeneity, requires 1) that there be no single global mean value and/or 2) that the variance of the variable changes in the domain. Here, the number of global mean values is estimated using a Bayesian inversion approach, while changes in the variance are determined using record counting techniques. An index of statistical heterogeneity (IXH) is proposed for rain such that as its value approaches zero, the more likely the data are wide-sense stationary and the more acceptable is the use of the Wiener–Khintchine theorem. Numerical experiments as well as several examples in real rain demonstrate the potential of IXH to identify statistical homogeneity, heterogeneity, and statistical mixtures. In particular, the examples demonstrate that visual inspections of data alone are insufficient for determining whether they are wide-sense stationary. Furthermore, in this small data collection, statistical heterogeneity was associated with convective rain, while statistical homogeneity appeared in more stratiform or mixed rain events. These tentative associations, however, need further substantiation.

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Peak Detection Algorithm Based on Second Derivative Properties for Two Dimensional Ion Mobility Spectrometry Signals

Cited by 11 publications

References 21 publications

Rapid detection and separation of olive oil and Camellia oil based on ion mobility spectrometry fingerprints and chemometric models

Rapid detection and separation of olive oil and Camellia oil based on ion mobility spectrometry fingerprints and chemometric models

On the Importance of Statistical Homogeneity to the Scaling of Rain

On the Detection of Statistical Heterogeneity in Rain Measurements

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