Wavelet based classification of MALDI-IMS-MS spectra of serum N-Linked glycans from normal controls and patients diagnosed with Barrett's esophagus, high grade dysplasia, and esophageal adenocarcinoma

Lavine, Barry K.; White, Collin G.; Ding, Tao; Gaye, Maissa M.; Clemmer, David E.

doi:10.1016/j.chemolab.2018.03.008

Cited by 9 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although a PC plot is not a shark knife for discrimination, if we have a PC plot that shows clustering then our experience is that we will be able to predict robustly using this coefficient set. For this study, the model inference was incorporated into the PCKaNN fitness function [28][29][30][31][32][33] to identify variables that minimize the error across the entire model, which is the PC score plot of the wavelet coefficients selected by the pattern recognition GA. This was accomplished by assessing the uncertainty of the scores for each sample in the PC plot using the jackknife 34 to generate estimates of dispersion.…”

Section: Pattern Recognition Analysismentioning

confidence: 99%

Raman Spectroscopy to Enhance Investigative Lead Information in Automotive Clearcoats

et al. 2023

View full text Add to dashboard Cite

A new method to determine the make and model of a vehicle from an automotive paint sample recovered at the crime scene of a vehicle-related fatality such as a hit-and-run using Raman microscopy has been developed. Raman spectra were collected from 118 automotive paint samples from six General Motors (GM) vehicle assembly plants to investigate the discrimination power of Raman spectroscopy for automotive clearcoats using a genetic algorithm for pattern recognition that incorporates model inference and sample error in the variable selection process. Each vehicle assembly plant pertained to a specific vehicle model. The spectral region between 1802 and 697 cm–1 was found to be supportive of the discrimination of these six GM assembly plants. By comparison, only one of the six automotive assembly plants could be differentiated from the other five assembly plants using Fourier transform infrared spectroscopy (FT-IR), which is the most widely used analytical method for the examination of automotive paint) and the genetic algorithm for pattern recognition. The results of this study indicate that Raman spectroscopy in combination with pattern recognition methods offers distinct advantages over FT-IR for the identification and discrimination of automotive clearcoats.

show abstract

Section: Pattern Recognition Analysismentioning

confidence: 99%

Raman Spectroscopy to Enhance Investigative Lead Information in Automotive Clearcoats

et al. 2023

View full text Add to dashboard Cite

show abstract

“…To identify wavelengths characteristic of each class (i.e., the variety of an edible oil), a genetic algorithm (GA) for pattern recognition analysis 26–30 was applied to the Raman spectra. The pattern recognition GA identifies those wavelengths that optimize the separation of the classes in a plot of the two or three largest principal components of the data.…”

Section: Data Preprocessing and Pattern Recognition Analysismentioning

confidence: 99%

Differentiation of Edible Oils by Type Using Raman Spectroscopy and Pattern Recognition Methods

et al. 2020

View full text Add to dashboard Cite

The application of Raman spectroscopy and pattern recognition methods to the problem of discriminating edible oils by type was investigated. Two-hundred and eighty-six Raman spectra obtained from 53 samples spanning 15 varieties of edible oils were collected for 90 s at 2 cm–1 resolution. Employing a Whittaker filter, all Raman spectra were baseline corrected after removing the high-intensity fluorescent background in each spectrum. The Raman spectral data were then examined using the three major types of pattern recognition methodology: mapping and display, discriminant development and clustering. The 15 varieties of edible oils could be partitioned into five distinct groups based on their degree of saturation and the ratio of polyunsaturated fatty acids to monounsaturated fatty acids. Edible oils assigned to one group could be readily differentiated from those assigned to other groups, whereas Raman spectra within the same group more closely resembled each other and therefore would be more difficult to classify by type.

show abstract

“…Wavelength characteristics of the variety of the edible oil were identified using a genetic algorithm for pattern recognition, which takes advantage of both supervised and unsupervised learning to identify spectral features that optimize the separation of the spectra by edible oil type in a plot of the two or three largest PCs of the data. [19][20][21][22][23][24] Because PCs maximize variance, the bulk of the information encoded by the wavelengths selected by the pattern recognition GA was about the differences between the edible oil types. A PC plot that shows separation of the data by edible oil type can only be generated using wavelengths whose variance or information is primarily about the differences between these edible oils.…”

Section: Genetic Algorithm For Pattern Recognition Analysismentioning

confidence: 99%

Authentication of edible oils using an infrared spectral library and digital sample sets: A feasibility study

Uba

White

Booksh

et al. 2023

Journal of Chemometrics

View full text Add to dashboard Cite

A potential method to determine whether two varieties of edible oils can be differentiated by Fourier transform infrared (FTIR) spectroscopy is proposed using digitally generated data of adulterated edible oils from an infrared (IR) spectral library. The first step is the evaluation of digitally blended data sets. Specifically, IR spectra of adulterated edible oils are computed from digitally blending experimental data of the IR spectra of an edible oil and the corresponding adulterant using the appropriate mixing coefficients to achieve the desired level of adulteration. To determine whether two edible oils can be differentiated by FTIR spectroscopy, pure IR spectra of the two edible oils are compared with IR spectra of two edible oils digitally mixed using a genetic algorithm for pattern recognition to solve a ternary classification problem. If the IR spectra of the two edible oils and their binary mixtures are differentiable from principal component plots of the spectral data, then differences between the IR spectra of these two edible oils are of sufficient magnitude to ensure that a reliable classification by FTIR spectroscopy can be obtained. Using this approach, the feasibility of authenticating edible oils such as extra virgin olive oil (EVOO) directly from library spectra is demonstrated. For this study, both digital and experimental data are combined to generate training and validation data sets to assess detection limits in FTIR spectroscopy for the adulterants.

show abstract

Wavelet based classification of MALDI-IMS-MS spectra of serum N-Linked glycans from normal controls and patients diagnosed with Barrett's esophagus, high grade dysplasia, and esophageal adenocarcinoma

Cited by 9 publications

References 31 publications

Raman Spectroscopy to Enhance Investigative Lead Information in Automotive Clearcoats

Raman Spectroscopy to Enhance Investigative Lead Information in Automotive Clearcoats

Differentiation of Edible Oils by Type Using Raman Spectroscopy and Pattern Recognition Methods

Authentication of edible oils using an infrared spectral library and digital sample sets: A feasibility study

Contact Info

Product

Resources

About