A pilot study on colonic mucosal tissues by fluorescence spectroscopy technique

Discrimination by principal component analysis (PCA) and artificial neural network (ANN) analysis

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Abstract

Pulsed laser-induced autofluorescence spectra of pathologically certified normal and malignant colonic mucosal tissues were recorded at 325 nm excitation. The spectra were analysed using three different methods for discrimination purposes. First, all the spectra were subjected to the principal component analysis (PCA) and the discrimination between normal and malignant cases were achieved using parameters like, spectral residuals, Mahatanobis distance and scores of factors. Second, to understand the changes in tissue composition between the two classes (normal, and malignant), difference spectrum was constructed by subtracting mean spectrum of calibration set samples from simulated mean of all spectra of any one class (normal/malignant) and in third, artificial neural network (ANN) analysis was carried out on the same set of spectral data by training the network with spectral features like, mean, median, spectral residual, energy, standard deviation, number of peaks for different thresholds (100,250 and 500) after carrying out 1 st-order differentiation of the training set samples and discrimination between normal and malignant conditions were achieved. The specificity and sensitivity were determined in PCA and ANN analyses and they were found to be 100 and 91.3% in PCA, and 100 and 93.47% in ANN, respectively.

Original languageEnglish
Pages (from-to)408-416
Number of pages9
JournalJournal of Chemometrics
Volume22
Issue number6
DOIs
Publication statusPublished - 06-2008

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Fluorescence Spectroscopy
Fluorescence spectroscopy
Network Analysis
Electric network analysis
Principal component analysis
Principal Component Analysis
Discrimination
Artificial Neural Network
Neural networks
Pulsed lasers
Autofluorescence
Calibration
Tissue
Pulsed Laser
Chemical analysis
Standard deviation
Specificity
Mucous Membrane
Excitation
Energy

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Applied Mathematics

Cite this

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title = "A pilot study on colonic mucosal tissues by fluorescence spectroscopy technique: Discrimination by principal component analysis (PCA) and artificial neural network (ANN) analysis",
abstract = "Pulsed laser-induced autofluorescence spectra of pathologically certified normal and malignant colonic mucosal tissues were recorded at 325 nm excitation. The spectra were analysed using three different methods for discrimination purposes. First, all the spectra were subjected to the principal component analysis (PCA) and the discrimination between normal and malignant cases were achieved using parameters like, spectral residuals, Mahatanobis distance and scores of factors. Second, to understand the changes in tissue composition between the two classes (normal, and malignant), difference spectrum was constructed by subtracting mean spectrum of calibration set samples from simulated mean of all spectra of any one class (normal/malignant) and in third, artificial neural network (ANN) analysis was carried out on the same set of spectral data by training the network with spectral features like, mean, median, spectral residual, energy, standard deviation, number of peaks for different thresholds (100,250 and 500) after carrying out 1 st-order differentiation of the training set samples and discrimination between normal and malignant conditions were achieved. The specificity and sensitivity were determined in PCA and ANN analyses and they were found to be 100 and 91.3{\%} in PCA, and 100 and 93.47{\%} in ANN, respectively.",
author = "Kamath, {Sudha D.} and D'souza, {Claretta S.} and Stanley Mathew and George, {Sajan D.} and C. Santhosh and Mahato, {K. K.}",
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AU - Santhosh, C.

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AB - Pulsed laser-induced autofluorescence spectra of pathologically certified normal and malignant colonic mucosal tissues were recorded at 325 nm excitation. The spectra were analysed using three different methods for discrimination purposes. First, all the spectra were subjected to the principal component analysis (PCA) and the discrimination between normal and malignant cases were achieved using parameters like, spectral residuals, Mahatanobis distance and scores of factors. Second, to understand the changes in tissue composition between the two classes (normal, and malignant), difference spectrum was constructed by subtracting mean spectrum of calibration set samples from simulated mean of all spectra of any one class (normal/malignant) and in third, artificial neural network (ANN) analysis was carried out on the same set of spectral data by training the network with spectral features like, mean, median, spectral residual, energy, standard deviation, number of peaks for different thresholds (100,250 and 500) after carrying out 1 st-order differentiation of the training set samples and discrimination between normal and malignant conditions were achieved. The specificity and sensitivity were determined in PCA and ANN analyses and they were found to be 100 and 91.3% in PCA, and 100 and 93.47% in ANN, respectively.

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