2012
DOI: 10.1159/000339487
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Microbiochemical Analysis of Carious Dentine Using Raman and Fluorescence Spectroscopy

Abstract: The aim of this study was to evaluate and correlate objectively the microspectroscopically derived biochemical components of sound, infected and affected carious dentine with their microhardness and autofluorescence (AF) characteristics. Over 3 million high-resolution Raman spectra from 8 extracted human carious teeth were recorded using Raman spectrometer with parallel spectrum acquisition. Green AF signals across each carious lesion from all samples were acquired with a similar spatial resolution using confo… Show more

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Cited by 72 publications
(76 citation statements)
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“…It resulted in the calculation of a new coordinate system whereby variations of the dataset is described via new axes, principal components (PCs). The K-means clustering is a method of analysis based on a centroid model which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (Almahdy et al, 2012). The natural groups of components (or data) based on some similarity and the centroids of a group of data sets were found by the clustering algorithm once calculated by the software.…”
Section: Microtensile Bond Strength (Mtbs)mentioning
confidence: 99%
“…It resulted in the calculation of a new coordinate system whereby variations of the dataset is described via new axes, principal components (PCs). The K-means clustering is a method of analysis based on a centroid model which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (Almahdy et al, 2012). The natural groups of components (or data) based on some similarity and the centroids of a group of data sets were found by the clustering algorithm once calculated by the software.…”
Section: Microtensile Bond Strength (Mtbs)mentioning
confidence: 99%
“…It resulted in the calculation of a new coordinate system whereby variations of the dataset is described via new axes, principal components (PCs). The K-means clustering is a method of analysis based on a centroid model which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean [29]. The natural groups of components (or data) based on some similarity and the centroids of a group of data sets were found by the clustering algorithm once calculated by the software.…”
Section: Raman Spectroscopy and Cluster Analysismentioning
confidence: 99%
“…KMC analysis was displayed in the groups "loading square" and "hold 72 h" from their special graphic significance. The K-means clustering is a method of cluster analysis based on a centroid model which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (Almahdy et al, 2012). The natural groups of components (or data) based on some similarity and the centroids of a group of data sets were found by the clustering algorithm once calculated by the software.…”
Section: 2mentioning
confidence: 99%