Cluster Analysis of Gallstone FT-IR Spectra: Tests on Simulated Mixture Spectra and Comparison between Spectral and Morphological Classification of Human Gallstones

1998 ◽  
Vol 52 (9) ◽  
pp. 1210-1221 ◽  
Author(s):  
Eric Laloum ◽  
Nguyen Quy Dao ◽  
Michel Daudon
Keyword(s):  
Cluster Analysis ◽  
Ir Spectra ◽  
Black Pigment ◽  
Brown Pigment ◽  
Morphological Classification ◽  
Single Linkage ◽  
Homogeneous Groups ◽  
Ft Ir ◽  
Pure Cholesterol

Sixty-four combination spectra of three major gallstone components [i.e., cholesterol, calcium bilirubinate, and calcium carbonate (aragonite)] were simulated in accordance with a “fractal” ternary diagram. Comparison between the original pattern of composition and factorial maps of pretreated spectra makes it possible to show the effects of different normalization procedures (Euclidean norm, spectrum maximum, and area under spectrum set to 1). Cluster analysis of these spectra, depending on different agglomerative links (single linkage, complete linkage, average linkage, and Ward's criterion), was carried out. All the resultant trees yield the same groups, but Ward's criterion best preserves the pattern of the data. More than 100 gallstones from France and Vietnam were classified by using cluster analysis of their FT-IR spectra with Ward's criterion. Seven homogeneous groups of spectra were extracted, which have been significantly correlated to the four morphological types of gallstones: pure cholesterol, mixed cholesterol, brown pigment, and black pigment stones. This analysis also reveals that the morphological groups are not homogeneous in composition, in particular for black pigment stones.

Application of optimization tasks in cluster analysis

10.12737/7483 ◽  
2014 ◽  
Vol 8 (7) ◽  
pp. 0-0
Author(s):  
Олег Сдвижков ◽  
Oleg Sdvizhkov
Keyword(s):  
Cluster Analysis ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Number Of Clusters ◽  
Homogeneous Groups ◽  
Finite Set ◽  
Minimum Number

Cluster analysis [3] is a relatively new branch of mathematics that studies the methods partitioning a set of objects, given a finite set of attributes into homogeneous groups (clusters). Cluster analysis is widely used in psychology, sociology, economics (market segmentation), and many other areas in which there is a problem of classification of objects according to their characteristics. Clustering methods implemented in a package STATISTICA [1] and SPSS [2], they return the partitioning into clusters, clustering and dispersion statistics dendrogram of hierarchical clustering algorithms. MS Excel Macros for main clustering methods and application examples are given in the monograph [5]. One of the central problems of cluster analysis is to define some criteria for the number of clusters, we denote this number by K, into which separated are a given set of objects. There are several dozen approaches [4] to determine the number K. In particular, according to [6], the number of clusters K - minimum number which satisfies where - the minimum value of total dispersion for partitioning into K clusters, N - number of objects. Among the clusters automatically causes the consistent application of abnormal clusters [4]. In 2010, proposed and experimentally validated was a method for obtaining the number of K by applying the density function [4]. The article offers two simple approaches to determining K, where each cluster has at least two objects. In the first number K is determined by the shortest Hamiltonian cycles in the second - through the minimum spanning tree. The examples of clustering with detailed step by step solutions and graphic illustrations are suggested. Shown is the use of macro VBA Excel, which returns the minimum spanning tree to the problems of clustering. The article contains a macro code, with commentaries to the main unit.

Classification of Vegetable Oils by FT-IR

1997 ◽  
Vol 51 (8) ◽  
pp. 1118-1124 ◽  
Author(s):  
Donald B. Dahlberg ◽  
Shawn M. Lee ◽  
Seth J. Wenger ◽  
Julie A. Vargo
Keyword(s):  
Ir Spectra ◽  
Principal Component ◽  
Fast Detection ◽  
Signal Correction ◽  
Ft Ir ◽  
Ir Detection ◽  
The Fourier Transform ◽  
Cooking Oils ◽  
Cis And Trans

The Fourier transform infrared (FT-IR) spectra of 27 brands of 10 types of cooking oils and margarines were measured without temperature control. Attempts to predict the vegetable source and physical properties of these oils failed until wavelength selection and multiplicative signal correction (MSC) were applied to the FT-IR spectra. After pretreatment of the data, principal component analysis (PCA) was totally successful at oil identification, and partial least-squares (PLS) models were able to predict both the refractive indices [standard error of estimation (SEE) 0.0002] and the viscosities (SEE 0.52 cP) of the oils. These models were based predominately on the FT-IR detection of the cis and trans double-bond content of the oils, as well as small amounts of defining impurities in sesame oils. Efforts to use selected wavelengths to discriminate oil sources were only partially successful. These results show the potential utility of FT-IR in the fast detection of substitution or adulteration of products like cooking oils.

Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks

2007 ◽  
Vol 38 (3) ◽  
pp. 303-314 ◽  
Author(s):  
K. Srinivasa Raju ◽  
D. Nagesh Kumar
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Artificial Neural Networks ◽  
Optimal Number ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Analysis ◽  
Homogeneous Groups ◽  
Index Approach ◽  
Artificial Neural

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are considered as the classification criteria for grouping. The optimal number of groups is determined as 14 based on the Davies–Bouldin index approach. It is observed that the FCA approach performed better than the other two methodologies for the present study.

scholarly journals Classification of Five Kinds of Moss Plants with the Use of Fourier Transform Infrared Spectroscopy and Chemometrics

2011 ◽  
Vol 25 (6) ◽  
pp. 271-285 ◽  
Author(s):  
Tao Hu ◽  
Wen-Ying Jin ◽  
Cun-Gui Cheng
Keyword(s):  
Cluster Analysis ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Ir Spectroscopy ◽  
Ir Spectra ◽  
Fourier Transform Infrared Spectroscopy ◽  
Moss Species ◽  
Ft Ir ◽  
And Cluster Analysis ◽  
Ft Ir Spectroscopy

Fourier transform infrared spectroscopy (FT-IR) with Horizontal Attenuated Total Reflectance (HATR) techniques is used to obtain the FT-IR spectra of five kinds of mosses, such asPtychomitrium dentatum(Mitt.) Jaeg.,Ptychomitrium polyphylloides(C. Muell.) Par.,Ptychomitrium sinense(Mitt.) Jaeg.,Macromitrium syntrichophyllumTher. Etp. Vard., andMacromitrium ferrieiCard. Sz Ther. Based on the comparison of the above mosses in the FT-IR spectra, the region ranging from 4000 to 650 cm−1was selected as the characteristic spectra for analysis. Principal component analysis (PCA) and cluster analysis are considered to identify the five moss species. Because they belong to the homogeneous plants, and have similar chemical components and close FT-IR spectroscopy, PCA and cluster analysis can only give a rough result of classification among the five moss species, Fourier self-deconvolution (FSD) and discrete wavelet transform (DWT) methods are used to enhance the differences between them. We use these methods for further study. Results show that it is an excellent method to use FT-IR spectroscopy combined with FSD and DWT to classify the different species in the same family. FT-IR spectroscopy combined with chemometrics, such as FSD and DWT, can be used as an effective tool in systematic research of bryophytes.

scholarly journals On the Identification of Rayon/Viscose as a Major Fraction of Microplastics in the Marine Environment: Discrimination between Natural and Manmade Cellulosic Fibers Using Fourier Transform Infrared Spectroscopy

2016 ◽  
Vol 71 (5) ◽  
pp. 939-950 ◽  
Author(s):  
Ionela Raluca Comnea-Stancu ◽  
Karin Wieland ◽  
Georg Ramer ◽  
Andreas Schwaighofer ◽  
Bernhard Lendl
Keyword(s):  
Cluster Analysis ◽  
Fourier Transform ◽  
Ir Spectra ◽  
Fiber Type ◽  
Major Fraction ◽  
Library Search ◽  
Cellulosic Fibers ◽  
Ft Ir ◽  
Atr Spectroscopy ◽  
Acquisition Method

This work was sparked by the reported identification of man-made cellulosic fibers (rayon/viscose) in the marine environment as a major fraction of plastic litter by Fourier transform infrared (FT-IR) transmission spectroscopy and library search. To assess the plausibility of such findings, both natural and man-made fibers were examined using FT-IR spectroscopy. Spectra acquired by transmission microscopy, attenuated total reflection (ATR) microscopy, and ATR spectroscopy were compared. Library search was employed and results show significant differences in the identification rate depending on the acquisition method of the spectra. Careful selection of search parameters and the choice of spectra acquisition method were found to be essential for optimization of the library search results. When using transmission spectra of fibers and ATR libraries it was not possible to differentiate between man-made and natural fibers. Successful differentiation of natural and man-made cellulosic fibers has been achieved for FT-IR spectra acquired by ATR microscopy and ATR spectroscopy, and application of ATR libraries. As an alternative, chemometric methods such as unsupervised hierarchical cluster analysis, principal component analysis, and partial least squares-discriminant analysis were employed to facilitate identification based on intrinsic relationships of sample spectra and successful discrimination of the fiber type could be achieved. Differences in the ATR spectra depending on the internal reflection element (Ge versus diamond) were observed as expected; however, these did not impair correct classification by chemometric analysis. Moreover, the effects of different levels of humidity on the IR spectra of natural and man-made fibers were investigated, too. It has been found that drying and re-humidification leads to intensity changes of absorption bands of the carbohydrate backbone, but does not impair the identification of the fiber type by library search or cluster analysis.

scholarly journals Classification of Green Bristle Grass, Yellow Foxtail and Chinese Pennisetum Seeds via HATR-FT-IR Combined with Chemometrics

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Peng Yu ◽  
Cha-Yan Wan ◽  
Chang-Shun Wu ◽  
Jia-Ni Shou ◽  
Cun-Gui Cheng
Keyword(s):  
Cluster Analysis ◽  
Ir Spectra ◽  
Chemical Components ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Plant Seeds ◽  
Setaria Faberii ◽  
Similar Chemical ◽  
Ft Ir ◽  
The Difference

Fourier transform infrared (FT-IR) and horizontal attenuated total reflectance (HATR) technique are used to obtain the FT-IR spectra of the seed of green bristle grass (the seed fromSetaria viridis(L.) Beauv), yellow foxtail seed (the seed fromSetaria glauca(L.) Beauv), and the Chinese pennisetum seed (the seed fromSetaria faberiiHerrum). In order to extrude the difference among them, cluster analysis is considered to identify the three kinds of plant seeds. Because they belong to the sibling plant seeds, and have similar chemical components and close FT-IR spectra. The result of Cluster analysis is not satisfactory. The discrete wavelet transformation (DWT) and a support vector machine (SVM) were used for further study. The compression detail 3 and 4 in DWT are used to extract the feature vectors, which are used to train SVM. The trained SVM is used to classify seed of green bristle grass, yellow foxtail seed and Chinese pennisetum seed. The seed samples are collected from different places around the country. With 40 testing samples we could effectively identify the sibling plants, seed of green bristle grass, yellow foxtail seed and Chinese pennisetum seed by FT-IR with discrete wavelet feature extraction and SVM classification.

IDENTIFYING HIGH POTENTIAL ENTREPRENEURS IN A DEVELOPING COUNTRY: A CLUSTER ANALYSIS OF UGANDAN ENTREPRENEURS

2013 ◽  
Vol 18 (02) ◽  
pp. 1350010 ◽  
Author(s):  
ARTHUR SSERWANGA ◽  
GERRIT ROOKS
Keyword(s):  
Cluster Analysis ◽  
Developing Countries ◽  
Developing Country ◽  
Opportunity Recognition ◽  
High Potential ◽  
Homogeneous Groups ◽  
Entrepreneurial Activities ◽  
Systematic Knowledge

It has often been argued that entrepreneurs in developing countries can be classified as either "survival" or "growth-oriented." However, there is little systematic knowledge about classification of entrepreneurs in developing countries. We propose that what we call high potential entrepreneurs can be distinguished from low potential entrepreneurs, given that high potential entrepreneurs recognize and effectively exploit opportunities. In this paper we classify entrepreneurs using three core entrepreneurial activities; opportunity recognition, planning and innovativeness. A cluster analysis of about 700 Ugandan entrepreneurs yielded two natural, distinct and internally homogeneous groups of high potential and low potential entrepreneurship.

Sign in / Sign up

Export Citation Format

Share Document