scholarly journals Monitoring the change in colour of meat: A comparison of traditional and kernel-based orthogonal transformations

2012 ◽  
Author(s):  
Asger Christiansen ◽  
Jens Carstensen ◽  
Flemming Møller ◽  
Allan Nielsen
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Orthogonal Transformation ◽  
Rate Of Change ◽  
Objective Method ◽  
Components Analysis ◽  
The Difference ◽  
Noise Fraction ◽  
Minimum Noise ◽  
Over Time

Currently, no objective method exists for estimating the rate of change in the colour of meat. Consequently, the purpose of this work is to develop a procedure capable of monitoring the change in colour of meat over time, environment and ingredients. This provides a useful tool to determine which storage environments and ingredients a manufacturer should add to meat to reduce the rate of change in colour. The procedure consists of taking multi-spectral images of a piece of meat as a function of time, clustering the pixels of these images into categories, including several types of meat, and extracting colour information from each category. The focus has primarily been on achieving an accurate categorisation since this is crucial to develop a useful method. The categorisation is done by applying an orthogonal transformation followed by k-means clustering. The purpose of the orthogonal transformation is to reduce the noise and amount of data while enhancing the difference between the categories. The orthogonal transformations principal components analysis, minimum noise fraction analysis and kernel-based versions of these have been applied to test which produce the most accurate categorisation.

scholarly journals Cloud Masking for Remotely Sensed Data Using Spectral and Principal Components Analysis

2012 ◽  
Vol 2 (3) ◽  
pp. 221-225 ◽  
Author(s):  
A. Ahmad ◽  
S. Quegan
Keyword(s):  
Spectral Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Principal Component ◽  
Geographical Information ◽  
Remotely Sensed Data ◽  
Tropical Conditions ◽  
Components Analysis ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

Two methods of cloud masking tuned to tropical conditions have been developed, based on spectral analysis and Principal Components Analysis (PCA) of Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the spectral approach, thresholds were applied to four reflective bands (1, 2, 3, and 4), three thermal bands (29, 31 and 32), the band 2/band 1 ratio, and the difference between band 29 and 31 in order to detect clouds. The PCA approach applied a threshold to the first principal component derived from the seven quantities used for spectral analysis. Cloud detections were compared with the standard MODIS cloud mask, and their accuracy was assessed using reference images and geographical information on the study area.

scholarly journals Status of Education System: Interactions of Input and Output Composite Indicators

2017 ◽  
Vol 56 (1) ◽  
pp. 31-40
Author(s):  
Dovilė Stumbrienė ◽  
Audronė Jakaitienė ◽  
Rimantas Želvys
Keyword(s):  
Principal Components Analysis ◽  
Educational System ◽  
Principal Components ◽  
Education System ◽  
The State ◽  
Composite Indicators ◽  
Components Analysis ◽  
The Baltic ◽  
The Impact ◽  
Over Time

Education is one of keystones that guarantees well-being of a country, therefore the understanding about the educationsystem status might be crucial. It motivates to measure the state of the education system, to understand its determinants and tomonitor changes over time that would allow the implementation of evidence-based education policy. Measurement and assessment ofthe state of the education system is a complex task, as the analysis of individual indicators of the educational system is insufficient tomonitor and evaluate education as a multidimensional phenomenon. To achieve a comprehensive and generalized assessment of theeducation system, we have chosen to calculate the composite indicators, namely, indicators of resources and outcomes. Using thelatter indicators we evaluate state of resources and output of the educational system, understand the factors, determining the state, andcompare it over time and in the context of other countries. Indices were calculated for the Baltic countries and three “old” EUmember states: UK representing the Anglo-Saxon liberal model, Germany for the Continental corporatist model and Finland as anexample of the Scandinavian model. For the analysis we used 2002-2014 annual publicly available data from EUROSTAT, OECD,and IEA databases. We have employed a simple weighted additive method with equal weights and principal components analysis forthe construction of indices. We have found that the differences between composite indicators, constructed by the simple weightedadditive method with equal and principal components analysis weights, are limited. The increase in the number of sub-indicators byalmost two-thirds does not affect dynamics of the output indices over time. We have established that inertia of the education systemis different for the countries: the impact of the output on the results is observed with 2–4 year lag for the Baltic States, as there is notime lag or there is a one year lag for Germany and the United Kingdom. Finland's results are different as compared with the othercountries examined. The dynamics of the Baltic indices is similar and possibly constitutes a separate group.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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