scholarly journals A three-dimensional principal component analysis approach for exploratory analysis of hyperspectral data: identification of ovarian cancer samples based on Raman microspectroscopy imaging of blood plasma

The Analyst ◽  
2019 ◽  
Vol 144 (7) ◽  
pp. 2312-2319 ◽  
Author(s):  
Camilo L. M. Morais ◽  
Pierre L. Martin-Hirsch ◽  
Francis L. Martin
Keyword(s):  
Ovarian Cancer ◽  
Principal Component Analysis ◽  
Blood Plasma ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Data ◽  
Exploratory Analysis ◽  
Hyperspectral Images ◽  
Analysis Approach

Three-dimensional principal component analysis (3D-PCA) for exploratory analysis of hyperspectral images.

scholarly journals Principal Component Analysis of Munich Functional Developmental Diagnosis

2021 ◽  
Vol 13 (2) ◽  
pp. 227-233
Author(s):  
Grażyna Pazera ◽  
Marta Młodawska ◽  
Jakub Młodawski ◽  
Kamila Klimowska
Keyword(s):  
Principal Component Analysis ◽  
Social Skills ◽  
Motor Development ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Three Dimensions ◽  
Matrix Analysis ◽  
Psychomotor Development ◽  
First Year Of Life

Objectives: Munich Functional Developmental Diagnosis (MFDD) is a scale for assessing the psychomotor development of children in the first months or years of life. The tool is based on standardized tables of physical development and is used to detect developmental deficits. It consists of eight axes on which the following skills are assessed: crawling, sitting, walking, grasping, perception, speaking, speech understanding, social skills. Methods: The study included 110 children in the first year of life examined with the MFDD by the same physician. The score obtained on a given axis was coded as a negative value (defined in months) below the child’s age-specific developmental level. Next, we examined the dimensionality of the scale and the intercorrelation of its axes using polychoric correlation and principal component analysis. Results: Correlation matrix analysis showed high correlation of MFDD axes 1–4, and MFDD 6–8. The PCA identified three principal components consisting of children’s development in the areas of large and small motor skills (axis 1–4), perception (axis 5), active speech, passive speech and social skills (axis 6–8). The three dimensions obtained together account for 80.27% of the total variance. Conclusions: MFDD is a three-dimensional scale that includes motor development, perception, and social skills and speech. There is potential space for reduction in the number of variables in the scale.

Component Analysis: An Aid to the Interpretation of Data

1969 ◽  
Vol 5 (2) ◽  
pp. 151-164 ◽  
Author(s):  
D. A. Holland
Keyword(s):  
Principal Component Analysis ◽  
Statistical Study ◽  
Principal Component ◽  
Component Analysis ◽  
Linear Functions ◽  
Analysis Approach ◽  
Specific Technique ◽  
Mathematical Technique ◽  
Biological Concepts ◽  
General Component

SummaryPrincipal component analysis is a mathematical technique for summarizing a set of related measurements as a set of derived variates, frequently fewer in number, which are definable as independent linear functions of the original measurements. Consideration of their mathematical nature shows that they do not, themselves, necessarily correspond to sensible biological concepts, though they are more amenable to statistical study than the original measurements. Further, by assessing the extent to which they are in accordance with biological hypotheses, or with the results of other, similar, analyses, they can be transformed into other linear functions which are meaningful in the biological sense, or consistent with other results. Thus the specific technique of principal component analysis is developed into a more general component analysis approach. With proper regard for the purpose the analysis is intended to serve and for the mathematical restrictions involved, this approach can lead to a useful condensation of a mass of data, a better under-standing of the observed individuals as entities rather than collections of isolated measurements, and to the formulation of new hypotheses for subsequent examination.

scholarly journals Hyperspectral monitoring of fructose content in marzipan

2018 ◽  
Vol 14 (s1) ◽  
pp. 79-88
Author(s):  
Katalin Badak-Kerti ◽  
Szabina Németh ◽  
Andreas Zitek ◽  
Ferenc Firtha
Keyword(s):  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Spectral Properties ◽  
Oil Content ◽  
Sugar Content ◽  
Principal Component ◽  
Reducing Sugar ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Analysis Technique

In our research marzipan samples of different sugar to almond paste ratios (1:1, 2:1, 3:1) were stored at 17 °C. Reducing sugar content was measured by analytical method, texture analysis was done by penetrometry, electric characteristics were measured by conductometry and hyperspectral images were taken 6–8 times during the 16 days of storage. For statistical analyses (discriminant analysis, principal component analysis) SPSS program was used. According to our findings with the hyperspectral analysis technique, it is possible to identify how long the samples were stored (after production), and to which class (ratio of sugar to almond) the sample belonged. The main wavelengths which gave the best discrimination results among the days of storage were between 960 and 1100 nm. The type of the marzipan was easy to distinguish with the hyperspectral data; the biggest differences were observed at 1200 and 1400 nm, which are connected to the first overtone of C-H bound, therefore correlate with the oil content. The spatial distribution of penetrometric, electric and spectral properties were also characteristic to fructose content. The fructose content of marzipan is difficult to measure by usual optical ways (polarimetry, spectroscopy), but since fructose is hygroscopic, the spatial distribution of spectral properties can be characteristic.

scholarly journals Hyperspectral data compression based upon the principal component analysis

2021 ◽  
Vol 45 (2) ◽  
pp. 235-244
Author(s):  
A.S. Minkin ◽  
O.V. Nikolaeva ◽  
A.A. Russkov
Keyword(s):  
Principal Component Analysis ◽  
Data Compression ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Data ◽  
Gas Absorption ◽  
Absorption Bands ◽  
Spectral Bands ◽  
The Mean ◽  
Retrieval Errors

The paper is aimed at developing an algorithm of hyperspectral data compression that combines small losses with high compression rate. The algorithm relies on a principal component analysis and a method of exhaustion. The principal components are singular vectors of an initial signal matrix, which are found by the method of exhaustion. A retrieved signal matrix is formed in parallel. The process continues until a required retrieval error is attained. The algorithm is described in detail and input and output parameters are specified. Testing is performed using AVIRIS data (Airborne Visible-Infrared Imaging Spectrometer). Three images of differently looking sky (clear sky, partly clouded sky, and overcast skies) are analyzed. For each image, testing is performed for all spectral bands and for a set of bands from which high water-vapour absorption bands are excluded. Retrieval errors versus compression rates are presented. The error formulas include the root mean square deviation, the noise-to-signal ratio, the mean structural similarity index, and the mean relative deviation. It is shown that the retrieval errors decrease by more than an order of magnitude if spectral bands with high gas absorption are disregarded. It is shown that the reason is that weak signals in the absorption bands are measured with great errors, leading to a weak dependence between the spectra in different spatial pixels. A mean cosine distance between the spectra in different spatial pixels is suggested to be used to assess the image compressibility.

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