Principal Components Analysis for the Interpretation of Humidification Phenomena—Preliminary Results

Moisture is one of the major causes of building decay, compromising the indoor air quality and the durability of building components. Infrared thermography is a non-destructive technique that can be used to prevent damage caused by the presence of water. In this study, the potential of Principal Component Analysis (PCA) was assessed as a quantitative technique for processing the thermal images captured during 24 hours of water absorption by a lightweight concrete specimen. PCA showed that the first principal component is the one that better explains the phenomenon. The initial instant was the most interesting to identify the water level in the sample and the final instant allowed defining the first 8h as the most relevant.

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Biodegradable and Compostable Shopping Bags under Investigation by FTIR Spectroscopy

Applied Sciences ◽

10.3390/app11020621 ◽

2021 ◽

Vol 11 (2) ◽

pp. 621

Author(s):

Silvana Alfei ◽

Anna Maria Schito ◽

Guendalina Zuccari

Keyword(s):

Principal Component Analysis ◽

Ftir Spectroscopy ◽

Principal Component ◽

Poly Lactic Acid ◽

Chemometric Methods ◽

Industrial Sectors ◽

Fresh Food ◽

Extruded Films ◽

Productive Process ◽

Non Destructive

In the recent years, plastic-based shopping bags have become irregular and progressively replaced by compostable ones. To be marketed, these “new plastics” must possess suitable requirements verified by specific bodies, which grant the conformity mark, and the approved physicochemical properties are periodically verified. The fast, inexpensive, non-destructive, easy to use, and reproducible Fourier-Transform infrared (FTIR) spectroscopy is a technique routinely applied to perform analysis in various industrial sectors. To get reliable information from spectral data, chemometric methods, such as Principal Component Analysis (PCA), are commonly suggested. In this context, PCA was herein performed on 4, 5, and 21 × 3251 matrices, collecting the FTIR data from regular and irregular shopping bags, including three freshly extruded films from the Italian industry MecPlast, to predict their compliance with legislation. The results allowed us to unequivocally achieve such information and to classify the bags as suitable for containing fresh food in bulk or only for transport. A self-validated linear model was developed capable to estimate, by acquiring a single FTIR spectrum if, after the productive process, the content of renewable poly-lactic-acid (PLA) in a new produced film respect the expectations. Surprisingly, our findings established that among the grocery bags available on the market, irregular plastic-based shopping bags continue to survive.

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Measuring the Systematic Risk of Sectors within the US Market Via Principal Components Analysis: Before and during the COVID-19 Pandemic

10.5772/intechopen.101860 ◽

2022 ◽

Author(s):

Jaime González Maiz Jiménez ◽

Adán Reyes Santiago

Keyword(s):

Principal Component Analysis ◽

Stock Market ◽

Principal Components Analysis ◽

Principal Components ◽

Systematic Risk ◽

Principal Component ◽

Component Analysis ◽

Market Capitalization ◽

The Us ◽

Components Analysis

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

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Analysis of infrared spectra of blood serum of patients with multiple myeloma

Journal of Physics Conference Series ◽

10.1088/1742-6596/2103/1/012052 ◽

2021 ◽

Vol 2103 (1) ◽

pp. 012052

Author(s):

D A Chernyshev ◽

E S Mikhailets ◽

E A Telnaya ◽

L V Plotnikova ◽

A D Garifullin ◽

...

Keyword(s):

Multiple Myeloma ◽

Principal Component Analysis ◽

Infrared Spectroscopy ◽

Blood Serum ◽

Infrared Spectra ◽

Early Stage ◽

Principal Component ◽

Healthy Donors ◽

Components Analysis ◽

Serious Disease

Abstract Multiple myeloma (MM) is a serious disease that is difficult to diagnose especially at early stage. Infrared spectroscopy is a promising approach for diagnosing MM. The principal component analysis (PCA) allows us to reduce the dimension of the data and keep only the important variables. In this study, we apply principal components analysis to infrared (IR) spectra of blood serum from healthy donors and multiple myeloma patients. As a result of the analysis by PCA, it was possible to visualize the separation of patient’s and donor’s samples into two clusters. The result indicates that this method is potentially applicable for diagnosis of multiple myeloma.

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Host Selection in Tomicus piniperda L.: Composition of Monoterpene Hydrocarbons in Relation to Attack Frequency in the Shoot Feeding Phase

Zeitschrift für Naturforschung C ◽

10.1515/znc-2006-5-622 ◽

2006 ◽

Vol 61 (5-6) ◽

pp. 439-444 ◽

Cited By ~ 9

Author(s):

Ann-Charlotte Almquist ◽

Jenny Fäldt ◽

Annie Yart ◽

Yohann Chevet ◽

Daniel Sauvard ◽

...

Keyword(s):

Principal Component Analysis ◽

Host Selection ◽

Principal Component ◽

Tomicus Piniperda ◽

Host Volatiles ◽

Principal Component Analysis Plot ◽

Attack Frequency ◽

Pine Shoot Beetle ◽

Monoterpene Hydrocarbons ◽

The One

The aim of this study was to investigate the host selection capacity of the pine shoot beetle, Tomicus piniperda, in the shoot-feeding phase and analyze the chiral and non-chiral host volatiles by means of GC-MS and 2D-GC in five Pinus species originating from France (Pinus sylvestris, P. halepensis, P. nigra laricio, P. pinaster maritima, P. pinaster mesogeensis). Dominating monoterpenes were (-)-α-pinene, (+)-α-pinene, (-)-β-pinene and (+)-3-carene. The amounts of the enantiomers varied considerably within and among the species. In a principal component analysis-plot, based on the absolute amounts of 18 monoterpene hydrocarbons, separation of the pine species into two groups was obtained. P. halepensis and P. sylvestris were grouped according to the amount of (+)-α-pinene and (+)-3-carene, while P. nigra laricio, P. pinaster maritima and P. pinaster mesogeensis were grouped according to (-)-α-pinene and (D)-β-pinene. P. nigra laricio was the species most attacked and P. halepensis the one least attacked by T. piniperda.

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Independent components analysis as an alternative to principal component analysis and discriminant analysis algorithms in the processing of spectrometric data

Journal of Analytical Chemistry ◽

10.1134/s1061934815090117 ◽

2015 ◽

Vol 70 (9) ◽

pp. 1055-1061 ◽

Cited By ~ 8

Author(s):

Yu. B. Monakhova ◽

A. M. Tsikin ◽

S. P. Mushtakova

Keyword(s):

Principal Component Analysis ◽

Discriminant Analysis ◽

Principal Component ◽

Component Analysis ◽

Independent Components Analysis ◽

Spectrometric Data ◽

Independent Components ◽

Components Analysis

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The Ethics of Tax Evasion: Relevant Demographic Variables within the scope of Higher Education Students and Professors.

Archives of Business Research ◽

10.14738/abr.810.9128 ◽

2020 ◽

Vol 8 (10) ◽

pp. 24-37

Author(s):

César Ribeiro ◽

Carlos Santos Pinho

Keyword(s):

Higher Education ◽

Principal Component Analysis ◽

Tax Evasion ◽

Principal Component ◽

Older Men ◽

Demographic Variables ◽

Education Students ◽

Input Matrix ◽

The One ◽

Official Information

The purpose of our study is to determine the depth of various arguments that have emerged to justify tax evasion as an ethical procedure considering several demographic variables. Data collection was done using a questionnaire addressed to professors and students of higher management and non-management courses. This instrument was based on the 18 statements reflecting the three views of tax evasion ethics used by McGee and Benk [1]. Using a 5-point Likert scale, it is intended to evaluate whether the arguments contained in the statements have an effect on the perception of tax evasion as an ethical procedure and whether the previous effect varies according to age, sex, bachelor degree and income level. A universe of 406,980 individuals was determined using official information (sample: 384 individuals). Principal Component Analysis was used, as well as the Kaiser-Meyer-Olkin Statistics in order to measure the adequacy of the input matrix. After the extraction of the components three variables were identified: “Always Ethical”, “Waste, Corruption and Injustice” and “Discrimination and Oppressive Regimes” (Cronbach's Alpha results: 0.887, 0.85 and 0.862). “Discrimination and Oppressive Regimes” is the one that has values closest to “totally agree” that tax evasion is ethical. In general, older men with higher incomes tend to disagree about the ethics of tax evasion. The originality of the study is reflected in the controversial relationship between Ethics and Evasion and the source of the data collected. Interacting with professors and students allows the business and academic components to be combined.

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A method of dimensionality reduction by selection of components in principal component analysis for text classification

Filomat ◽

10.2298/fil1805499z ◽

2018 ◽

Vol 32 (5) ◽

pp. 1499-1506 ◽

Cited By ~ 1

Author(s):

Yangwu Zhang ◽

Guohe Li ◽

Heng Zong

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Dimensionality Reduction ◽

Principal Components Analysis ◽

Text Classification ◽

Principal Components ◽

Principal Component ◽

Method Of Dimensionality Reduction ◽

Components Analysis ◽

Selection Of

Dimensionality reduction, including feature extraction and selection, is one of the key points for text classification. In this paper, we propose a mixed method of dimensionality reduction constructed by principal components analysis and the selection of components. Principal components analysis is a method of feature extraction. Not all of the components in principal component analysis contribute to classification, because PCA objective is not a form of discriminant analysis (see, e.g. Jolliffe, 2002). In this context, we present a function of components selection, which returns the useful components for classification by the indicators of the performances on the different subsets of the components. Compared to traditional methods of feature selection, SVM classifiers trained on selected components show improved classification performance and a reduction in computational overhead.

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Ranking Cultivated Blueberry for Mummy Berry Blight and Fruit Infection Incidence Using Resampling and Principal Components Analysis

HortScience ◽

10.21273/hortsci.45.8.1205 ◽

2010 ◽

Vol 45 (8) ◽

pp. 1205-1210 ◽

Cited By ~ 9

Author(s):

Mark K. Ehlenfeldt ◽

James J. Polashock ◽

Allan W. Stretch ◽

Matthew Kramer

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

The Other ◽

Breeding Strategies ◽

Relative Resistance ◽

Term Variation ◽

Components Analysis ◽

Infection Stage ◽

Selection Of

Mummy berry disease of blueberry has two distinct phases: a blighting phase that infects emerging shoots and leaves early in the spring and a flower infection phase that ultimately leads to infected (mummified) fruit. Cultivated blueberry (Vaccinium spp.) genotypes that are resistant to one phase are not necessarily resistant to the other phase. The resistance of cultivated blueberry (Vaccinium spp.) genotypes to each phase of the disease is different. A large number of cultivars were screened for resistance to each phase. Cultivar standards (cultivars with well-documented responses to the disease) were used in the screening to evaluate long-term variation and aid comparisons across years. Using nine standards for the blight phase, 125 cultivars were tested and ranked for relative resistance using a ranking system based on resampling and principal component analysis. Similarly, using six standards for the flower/fruit infection stage, 110 blueberry cultivars were tested and ranked for relative resistance. Cultivar rankings show that lowbush cultivars and other types possessing high percentages of lowbush germplasm are generally more resistant to both phases of the disease. Among highbush cultivars, Bluejay is reliably resistant to both phases. Documentation of resistance to each phase will allow selection of cultivars for planting in affected areas and will facilitate the development of breeding strategies to produce cultivars with superior resistance.

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Psychotic Symptoms Predicting Evidence Integration in Schizophrenia

Zeitschrift für Psychologie ◽

10.1027/2151-2604/a000334 ◽

2018 ◽

Vol 226 (3) ◽

pp. 174-181 ◽

Cited By ~ 3

Author(s):

Jessica Luk ◽

Kendra Underhill ◽

Todd S. Woodward

Keyword(s):

Principal Component Analysis ◽

Cognitive Bias ◽

Principal Component ◽

Thought Disorder ◽

Psychotic Symptoms ◽

Component Structure ◽

Single Measure ◽

Patient Groups ◽

The One ◽

Evidence Integration

Abstract. A bias against disconfirmatory evidence (BADE) is a cognitive bias associated with delusions in schizophrenia. Previous studies reporting an association between reduced evidence integration and delusions used a single measure of delusion severity, typically to form patient groups. In the current study we perform an exploratory analysis to investigate whether BADE is specific to delusions or extends to other symptoms of psychosis. To address this, we used constrained principal component analysis (CPCA) on four merged BADE studies on schizophrenia to explore the component structure in the BADE task measures that is predictable from symptoms. A component reflecting evidence integration emerged, and was predicted by delusions as expected, but also by thought disorder. This provides novel methodology for cognitive neuropsychiatric investigations into the underpinnings of the symptoms of schizophrenia by enabling investigators to consider a range of symptoms alongside the one that is the target of their investigation.

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