The use of software packages of R factoextra and FactoMineR and their application in principal component analysis for authentication of oils

Several oils have been reported as nutritional source and providing potential benefits for human life. Oil adulteration becomes major issue due to economical attempt to reduce the price of high cost oils. The employment of FTIR spectroscopy combined with Principal Component Analysis (PCA) technique can be applied in oils authentication study. Two of R software packages namely factoextra and FactoMineR were exploited to perform PCA for analysis sixteen various oils from market in Yogyakarta, Indonesia. The results showed that PCA model have been successfully generated using these two statistical packages. Individual plot, variable plot, and biplot were presented to visualize the PCA model. It was also proved that extra virgin olive oil (EVOO) has similar chemical characteristics to palm oil (PO) as reported in the previous study.

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Validation of Fluorescence Spectroscopy to Detect Adulteration of Edible Oil in Extra Virgin Olive Oil (EVOO) by Applying Chemometrics

Applied Spectroscopy ◽

10.1177/0003702818768485 ◽

2018 ◽

Vol 72 (9) ◽

pp. 1371-1379 ◽

Cited By ~ 13

Author(s):

Hina Ali ◽

Muhammad Saleem ◽

Muhammad Ramzan Anser ◽

Saranjam Khan ◽

Rahat Ullah ◽

...

Keyword(s):

Principal Component Analysis ◽

Fluorescence Spectroscopy ◽

Olive Oil ◽

Standard Error ◽

Sunflower Oil ◽

Virgin Olive Oil ◽

Principal Component ◽

Extra Virgin Olive Oil ◽

Edible Oil ◽

Pls Regression

Due to high price and nutritional values of extra virgin olive oil (EVOO), it is vulnerable to adulteration internationally. Refined oil or other vegetable oils are commonly blended with EVOO and to unmask such fraud, quick, and reliable technique needs to be standardized and developed. Therefore, in this study, adulteration of edible oil (sunflower oil) is made with pure EVOO and analyzed using fluorescence spectroscopy (excitation wavelength at 350 nm) in conjunction with principal component analysis (PCA) and partial least squares (PLS) regression. Fluorescent spectra contain fingerprints of chlorophyll and carotenoids that are characteristics of EVOO and differentiated it from sunflower oil. A broad intense hump corresponding to conjugated hydroperoxides is seen in sunflower oil in the range of 441–489 nm with the maximum at 469 nm whereas pure EVOO has low intensity doublet peaks in this region at 441 nm and 469 nm. Visible changes in spectra are observed in adulterated EVOO by increasing the concentration of sunflower oil, with an increase in doublet peak and correspondingly decrease in chlorophyll peak intensity. Principal component analysis showed a distinct clustering of adulterated samples of different concentrations. Subsequently, the PLS regression model was best fitted over the complete data set on the basis of coefficient of determination (R2), standard error of calibration (SEC), and standard error of prediction (SEP) of values 0.99, 0.617, and 0.623 respectively. In addition to adulterant, test samples and imported commercial brands of EVOO were also used for prediction and validation of the models. Fluorescence spectroscopy combined with chemometrics showed its robustness to identify and quantify the specified adulterant in pure EVOO.

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Focused Principal Component Analysis: a graphical method for exploring dietary patterns

Cadernos de Saúde Pública ◽

10.1590/s0102-311x2010001100016 ◽

2010 ◽

Vol 26 (11) ◽

pp. 2149-2156 ◽

Cited By ~ 7

Author(s):

Raquel Canuto ◽

Suzi Camey ◽

Denise P. Gigante ◽

Ana M. B. Menezes ◽

Maria Teresa Anselmo Olinto

Keyword(s):

Principal Component Analysis ◽

Dietary Patterns ◽

Fast Food ◽

Principal Component ◽

Component Analysis ◽

R Software ◽

Animal Fats ◽

Whole Wheat ◽

Show Evidence ◽

Insight Into

The aim of the present study was to introduce Focused Principal Component Analysis (FPCA) as a novel exploratory method for providing insight into dietary patterns that emerge based on a given characteristic of the sample. To demonstrate the use of FPCA, we used a database of 1,968 adults. Food intake was obtained using a food frequency questionnaire covering 26 food items. The focus variables used for analysis were age, income, and schooling. All analyses were carried out using R software. The graphs generated show evidence of socioeconomic inequities in dietary patterns. Intake of whole-wheat foods, fruit, and vegetables was positively correlated with income and schooling, whereas for refined cereals, animal fats (lard), and white bread this correlation was negative. Age was inversely associated with intake of fast-food and processed foods and directly associated with a pattern that included fruit, green salads, and other vegetables. In an easy and direct fashion, FPCA allowed us to visualize dietary patterns based on a given focus variable.

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New Student Drug Tests at College Using Principal Component Analysis Method

IT JOURNAL RESEARCH AND DEVELOPMENT ◽

10.25299/itjrd.2022.7583 ◽

2021 ◽

pp. 98-108

Author(s):

Agnes Chrisnalia ◽

Edwar Ali ◽

Mardainis Mardainis ◽

Rahmiati Rahmiati

Keyword(s):

Principal Component Analysis ◽

Drug Users ◽

Human Life ◽

Principal Component ◽

Component Analysis ◽

Physical Characteristics ◽

People Who Use Drugs ◽

Analysis Method ◽

Principal Component Analysis Method ◽

New Student

Drugs are substances or illegal drugs that can endanger human life. Someone who consumes it in an inappropriate way will become dependent and even result in death. The physical characteristics of people who use drugs vary, but the more obvious characteristics are on the faces of drug users such as red eyes, stiff facial muscles, dark spots, pupils susceptible to light, sunken face shape, and dullness. The lack of physical characteristics of drug users due to similarities with other diseases makes it difficult for people to recognize them initially. However, for users whose face data has been tracked by the National Narcotics Agency, the facial data is stored in the dataset. This research was conducted with the aim of building a system that can detect and recognize prospective students whether they have ever been included in drug users recorded in the National Narcotics Agency dataset or not as one of the requirements for new student admissions to universities. The system built using the Principal Component Analysis method to process and extract images of the physical characteristics of drug users through the facial image data of drug users stored in the dataset. If the detected face has similarities with the characteristics in the dataset, it is necessary to suspect that the detected face is a drug user. The results of this study are the system is able to detect the faces of drug users using the Principal Component Analysis method with an accuracy of 90% and it is hoped that with this research the system can be one solution in helping universities as an identification effort to minimize drug use so that it can be an additional identification tool which strengthens someone detected using drugs.

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Principal component analysis of C-SAR images for flood mapping – Santa Fé province, Argentina

Territorium Revista Portuguesa de riscos prevenção e segurança ◽

10.14195/1647-7723_27-2_4 ◽

2020 ◽

pp. 41-48

Author(s):

Jones Zamboni Graosque ◽

Laurindo Antônio Guasselli

Keyword(s):

Principal Component Analysis ◽

Human Life ◽

Principal Component ◽

Component Analysis ◽

Santa Fe ◽

Flood Events ◽

Sar Images ◽

Pixel Intensity ◽

Pca Method ◽

Flooded Area

Flood events are phenomena associated with heavy rainfall. In Argentina, floods have high economic and social costs, including loss of human life. In this paper, principal component analysis (PCA) is used to map flood-prone areas along the Paraná river in Santa Fe, Argentina. The Sentinel-1B (S1B) images, sensor C-SAR with VH polarisation Interferometric type (IW) Ground Range Detected (GRD) with spatial resolution of 10 m, from 2016, were referenced and the PCA method was used to extract the four first principal components. The flood-affected images make it possible to accurately define the flooded area. In targets with dense vegetation, however, there is no pixel backscatter pattern. PC2 better highlighted the threshold of pixel intensity, with an accuracy of 70%, and 93% of the mapped area was shown to be flood-prone. Procedures to map floods remotely are pivotal because they can quickly obtain precise data on flood areas that may not be accessible for fieldwork or that have not yet been mapped in great detail.

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A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽

10.1024/0301-1526/a000218 ◽

2012 ◽

Vol 41 (5) ◽

pp. 333-342 ◽

Cited By ~ 3

Author(s):

Kirchberger ◽

Finger ◽

Müller-Bühl

Keyword(s):

Principal Component Analysis ◽

Intermittent Claudication ◽

Completion Time ◽

Short Form ◽

Principal Component ◽

Component Analysis ◽

German Version ◽

Average Completion Time ◽

Sf 36 ◽

Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbachs alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbachs alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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