Socioeconomic disparities in multimorbidity of chronic diseases in the RaNCD cohort study by Roya Safari Faramani, Keyvan Samian, Farid Najafi, Yahya Salimi, Mitra Darbandi

“…A sample of 10,047 adults aged 35–65 years was analyzed. Principal component analysis was utilized to determine socioeconomic status (SES). …”
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Determination of Ca, P, K, Na, and Mg in Australian Retail Pasteurised Milk Using Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP OES) by Duc Doan Nguyen, Vicky Solah, Suzanne Daubney, Saijel Jani

“…The contents of macroelements in 6 retail pasteurised milk samples were between 1099.32 and 1348.65 µg·g−1 (Ca), 914.01 and 1091.21 µg·g−1 (P), 1362.76 and 1549.74 µg·g−1 (K), 288.89 and 323.22 µg·g−1 (Na), and 97.62 and 110.57 µg·g−1 (Mg). Principal component analysis (PCA) revealed that retail pasteurised milk samples were distinctly separated into four groups on the first two principal components (PCs). …”
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Investigating the relationship between physical cognitive tasks and a social cognitive task in a wild bird by Grace Blackburn, Benjamin J. Ashton, Alex Thornton, Holly Hunter, Sarah Woodiss-Field, Amanda R. Ridley

“…In this study, we tested 36 wild Western Australian magpies (Gymnorhina tibicen dorsalis) on a battery of three physical (associative learning, spatial memory, and numerical assessment) and one social (observational spatial memory) cognitive task, to investigate if cognition in this species fits a general cognitive factor model, or instead one of separate physical and social cognitive domains. A principal component analysis (PCA) identified two principal components with eigenvalues exceeding 1; a first component onto which all three physical tasks loaded strongly and positively, and a second component onto which only the social task (observational spatial memory) loaded strongly and positively. …”
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Prediction of Lycii Cortex Quality Marker Based on Network Pharmacology and Chemometrics Methods by Xinrui Wang, Guotong Li, Haoqiang Ding, Xiqin Du, Lanying Zhang, Jingze Zhang, Dailin Liu

“…A UPLC–MS/MS quantitative analysis method was established to determine the contents of 11 components such as kukoamine A in 35 batches of LC from seven producing areas. Principal component analysis, orthogonal partial least squares discriminant analysis, and other mathematical analysis methods were used to screen the differential components. …”
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Monitoring of milk rennet coagulation: Chemical and physical perspective using Raman spectroscopy by Leonardo Sibono, Stefania Tronci, Martin Aage Barsøe Hedegaard, Massimiliano Errico, Massimiliano Grosso

“…Raman sensor information (Laser 785 nm) was simultaneously retrieved with rheological measurements to predict the curd cutting time. Principal component analysis was conducted on Raman spectra to collect the relevant information on the coagulating process. …”
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Organizational and economic aspect of labor protection specialist professional competence formation by E. V. Bakiko, V. V. Fadeeva, V. S. Serdyuk

“…In this study the labor protection specialist professional competence integral concept is defined by the principal component analysis. An integral competence model labor protection professional is developed. …”
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Evaluation of the Exospheric Temperature Modeling From Different Empirical Orthogonal Functions by Xu Yang, Libin Weng, Jiuhou Lei, Xiaoqian Zhu, Haibing Ruan, Dexin Ren, Zhongli Li, Ruoxi Li, Liangjie Chen

“…The EOFs of the exospheric temperature can be derived either from satellite data directly or from the outputs of the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) and MSIS models by applying the Principal Component Analysis method. Then, the thermospheric mass densities calculated from ETM are used to compare with the observed data in order to evaluate the performance of different ETM models. …”
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Use of UTAUT for analyzing the acceptance and use of electric scooters in the public transport system by Fernando Marroquín-Ciendúa, Manuel Medina-Labrador, Laura Camila Hurtado Méndez, Lorena Patricia Mora Hernández, Cristian Nicolás Puentes Guzmán

“…The research was conducted in three phases: (1) Descriptive phase, a qualitative study with in-depth interviews, Exploratory Factor Analysis, and Principal Component Analysis; (2) Correlational phase, a statistical validation of the UTAUT model using survey responses from 424 participants; (3) Predictive phase, two Binary Logistic Regressions and a Log-Linear Analysis assessed the relationships between independent variables (Gender, Socioeconomic Strata, Incentives, and Knowledge of Scooters) and the dependent variable (Scooter Use). …”
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Analysis of Hydrogeochemical Characteristics of Tunnel Groundwater Based on Multivariate Statistical Technology by Chen Peng, Yuanming Liu, Huiyu Chen, Qiaowei Yuan, Qingzhi Chen, Shilong Mei, Zhonghu Wu

“…The representative clusters 1, 3, and 5 were selected for principal component analysis. Clusters 1 and 5 of groundwater represent weathering, dissolution, and ion exchange of carbonate and sulfate rocks and are closely related to the lithologic limestone, limestone intercalated with carbonaceous mudstone, carbonaceous mudstone, and coal-measure strata in the aquifer. …”
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Genetically Modified Crops’ Technology and its Awareness among Smallholder Farmers in Nigeria by Ogheneruemu Obi-Egbedi, Omowunmi Ogungbite, Isaac Oluwatayo

“…Therefore, this study aimed to assess farmers’ awareness for GM crops and the factors that determine their awareness in Oyo state, Nigeria, using primary data collected in 2018 with the aid of well-structured questionnaires from 242 smallholder farmers. Principal component analysis and Tobit regression model were used for data analysis. …”
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Assessment of phenotypic diversity of Ñuña, a local common bean (Phaseolus vulgaris L.) from the northern Andes in Peru by Angel Esteban Santa Cruz Padilla, Jorge Luis Vásquez-Orrillo, Ricardo Manuel Bardales-Lozano, Hipolito Murga-Orrillo

“…For quantitative traits, Principal Component Analysis showed no discrimination between regions of origin but indicated a highly positive correlation between leaf length and width, and between pod length and width, as well as seed length, width, thickness and weight. …”
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A Machine Learning Approach to Predict Air Quality in California by Mauro Castelli, Fabiana Martins Clemente, Aleš Popovič, Sara Silva, Leonardo Vanneschi

“…Using the whole set of available variables revealed a more successful strategy than selecting features using principal component analysis. The presented results demonstrate that SVR with RBF kernel allows us to accurately predict hourly pollutant concentrations, like carbon monoxide, sulfur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, as well as the hourly AQI for the state of California. …”
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Raman spectroscopy in the characterization of autochthonous sweet cherry (Prunus avium L.) cultivars from the Balkan region by Volić Mina M., Obradović Nataša S., Milivojević Ana D., Milošević Nebojša T., Pećinar Ilinka M.

“…Combined with principal component analysis (PCA), Raman spectroscopy was used in assessing nutritionally similar samples, such as the studied sweet cherry cultivars. …”
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Beamforming Feedback-Based Line-of-Sight Identification Toward Firmware-Agnostic WiFi Sensing by Hiroki Shimomura, Koji Yamamoto, Takayuki Nishio, Akihito Taya

“…Focusing on the relationship between singular value decomposition and principal component analysis, we numerically demonstrated that the first column vectors of the BFM reflect the LOS/NLOS conditions. …”
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Evaluating modified atmosphere with variable CO₂-O₂ concentrations for Tribolium castaneum management and quality preservation in Rice storage by Xue Dong, Ming Yang, Peian Tang

“…Quality assessments revealed that TD effectively mitigated lipid oxidation by reducing aldehydes and alcohols, major contributors to rice aging and off-flavors while maintaining acceptable enzymatic activities and VOC profiles. Principal component analysis confirmed that TD minimized oxidative stress and preserved desirable sensory attributes more effectively than hypoxic treatments. …”
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Intraspecific Morphological Variations among the Populations of Milicia excelsa, Pouteria adolfi-friedericii, and Prunus africana in Different Natural Forests of Southwest Ethiopia by Mohammed Adefa Seid, Yigardu Mulatu Mengesha

“…The intraspecific morphological variations among populations of each species were computed using hierarchical clustering and principal component analysis (PCA) with R.4.1.3. A total of 55 trees for M. excelsa in four forests, 232 trees for P. adolfi-friedericii in eight forests, and 184 trees for P. africana in five forests were measured. …”
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Determination of Volatile Organic Compounds in Selected Strains of Cyanobacteria by Ivan Milovanović, Aleksandra Mišan, Jelica Simeunović, Dajana Kovač, Dubravka Jambrec, Anamarija Mandić

“…In addition, multivariate techniques, namely, principal component analysis (PCA) and hierarchical cluster analysis (HCA), were used for making distinction among different microalgal strains. …”
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Protein Quality of African Locust Bean—A High-Value Gathered Tree Food Contributing Protein and Palatability to Plant-Based Diets by Eliot T. Masters, Bokary Allaye Kelly

“…A protein quality test was conducted in the aggregate and for each zone using the WHO/FAO Protein Digestibility-Corrected Amino Acid Score (PDCAAS) method. Principal component analysis (PCA) was used to assess patterns of amino acid compositional variation between the three origins. …”
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Characterization of m6A Methylation Modification Patterns in Colorectal Cancer Determines Prognosis and Tumor Microenvironment Infiltration by Qingfang Yue, Yuan Zhang, Fei Wang, Fei Cao, Jun Bai, Xianglong Duan

“…A comprehensive analysis of the m6A modification profiles of 458 patients with CRC was performed by clustering 21 genes encoding m6A methylation regulators and linking the m6A modification pattern with TME characteristics. Using principal component analysis (PCA), a risk model was constructed to quantify individual m6A modification patterns in patients with CRC. …”
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Characterization of Origin and Evolution of Formation Water in Buried Hill of Jizhong Depression, China, Using Multivariate Statistical Analysis of Geochemical Data by Fei Li, Jianhui Zeng

“…Groundwater samples from buried hill of Jizhong Depression were evaluated using two statistical analyses: hierarchical cluster analysis (HCA) and principal component analysis (PCA). The samples were classified into four clusters, C1–C4, in HCA and the hydrochemical types of C1–C4 are HCO3-Na, Cl·HCO3-Na, Cl-Na, and Cl-Na·Ca. …”
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