MOX Nanosensors to Detect Colorectal Cancer Relapses from Patient’s Blood at Three Years Follow-Up, and Gender Correlation by Michele Astolfi, Giulia Zonta, Cesare Malagù, Gabriele Anania, Giorgio Rispoli

“…Data was scrutinized by using statistical standard techniques to highlight their statistical differences, and through principal component analysis and support vector machine to classify them. …”
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Impact of capacity building through learning, training, and coaching on agricultural innovation. by Learnmore Mwadzingeni, Martin Dandira, Dumisani Kutywayo, Liboster Mwadzingeni, Andrew Chiwawa, Mulala Danny Simatele

“…Descriptive statistics, factor analysis, principal component analysis (PCA), correlation analysis, and binary logistic regression analysis were employed. …”
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Variables Selection from the Patterns of the Features Applied to Spectroscopic Data—An Application Case by José L. Romero-Béjar, Francisco Javier Esquivel, José Antonio Esquivel

“…Spectrometers provide a large amount of data, the so-called “big data” type, which requires the use of multivariate statistical techniques, mainly principal component analysis, cluster analysis, and discriminant analysis. …”
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Changes of Volatile Flavor Compounds in Different Parts of Tricholoma matsutake during Shelf Life Analyzed by Electronic Nose Combined with Gas Chromatography-Ion Mobility Spectrom... by MENG Fan, WANG Yiru, ZHAO Yuechen, GUAN Wenqiang, WANG Xuan, HE Xingxing, YAO Gang, HU Xin

“…In order to investigate trends in the odor profiles of different parts (cap and stipe) of Tricholoma matsutake during the shelf-life, the present study used an electronic nose and gas chromatography-ion mobility spectrometry (GC-IMS) combined with principal component analysis (PCA) and cluster analysis to analyze the changes of volatile flavor substances in T. matsutake at different points of the shelf-life. …”
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Rapid Determination of the Freshness of Lotus Seeds Using Surface Desorption Atmospheric Pressure Chemical Ionization-Mass Spectrometry with Multivariate Analyses by Yunyang Chi, Liping Luo, Xueyong Huang, Meng Cui, Ximo Dai, Yingbin Hao, Xiali Guo, Huolin Luo

“…The obtained data were processed by principal component analysis (PCA) and backpropagation artificial neural networks (BP-ANNs). …”
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Quality Assessment of Different Species and Differently Prepared Slices of Zedoray Rhizome by High-Performance Liquid Chromatography and Colorimeter with the Aid of Chemometrics by Fei Sun, Xiaolu Yang, Fei Liu, Ying Zhang, Shumei Wang, Hui Cao, Jiang Meng

“…Then, the collected data were analyzed by the principal component analysis and Pearson correlation analysis. …”
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Classification of Different Blueberry Cultivars by Analysis of Physical Factors, Chemical and Nutritional Ingredients, and Antioxidant Capacities by Juan Song, Qiong Shi, Si-Min Yan, Hai-Yan Fu, Si-Zhan Wu, Lu Xu

“…Using the autoscaled data of quality factors, unsupervised principal component analysis was performed for exploratory analysis of intercultivar differences and the influences of quality factors. …”
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Effect of cooking processes on tilapia aroma and potential umami perception by Danni Zhang, Charfedinne Ayed, Ian D. Fisk, Yuan Liu

“…Their aroma profiles were analysed with special focus on off-notes and umami-enhancing odorants by principal component analysis, and correlated with the heating time, colour, moisture and water activity by partial least squares regression analysis. …”
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Temporal and Spatial Variation of Potential Evaporation and Its Influencing Factors in Pearl River Basin from 1980 to 2019 by ZUO Daxing, ZANG Chuanfu, WANG Lina

“…Evaporation is an important link in the water cycle,and in-depth studies on the influence of various meteorological factors on evaporation changes are of great significance for climate change,ecological restoration,and utilization of water resources.In this study,the Penman-Monteith model modified by the Food and Agriculture Organization of the United Nations (FAO) is used to calculate the potential evaporation (ET₀) in the Pearl River Basin from 1980 to 2019.The Mann-Kendall non-parametric test method and inverse distance weighting method are used to analyze the spatial and temporal distribution characteristics of the ET₀,and the principal component analysis method is used to analyze the influencing factors.The results show that:① The average ET₀ of the Pearl River Basin is 1 145.8 mm,and the growth rate is 1.37mm/a,which shows an increasing trend.In terms of four seasons,the growth rate increases in spring (0.56 mm/a),autumn (0.4 mm/a),and winter (0.44 mm/a) but decreases in summer (-0.03 mm/a).② In terms of spatial distribution,the annual average ET₀ of the Pearl River Basin is high in the east,west,and south but low in the middle andnorth.The distribution of ET₀in summer,autumn,and winter is similar.However,the distribution of ET₀ in spring is opposite to that in summer,autumn,and winter.③ Temperature,average water vapor pressure,and altitude are the leading factors of ET₀ changes.Factors such as temperature,average water vapor pressure,sunshine duration,and wind speed make positive contributions to ET₀,while altitude,latitude,and relative humidity make negative contributions to ET₀ changes.…”
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Social behavior and spatial orientation in rat strains with genetic predisposition to catatonia (GC) and stereotypes (PM) by V. S. Plekanchuk, O. I. Prokudina, M. A. Ryazanova

“…In addition, principal component analysis revealed a negative association between catatonic freezing and the socialization index in the three-chamber test. …”
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3D Primary Geochemical Halo Modeling and Its Application to the Ore Prediction of the Jiama Polymetallic Deposit, Tibet, China by Zhongping Tao, Bingli Liu, Ke Guo, Na Guo, Cheng Li, Yao Xia, Yaohua Luo

“…Integrated geochemical anomalies were extracted by kernel principal component analysis, which has the advantage of accommodating nonlinear data. …”
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Which uncertainty measures matter for corporate financialization? Evidence from China intensive polluted industries by Mingyao Wang, Normaziah Mohd Nor, Norhuda Abdul Rahim, Faisal Khan, Yuxiang Cheng

“…Utilizing a panel dataset of Chinese A-share listed companies from 2011Q1 to 2022Q4, this study applies Principal Component Analysis (PCA) to construct a multidimensional index of financialization, followed by panel regression analysis. …”
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Soilscapes from Byers Peninsula, Maritime Antarctic: landform-lithology controls in soil formation by JÔNATAS PEDRO DA SILVA, MÁRCIO R. FRANCELINO, ANDRÉ LUIZ L. DE FARIA, HEITOR P. PALMA, ISABELLE DE A. OLIVEIRA, JOSÉ JOÃO L.L. DE SOUZA, CARLOS ERNESTO G.R. SCHAEFER

“…Thirty-seven soil profiles were classified, collected, and chemically and physically analyzed. Principal component analysis explored relationships among variables in these profiles and 36 others from the Peninsula project database. …”
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Rapid Assessment of Exercise State through Athlete’s Urine Using Temperature-Dependent NIRS Technology by Lihe Ding, Lei-ming Yuan, Yiye Sun, Xia Zhang, Jianpeng Li, Zou Yan

“…To obtain the detection results accurately, several chemometrics methods including principal component analysis (PCA), variables selection method of variable importance in projection (VIP), continuous 1D wavelet transform (CWT), and partial least square-discriminant analysis (PLS-DA) were employed to develop a classifier to distinguish the physical status of athletes. …”
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Characteristics and Influencing Factors of Microbial Community in Heavy Metal Contaminated Soil under Silicon Fertilizer and Biochar Remediation by Jiachao Zhang, Yuewei He, Yingchun Fang, Keqi Zhao, Nanyi Wang, Yaoyu Zhou, Lin Luo, Yuan Yang

“…Microbial community abundance and structure were also significantly changed. Principal component analysis shows that the difference in soil microbial community structure is the most obvious under the combined addition of biochar, silicon fertilizer and biochar. …”
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A Reliable Method for Identification of Antibiotics by Terahertz Spectroscopy and SVM by Jin Guo, Hu Deng, Quancheng Liu, Linyu Chen, Zhonggang Xiong, Liping Shang

“…For dimensionality reduction, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were implemented, respectively. …”
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Effects of Milling Methods and Cultivars on Physicochemical Properties of Whole-Wheat Flour by Min Jeong Kang, Mi Jeong Kim, Han Sub Kwak, Sang Sook Kim

“…Overall, results from principal component analysis showed that, among the three cultivars, Keumkang WWF was the most affected by the milling method.…”
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Application of machine learning in asphalt and concrete material testing: A comprehensive review by Khorshidi Meisam, Dave Eshan, Sias Jo

“…Additionally, unsupervised learning methods, such as K-means clustering and Principal Component Analysis (PCA), proved effective in classifying pavement conditions and detecting anomalies, with K-means offering simplicity and efficiency at the cost of sensitivity to initialization and cluster definitions. …”
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Piper-PCA-Fisher Recognition Model of Water Inrush Source: A Case Study of the Jiaozuo Mining Area by Pinghua Huang, Xinyi Wang

“…Additionally, principal component analysis (PCA) was used for dimensionality reduction of conventional hydrochemical variables, after which mutually independent variables were extracted. …”
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Degradation of PVDF-Coated Fabrics after Engineering Applications: Correlations between Surface Microstructure, Physical Properties, and Mechanical Properties Based on Statistical... by Yingying Shang, Bin Yang, Minger Wu, Youji Tao, Jiaxiang Qin

“…In order to further study the degradation mechanism, infrared spectroscopy was used to characterize the molecular structure of aged fabrics. Finally, Principal Component Analysis (PCA) provided a comprehensive description of correlations between surface microstructure, physical properties, and mechanical properties. …”
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