Using selective NIR wavelengths in portable devices to evaluate the chemical composition of cattle feeds by Lorenzo Serva, Luisa Magrin, Giovanni Chillemi, Giorgio Marchesini, Daniele Pietrucci, Francesco Renzi, Riccardo Valentini, Igino Andrighetto, Marco Milanesi

“…Reducing the spectral data helps prevent multicollinearity issues, enabling the use of a minimal–optimal problem approach and supporting the development of a targeted and cost-effective instrument. …”
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Determining sensory drivers of complex metadescriptors through regression modelling by Emily Fisher, Charles Diako, Rebecca Shingleton, Sidsel Jensen, Joanne Hort

“…Least absolute shrinkage and selection operator (LASSO), a regression technique known for automatic predictor selection, and partial least squares regression, which handles multicollinearity, were compared for their ability to accurately identify the underlying sensory attributes driving creaminess perception.Twenty-eight sensory attributes were selected after discussions with milk consumers. …”
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Process variables in mixture experimental design applied to wood plastic composites by Javier Cruz-Salgado, Sergio Alonso-Romero, Edgar Augusto Ruelas-Santoyo, José Alfredo Jiménez-García, Israel Miguel-Andrés, Roxana Zaricell Bautista-López

“…The absence of high multicollinearity, as indicated by variance inflation factor (VIF) values below 5, further supports the model's stability and interpretability. …”
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Investigating LiDAR Metrics for Old-Growth Beech- and Spruce-Dominated Forest Identification in Central Europe by Devara P. Adiningrat, Andrew Skidmore, Michael Schlund, Tiejun Wang, Haidi Abdullah, Marco Heurich

“…To determine the important LiDAR standard and structural metrics in identifying old-growth forests, multicollinearity analysis using the variance inflation factor (VIF) approach was applied to identify and remove metrics with high collinearity, followed by the random forest algorithm to rank which LiDAR standard and structural metrics are important in old-growth forest classification. …”
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Integrated Machine Learning Approaches for Landslide Susceptibility Mapping Along the Pakistan–China Karakoram Highway by Mohib Ullah, Haijun Qiu, Wenchao Huangfu, Dongdong Yang, Yingdong Wei, Bingzhe Tang

“…Key factors influencing landslide susceptibility showed slight variations across the models, while multicollinearity among variables remained minimal. The proposed modeling approach reduces uncertainties, enhances prediction accuracy, and supports decision-makers in implementing effective landslide mitigation strategies.…”
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Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches by Nafiseh Hosseini, Hamid Tanzadehpanah, Amin Mansoori, Mostafa Sabzekar, Gordon A. Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…Result After feature selection analysis and assessing multicollinearity, six factors (Mid-arm Circumference (MAC), Waist Circumference (WC), Body Roundness Index (BRI), Body Adiposity Index (BAI), Body Mass Index (BMI), age) were used in the final model. …”
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Prediction models of iron level in beef muscle tissue toward ecological well-being by K. Narozhnykh

“…Furthermore, no signs of multicollinearity exist between the main effects of the model (variance-inflation factor = 1.2–1.7).CONCLUSION: The model can be used for the intravital analysis of iron level in the muscle tissue of cattle. …”
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Relationship Between Body Mass Index and Low Skeletal Muscle Mass in Adults Based on NHANES 2011–2018 by Rong-Zhen Xie, Xu-Song Li, Fang-Di Zha, Guo-Qing Li, Wei-Qiang Zhao, Yu-Feng Liang, Jie-Feng Huang

“…Variance inflation factors (VIF) confirmed the absence of multicollinearity. Lower BMI was significantly associated with higher odds of low muscle mass (adjusted OR: 0.508, 95% CI: 0.483–0.533, p < 0.001), while higher BMI exhibited a protective effect. …”
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Spatiotemporal Characteristics and Influential Factors of Electronic Cigarette Web-Based Attention in Mainland China: Time Series Observational Study by Zhongmin Zhang, Hengyi Xu, Jing Pan, Fujian Song, Ting Chen

“…A variance inflation factor test was performed to avoid multicollinearity. A spatial panel econometric model was developed to assess the determinants of e-cigarette web-based attention. …”
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Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniques by Mohamed Abdellatief, Leong Sing Wong, Norashidah Md Din, Ali Najah Ahmed, Abba Musa Hassan, Zainah Ibrahim, G. Murali, Kim Hung Mo, Ahmed El-Shafie

“…Data preprocessing involved Pearson correlation analysis to address multicollinearity and reveal nonlinear relationships among variables. …”
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Factors influencing waist circumference among urban bank employees in Northeast Ethiopia: a cross-sectional study by Woynshet Yimer, Lakew Asmare, Fikre Bayu Gebeyehu, Tihtna Alemu, Anisa Mehamed, Fanos Yeshanew Ayele

“…Normality, homoscedasticity, significant outliers, and multicollinearity were assessed, and a p-value of less than 0.05, along with a 95% confidence interval, was considered statistically significant.ResultsA total of 345 participants were included in the final analysis, with a 95% response rate. …”
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Anemia and Contributing Factors in Severely Malnourished Infants and Children Aged between 0 and 59 Months Admitted to the Treatment Centers of the Amhara Region, Ethiopia: A Multi... by Wubet Worku Takele, Adhanom Gebreegziabher Baraki, Haileab Fekadu Wolde, Hanna Demelash Desyibelew, Behailu Tariku Derseh, Abel Fekadu Dadi, Eskedar Getie Mekonnen, Temesgen Yihunie Akalu

“…The binary logistic regression analysis was employed to show an association between the dependent and independent variables. Multicollinearity was assessed using the variance inflation factor (VIF) and no problem was detected (overall VIF = 1.67). …”
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Hyperspectral imaging for precision nitrogen management: A comparative exploration of two methodological approaches to estimate optimal nitrogen rate in processing tomato by Vito Aurelio Cerasola, Francesco Orsini, Giuseppina Pennisi, Gaia Moretti, Stefano Bona, Francesco Mirone, Jochem Verrelst, Katja Berger, Giorgio Gianquinto

“…., Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Partial Least Square Regression (PLSR). Multicollinearity of spectral bands was prevented with a principal component analysis, and models were 5-fold cross-validated. …”
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600 meters to VO2max: Predicting Cardiorespiratory Fitness with an Uphill Run by Kübra Stoican, Regina Oeschger

“…For the purpose of overcoming multicollinearity among the predictor variables speed to HR ratio, time, and gender, principal component analysis with two components was applied before we fed the data into the multiple linear regression model. …”
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Customer segmentation in the digital marketing using a Q-learning based differential evolution algorithm integrated with K-means clustering. by Guanqun Wang

“…Initially, a correlation matrix is used to identify redundant noise and multicollinear features within customer feature groups, and Principal Component Analysis is applied for denoising and dimensionality reduction to enhance the ability of the model to identify potential features. …”
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A Generalized Bridge Regression in Fuzzy Environment and Its Numerical Solution by a Capable Recurrent Neural Network by Delara Karbasi, Mohammad Reza Rabiei, Alireza Nazemi

“…We use a simulation study to depict the performance of the proposed bridge technique in the presence of multicollinear data. Furthermore, real data analysis is used to show the performance of the proposed method. …”
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