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This work proposes the use of near infrared (NIR) spectroscopy in diffuse reflectance mode and multivariate statistical process control (MSPC) based on principal component analysis (PCA) for real-time monitoring of the coffee roasting process.
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By comparing the results obtained from the proposed method, signal channel Fast Fourier Transform (FFT), SSA and multivariate empirical mode decomposition (MEMD), it is found that the application of the proposed model leads to a better solution to extract the feature frequency in mechanical fault diagnosis.
We assessed the outcomes by mode of delivery with bivariate and multivariate analyses in order to control for all possible confounders that can be both related to the need of an operative delivery and to the increased risk of adverse outcomes.
Cox proportional hazards regression models were used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs), with adjustment for age in the univariate analysis and further, histological grade, type of surgery, tumor size, detection mode and postoperative radiotherapy in the multivariate analysis.
Bivariate and multivariate logistic regressions with enter mode were used to identify potential predictors of unintended pregnancy.
Univariate and multivariate techniques were used, and for multivariate regression, a backward mode with a threshold 0.15 was used for elimination.
Univariate and multivariate analyses were undertaken to assess associations between stage of disease and mode of presentation, age, sex, ethnicity, distance to place of residence, literacy, occupation and family history of glaucoma.
Univariate and multivariate variations abound.
Fig. 3 Univariate and multivariate count Gaussianity.
Fig. 2 Univariate and multivariate statistical analysis.
Univariate and multivariate analysis were conducted.
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