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multivariate analysis approaches in the treatment of mineral

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(PDF) A multivariate approach to plant mineral nutrition

Multivariate discriminant analysis was used to describe the position and area of nine different plant species in an ''elemental hyperspace'' based on the mineral element composition of leaves.

A multivariate analysis for enhancing the interpretation

 · Multivariate analysis of the spectra. In order to mine similarities between the classes of Table 1, we used multivariate statistical methods in analogy with those employed in chemometric treatment of data (Todeschini, 2010) and successfully adopted also for the analysis of nutrients (Cocchi et al., 2005). In particular we exploited the .

Multivariate Statistical Analysis of Trace Elements in

Multivariate statistical analysis can significantly complement the conventional ways of looking at mineral chemistry data by identifying associations among elements and grouping geochemical analyses into meaningful and interpretable clusters. Such an approach is reproducible, quantitative and not subjective (e.g., [ 26, 27, 28, 29 ]).

(PDF) Multivariate Statistical Analysis of Trace Elements

Using a multivariate statistical approach applied to a large trace element dataset derived from analysis of random pyrite grains, trace element signatures in Olympic Dam pyrite are assessed.

Multivariate Correlation between Color and Mineral

Multiple linear regression (MLR) was used to establish equations relating the colorimetric CIELAB coordinates to the mineral data. The results obtained shown that lightness (L *) was significantly correlated with S, Ca, Fe, As, Pb, and Cd for the dark honey types (avocado, heather, chestnut, and honeydew).

(PDF) A multivariate approach to plant mineral nutrition

Multivariate discriminant analysis was used to describe the position and area of nine different plant species in an ''elemental hyperspace'' based on the mineral element composition of leaves.

Multivariate Statistical Analysis of Trace Elements in

Using a multivariate statistical approach applied to a large trace element dataset derived from analysis of random pyrite grains, trace element signatures in Olympic Dam pyrite are assessed. Pyrite is characterised by: (i) a Ag–Bi–Pb signature predicting inclusions of tellurides (as PC1); and (ii) highly variable Co–Ni ratios likely .

Multivariate Correlation between Color and Mineral

The mineral content and color characteristics of 77 honey samples were analyzed. Eighteen minerals were quantified for each honey. Multiple linear regression (MLR) was used to establish equations relating the colorimetric CIELAB coordinates to the mineral data. The results obtained shown that lightness (L*) was significantly correlated with S, Ca, Fe, As, Pb, and Cd for the …

Multivariate statistical analysis and partitioning of

 · While other multivariate techniques are also useful for other approaches [e.g., mineralogy, Andrews and Eberl, 2012; general similarity analysis, Borchardt, 1974; principal components analysis, Vermeesch, 2013], for specific identification of geochemical sources and their respective contributions these Q‐mode factor analysis, constrained .

Multivariate analysis reveals environmental and genetic

 · Comparing these multivariate QTL mapping results to previous single‐element QTL analyses of the same data (Asaro et al., 2016) and demonstrates that a multivariate approach uncovers unique loci affecting multi‐element covariance. Additionally, experiment‐wide PCA performed on combined data from all environments produced components capable .

A multivariate approach for evaluation and monitoring of

 · Multivariate data analysis as a process monitoring approach is a powerful tool that could help to deal with complex and big datasets. However, it also poses some disadvantages, as highlighted by (Rogalewicz, 2012). First, the application of multivariate data analysis can help to identify the process problem, however, the procedure of .

State-of-the-art analysis of geochemical data for mineral

Upscaling a multivariate statistics-based prospectivity analysis for arc-related Cu–Au mineralization from a regional survey in the southern Thomson Orogen in Australia to the continental scale, reveals a number of regions with a similar (or stronger) multivariate response and hence potentially similar (or higher) mineral potential throughout .

ORIGINAL ARTICLE JBMR - Wiley Online Library

analysis of the propensity score–matched groups revealed that the dosage and duration of administration of an antiresorptive agent, serum albumin level, discontinuing drug, and treatment method were significantly correlated with treatment outcome (Table 6). A multivariate analysis demonstrated that two Table 1. Treatment Outcome of MRONJ

Eleven Multivariate Analysis Techniques: Key Tools In Your

In order to understand multivariate analysis, it is important to understand some of the terminology. A variate is a weighted combination of variables. The purpose of the analysis is to find the best combination of weights. Nonmetric data refers to data that are either qualitative or categorical in nature.

Frontiers | Multivariate Analysis and Machine Learning in

 · As multivariate analytic approaches and data processing technologies advance in the Big Data era of the 21st century, it is anticipated that multivariate analysis and machine learning will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rate, and enhance patient care for children with CP.

Multivariate Analysis: Greater Insights into Complex

Multivariate analysis refers to a broad category of methods used when multiple response variables are measured on a set of experimental units or sampling objects. . hypothesis‐driven comparison of treatment groups affected by experimental treatment structure . MANOVA is the appropriate MV analysis approach. Multivariate ANOVA is .

Multidisciplinary approach to calcific uremic

Recent findings Overall, the scientific literature on CUA is largely restricted to case reports and case series. Recent reports indicate that the incidence of CUA may be on the rise and emphasize an association with vitamin K antagonist therapy, obesity, and diabetes mellitus. Serum calcium, phosphorous, and parathyroid hormone levels have been reported to be quite variable in …

A Statistical Approach Regarding the Diagnosis of

Introduction. Bone Mineral Density (BMD) is influenced by several factors including genetics, activity, nutrition, and medical comorbidities. (1 , 2 , 3 ) BMD naturally decreases after midadulthood and low BMD is associated with increased risk of fractures of the spine, forearm, and hip.(1 , 3 ) Hip fractures, for example, occur in approximately 400 of 100,000 people in the …

Computational Statistics & Data Analysis期刊最新论文, 化学 •

Inferring causal effect from observational studies is a central topic in many scientific fields, including social science, health and medicine. The statistical methodology for est

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