Exploratory Data Analysis

Vigni, Mario Li; Durante, Caterina; Cocchi, Marina

doi:10.1016/b978-0-444-59528-7.00003-x

Cited by 62 publications

(55 citation statements)

References 95 publications

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“…The selected variables are also major components found in nutritional label of honey. The PCs are the linear combination of the original variables where score of an object is computed by multiplying the original variables with the eigenvectors (coefficients) on a PC (dos Santos et al 2008;Vigni et al 2013). The first three PCs accounted for 88.82 % of the total variance and are given by expression below: …”

Section: Principal Component Analysismentioning

confidence: 99%

Classification of Honey from Its Bee Origin via Chemical Profiles and Mineral Content

et al. 2016

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Raw honeys from four different bee species, namely the honey bees and stingless bees, were classified based on its chemical profiles, mineral contents and heavy metals. Chemical profiles including proximate composition, predominant sugars, hydroxymethylfurfural content, and diastase activity were determined following official methods while mineral and heavy metals contents were obtained from atomic adsorption spectrometry (AAS) and inductively coupled plasma-mass spectrometry (ICP-MS) measurements, respectively. Both hierarchical cluster analysis and principal component analysis show high possibility of distinguishing honey by its bee species of honey bees (Apis spp.) and stingless bees (Heterotrigona itama) based on distinctive differences in chemical compositions and mineral contents. Potassium and sodium were the major elements in raw honey samples at average of 904.9 and 617.6 mg/kg, respectively. Honey from stingless bee contained more protein, 0.85 g/100 g and less total sugar of fructose and glucose at 24.99 g/100 g. The information of bee speciation origin of honey bees and stingless bees enhances the identity of honey on the product labelling.

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Section: Principal Component Analysismentioning

confidence: 99%

Classification of Honey from Its Bee Origin via Chemical Profiles and Mineral Content

et al. 2016

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“…A comparison of the metabolites' relative content (peak areas obtained by MCR) of lymphoma samples 23 and 34 with respect to the correctly rejected ones reveals that the two rejected samples have significantly lower content of all metabolites. Moreover, sample 05 was correctly rejected but it can be found close to the acceptance limit ( Figure 4), and its profile resulted more similar to the two wrongly accepted samples (23,34) than to the other two rejected ones (24,31). Recently, a paper has been published about lymphoma tumor.…”

Section: Classification Analysis: Simcamentioning

confidence: 83%

“…Exploratory data analysis was performed by PCA on the autoscaled ( i.e. the mean value was subtracted from each dataset's column, which was then divided by its standard deviation) mid‐level fused data set containing glioma samples of grade IV and III.…”

Section: Methodsmentioning

confidence: 99%

A metabolomic data fusion approach to support gliomas grading

et al. 2019

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Magnetic resonance imaging (MRI) is the current gold standard for the diagnosis of brain tumors. However, despite the development of MRI techniques, the differential diagnosis of central nervous system (CNS) primary pathologies, such as lymphoma and glioblastoma or tumor-like brain lesions and glioma, is often challenging. MRI can be supported by in vivo magnetic resonance spectroscopy (MRS) to enhance its diagnostic power and multiproject-multicenter evaluations of classification of brain tumors have shown that an accuracy around 90% can be achieved for most of the pairwise discrimination problems. However, the survival rate for patients affected by gliomas is still low. The High-Resolution Magic-Angle-Spinning Nuclear Magnetic Resonance (HR-MAS NMR) metabolomics studies may be helpful for the discrimination of gliomas grades and the development of new strategies for clinical intervention. Here, we propose to use T 2 -filtered, diffusion-filtered and conventional waterpresaturated spectra to try to extract as much information as possible, fusing the data gathered by these different NMR experiments and applying a chemometric approach based on Multivariate Curve Resolution (MCR). Biomarkers important for glioma's discrimination were found. In particular, we focused our attention on cystathionine (Cyst) that shows promise as a biomarker for the better prognosis of glioma tumors.

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“…An original data matrix (X), is decomposed into a score matrix (T ) and a loading 6 matrix (P ), with the residuals collected in a matrix (E): X = T P T + E. The loadings define the new coordinates system, the weights that the previous/original variables have on each principal component. 28 The scores are the "amount of" those new artificial variables represented in particular sample, in other words, they are the coordinates of the samples in the principal component space. 29…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Raw material quality assessment approaches comparison in pectin production

Caroço

Bevilacqua

Armagan³

et al. 2018

Biotechnology Progress

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This article explores different opportunities to evaluate quality variation in raw materials from biological origin. Assessment of raw materials attributes is an important step in a bio-based production since fluctuations in quality are a major source of process disturbance. This can be due to a variety of biological, seasonal and supply scarcity reasons. The final properties of a product are invariably linked with the initial properties of the raw material. Thus, the operational conditions of a process can be tuned to drive the product to the required specification based on the quality assessment of the raw material being processed. Process analytical technology (PAT) tools which enable this assessment in a far more informative and rapid manner than current industrial practices that rely on rule-of-thumb decisions are assessed. An example with citrus peels is used to demonstrate the conceptual and performance differences of distinct quality assessment approaches. The analysis demonstrates the advantage of characterization through multivariate data analysis coupled with a complementary spectroscopic technique, near-infrared spectroscopy. The quantitative comparative analysis of three different approaches, discriminant classification based on expert-knowledge, unsupervised classification, and spectroscopic correlation with reference physicochemical variables, is performed in the same dataset context. Current traditional bioprocessing plant manufacturing does not tackle raw material variability with enough operational flexibility, resulting in product quality variability. Unlike other process conditions, the manufacturer does not directly control raw materials. The quality is highly dependent on external vendors. Furthermore, raw materials may vary from lot to lot on a long timescale. In these cases, the measurement of critical properties of the raw material can allow for the dynamic monitoring and control. 1,2 The need to tackle the natural variability of raw materials is considered an important challenge in industrial biotech processes.An adequate approach to this problem can mitigate production performance issues and undesired deviation of the critical quality attributes of the end-product due to raw material quality fluctuation. 3-5 Identification and measurement of key raw material characteristics (physicochemical or (micro)biological properties) through conventional analytical chemistry are an immediate first step into working towards this paradigm. Statistical analysis and modeling of this data can be performed to further enhance the use of raw material in process and learn to better classify different lots of the same raw materials. 6 However, the measurement of raw material quality may be time-consuming and prohibitive for in-production monitoring and optimization applications. 7This limitation can be overcome by coupled use of advanced spectroscopic methods such as near-infrared (NIRS), UV-visible and Raman spectroscopy with chemometric techniques. This combination constitutes a process analytical technol...

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Exploratory Data Analysis

Cited by 62 publications

References 95 publications

Classification of Honey from Its Bee Origin via Chemical Profiles and Mineral Content

Classification of Honey from Its Bee Origin via Chemical Profiles and Mineral Content

A metabolomic data fusion approach to support gliomas grading

Raw material quality assessment approaches comparison in pectin production

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