scholarly journals A New Approach for Provenance Studies of Archaeological Finds: Inferences from Trace Elements in Carbonate Minerals of Alpine White Marbles by a Bench-to-Top μ-XRF Spectrometer

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Gloria Vaggelli ◽  
Margherita Serra ◽  
Roberto Cossio ◽  
Alessandro Borghi
Keyword(s):  
Principal Component ◽  
Hierarchical Cluster ◽  
Western Alps ◽  
Metamorphic Rocks ◽  
Carbonate Minerals ◽  
Data Set ◽  
New Approach ◽  
Provenance Studies ◽  
Individual Observation ◽  
Geological Units

The metamorphic rocks outcropping in the Western Alps are characterised by a great variety of white marbles which have been used since the antiquity. This variety mostly includes nine historical Piedmont white marbles (Ornavasso, Candoglia, Crevola, Pont Canavese, Foresto, Chianocco, Prali, Brossasco, and Garessio marbles) coming from well-known quarry sites and belonging to different metamorphic geological units of the Western Alps. The petrographical, minerochemical, and C–O isotopic data of these white marbles have been integrated with CaO and trace element (Fe, Mn, and Sr) concentration determined on single crystals of carbonate minerals (i.e., calcite and/or dolomite) by means of a bench-to-top µ-XRF spectrometer. Principal component analysis and hierarchical cluster analysis were performed on a data set of 178 observations containing CaO, Fe, Mn, and Sr concentration as well as the maximum grain size (MGS), δ18O and δ13C. The use of only five selected variables (CaO, Fe, Mn, Sr, and δ18O) has provided the correct allocation of each individual observation to its relevant class. Therefore, this approach based mostly on a noninvasive µ-XRF determination will be useful to define the provenance of unknown marbles of alpine origin used in antiquity for cultural heritage.

scholarly journals Hydrochemical assessment of Semarang area using multivariate statistics: A sample based dataset

2016 ◽  
Author(s):  
Dasapta Erwin Irawan ◽  
Thomas Triadi Putranto
Keyword(s):  
Cluster Analysis ◽  
Statistical Treatment ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Data Set ◽  
Anal Ysis ◽  
R Packages ◽  
Point Data ◽  
Depth Water ◽  
Water Elevation

Abstract. The following paper describes in brief the data set related to our project "Hydrochemical assessment of Semarang Groundwater Quality". All of 58 samples were taken in 1992, 1993, 2003, 2006, and 2007 using well point data from several reports from Ministry of Energy and Min- eral Resources and independent consultants. We provided 20 parameters in each samples (sample id, coord X, coord Y, well depth, water level, water elevation, TDS, pH, EC, K, Ca, Na, Mg, Cl, SO4, HCO3, year, ion balance, screen location, and chemical facies). The chemical composi- tion were tested in the Water Quality Laboratory, Universitas Diponegoro using mas spectrofotometer method. The statistical treatment for the dataset (available on Zenodo doi:10.5281/zenodo.57293) were described as follows: (1) data preparation in to csv file format, load it in to R environment; (2) data treatment, including: correlation matrix, cluster analysis using kmeans and hierarchical cluster analysis, and principal component analysis. For anal- ysis and visualizations, We used the following R packages: ggplot2, dplyr, factomineR, factoExtra, cluster, ggcorrplot, and ape.

scholarly journals Applying Chemometrics to Evaluate Mine Tailings’ Potential As Partial Cement Replacement

2020 ◽  
Author(s):  
Anne Mette T. Simonsen ◽  
Kristine B. Pedersen ◽  
Pernille E. Jensen
Keyword(s):  
Mine Tailings ◽  
Principal Component ◽  
Large Data ◽  
Hierarchical Cluster ◽  
Physical Characteristics ◽  
Mine Tailing ◽  
Data Set ◽  
Cement Replacement ◽  
Partial Cement Replacement ◽  
Physical And Chemical

This study investigates the utilization of mine tailings, the by-product originating from metal- and mineral-based ore mining, as a new cement replacement material. This paper is based on the chemical and physical characteristics of 13 mine tailing samples. In this study, Chemometrics were applied to consider all parameters simultaneously and obtain a thorough screening of potential relations in the large data set. Hierarchical Cluster Analysis (HCA) groups samples according to (dis)similar features and Principal Component Analysis (PCA) visualizes predominating variables and relations to samples. The application of HCA highlighted a clear grouping between mine tailings according to characteristics. Meanwhile, PCA identified the predominant chemical and physical characteristics in the mine tailing samples. Chemometrics therefore provided a thorough overview of mine tailings’ physical and chemical characteristics. Keywords: mine tailings, chemometrics, cement replacement

scholarly journals Chemometric approach to quality characterization of milk-based kombucha beverages

Mljekarstvo ◽  
2021 ◽  
Vol 71 (2) ◽  
pp. 83-94
Author(s):  
Jasmina Vitas ◽  
Keyword(s):  
Fatty Acids ◽  
Milk Fat ◽  
Unsaturated Fatty Acids ◽  
Principal Component ◽  
Unknown Origin ◽  
Hierarchical Cluster ◽  
Monounsaturated Fatty Acids ◽  
Data Set ◽  
Linear Discriminant

Milk-based kombucha beverages were obtained conducting kombucha lead fermentation of milk. In order to discriminate the analysed samples and to detect similarities or dissimilarities among them in the space of experimentally determined variables, hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied. Linear discriminant analysis (LDA) was conducted on the raw data set in order to find a rule for allocating a new sample of unknown origin to the correct group of samples. In the space of the variables analysed by HCA, the dominant discriminating factor for the studied samples of kombucha beverages is the milk fat (MF) content, followed by total unsaturated fatty acids content (TUFA), monounsaturated fatty acids content (MUFA) and polyunsaturated fatty acids content (PUFA). The samples with 0.8 and 1.6% milk fat belong to the same cluster in the space of the analysed variables due to similarities in their AADPPH. It was determined by LDA that there was the biggest difference in quality between the groups of products with winter savoury and stinging nettle, while the highest similarity is between groups of products with wild thyme and peppermint regarding their pH values and antioxidant activity expressed as AADPPH.

scholarly journals GC-MS Metabolic Profile and α-Glucosidase-, α-Amylase-, Lipase-, and Acetylcholinesterase-Inhibitory Activities of Eight Peach Varieties

Molecules ◽  
2021 ◽  
Vol 26 (14) ◽  
pp. 4183
Author(s):  
Dasha Mihaylova ◽  
Ivelina Desseva ◽  
Aneta Popova ◽  
Ivayla Dincheva ◽  
Radka Vrancheva ◽  
...  
Keyword(s):  
Inhibitory Activity ◽  
Enzyme Inhibitors ◽  
Amylase Activity ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
New Approach ◽  
Natural Agents ◽  
Patients With Diabetes ◽  
Inhibitory Activities ◽  
Inhibitory Potential

The inhibition of certain digestive enzymes by target food matrices represents a new approach in the treatment of socially significant diseases. Proving the ability of fruits to inhibit such enzymes can support the inclusion of specific varieties in the daily diets of patients with diabetes, obesity, Alzheimer’s disease, etc., providing them with much more than just valuable micro- and macromolecules. The current study aimed atidentifying and comparing the GC-MS metabolic profiles of eight peach varieties (“Filina”, “Ufo 4, “Gergana”, “Laskava”, “July Lady”, “Flat Queen”, “Evmolpiya”, and “Morsiani 90”) grown in Bulgaria (local and introduced) and to evaluate the inhibitory potential of their extracts towards α-glucosidase, α-amylase, lipase, and acetylcholinesterase. In order to confirm samples’ differences or similarities, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were also applied to the identified metabolites. The results provide important insights into the metabolomic profiles of the eight peach varieties and represent a first attempt to characterize the peels of the peach varieties with respect to α-glucosidase-, α-amylase-, lipase-, and acetylcholinesterase-inhibitory activities. All of the studied peach extracts displayed inhibitory activity towards α-glucosidase (IC50: 125–757 mg/mL) and acetylcholinesterase (IC50: 60–739 mg/mL), but none of them affected α-amylase activity. Five of the eight varieties showed inhibitory activity towards porcine pancreatic lipase (IC50: 24–167 mg/mL). The obtained results validate the usefulness of peaches and nectarines as valuable sources of natural agents beneficial for human health, although further detailed investigation should be performed in order to thoroughly identify the enzyme inhibitors responsible for each activity.

scholarly journals Multi-Sensor Characterization of Sparkling Wines Based on Data Fusion

Chemosensors ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 200
Author(s):  
Anais Izquierdo-Llopart ◽  
Javier Saurina
Keyword(s):  
Data Fusion ◽  
Organic Acids ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Sensor Data ◽  
Data Set ◽  
Total Antioxidant ◽  
Red Grape ◽  
Grape Varieties

This paper is focused on the assessment of a multi-sensor approach to improve the overall characterization of sparkling wines (cava wines). Multi-sensor, low-level data fusion can provide more comprehensive and more accurate vision of results compared with the study of simpler data sets from individual techniques. Data from different instrumental platforms were combined in an enriched matrix, integrating information from spectroscopic (UV/Vis and FTIR), chromatographic, and other techniques. Sparkling wines belonging to different classes, which differed in the grape varieties, coupages, and wine-making processes, were analyzed to determine organic acids (e.g., tartaric, lactic, malic, and acetic acids), pH, total acidity, polyphenols, total antioxidant capacity, ethanol, or reducing sugars. The resulting compositional values were treated chemometrically for a more efficient recovery of the underlaying information. In this regard, exploratory methods such as principal component analysis showed that phenolic compounds were dependent on varietal and blending issues while organic acids were more affected by fermentation features. The analysis of the multi-sensor data set provided a more comprehensive description of cavas according to grape classes, blends, and vinification processes. Hierarchical Cluster Analysis (HCA) allowed specific groups of samples to be distinguished, featuring malolactic fermentation and the chardonnay and red grape classes. Partial Least Squares-Discriminant Analysis (PLS-DA) also classified samples according to the type of grape varieties and fermentations. Bar charts and complementary statistic test were performed to better define the differences among the studied samples based on the most significant markers of each cava wine type. As a conclusion, catechin, gallic, gentisic, caftaric, caffeic, malic, and lactic acids were the most remarkable descriptors that contributed to their discrimination based on varietal, blending, and oenological factors.

Privacy Preserving Clustering for Distributed Homogeneous Gene Expression Data Sets

2010 ◽  
Vol 1 (3) ◽  
pp. 31-54 ◽  
Author(s):  
Xin Li
Keyword(s):  
Privacy Preservation ◽  
Cluster Formation ◽  
Genomic Data ◽  
Principal Component ◽  
Privacy Preserving ◽  
Gene Clustering ◽  
Data Sets ◽  
Data Set ◽  
New Approach ◽  
Gene Profiles

In this paper, the authors present a new approach to perform principal component analysis (PCA)-based gene clustering on genomic data distributed in multiple sites (horizontal partitions) with privacy protection. This approach allows data providers to collaborate together to identify gene profiles from a global viewpoint, and at the same time, protect the sensitive genomic data from possible privacy leaks. The authors developed a framework for privacy preserving PCA-based gene clustering, which includes two types of participants such as data providers and a trusted central site. Within this mechanism, distributed horizontal partitions of genomic data can be globally clustered with privacy preservation. Compared to results from centralized scenarios, the result generated from distributed partitions achieves 100% accuracy by using this approach. An experiment on a real genomic data set is conducted, and result shows that the proposed framework produces exactly the same cluster formation as that from the centralized data set.

scholarly journals Application of principal component and hierarchical cluster analyses in the classification of Serbian bottled waters and a comparison with waters from some European countries

2017 ◽  
Vol 82 (6) ◽  
pp. 711-721 ◽  
Author(s):  
Jelena Cvejanov ◽  
Biljana Skrbic
Keyword(s):  
Major Ions ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
European Countries ◽  
Total Hardness ◽  
Chemical Parameters ◽  
Data Set ◽  
Fast Classification ◽  
Hierarchical Cluster Analyses

The contents of major ions in bottled waters were analyzed by principal component (PCA) and hierarchical cluster (HCA) analysis in order to investigate if these techniques could provide the information necessary for classifications of the water brands marketed in Serbia. Data on the contents of Ca2+, Mg2+, Na+, K+, Cl-, SO4 2-, HCO3 - and total dissolved solids (TDS) of 33 bottled waters was used as the input data set. The waters were separated into three main clusters according to their levels of TDS, Na+ and HCO3 -; sub-clustering revealed a group of soft waters with the lowest total hardness. Based on the determined chemical parameters, the Serbian waters were further compared with available literature data on bottled waters from some other European countries. To the best of our knowledge, this is the first report applying chemometric classification of bottled waters from different European countries, thereby representing a unique attempt in contrast to previous studies reporting the results primarily on a country-to-country scale. The diverse character of Serbian bottled waters was demonstrated as well as the usefulness of PCA and HCA in the fast classification of the water brands based on their main chemical parameters.

scholarly journals DIFFERENTIATION OF VIETNAMESE COFFEE ORIGIN AND CULTIVARS BY AMINO AND FATTY ACID PROFILE ANALYSIS PRELIMINARY STUDY

2021 ◽  
Vol 58 (6A) ◽  
pp. 288
Author(s):  
Hoang Quoc Tuan ◽  
Lai Quoc Dat ◽  
Cung Thi To Quynh ◽  
Nguyen Hoang Dung ◽  
Nguyen Xuan Loi ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Glutamic Acid ◽  
Geographical Origin ◽  
Profile Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Eicosenoic Acid ◽  
Complete Data ◽  
Data Matrix ◽  
Data Set

Compositions of fatty acids and amino acids compound were investigated in coffee beans included Arabica and Robusta cultivars grown in three region of Vietnam. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were performed on the complete data set to reveal chemical differences among all samples and identify markers characteristic of a particular botanical geographical origin of the coffee. The major fatty acids in the coffee oil analyzed in this study were linoleic acid (C18:2), stearic acid (C18:0), oleic acid (C18:1) palmitic acid (C16:0) and myristic acid (C14:0), followed by small amounts of arachic acid (C20:0), docosanoic acid (C22:0) and eicosenoic acid (C20:1). Glutamic acid and aspartic acid were found at high amount in robusta coffee, from 271 mg/100gDW to 786 mg/100g DW and 373mg/100g DW to 486 mg/100g DW, respectively, whereas alanine and glutamic acid in arabica coffee were in high amount at 268 mg/100g DW to 351 mg/100g DW and 209 mg/100g DW to 285 mg/100g DW, respectively. Leucine (301 to 416 mg/100 g DW), phenylalanine (226 to 305 mg/100 g DW), and lysine (199 to 269 mg/100 g DW). PCA of the complete data matrix demonstrated that there were significant differences among all coffee cultivars and geographical origin, HCA supported the results of PCA and achieved a satisfactory classification performance.

Customer-Related Social Stressors

2015 ◽  
Vol 14 (4) ◽  
pp. 165-181 ◽  
Author(s):  
Sarah Dudenhöffer ◽  
Christian Dormann
Keyword(s):  
Service Providers ◽  
Principal Component ◽  
Well Being ◽  
Emotional Dissonance ◽  
Social Stressors ◽  
Data Set ◽  
Confirmatory Factor Analyses ◽  
Confirmatory Factor ◽  
Service Jobs ◽  
Single Data

Abstract. The purpose of this study was to replicate the dimensions of the customer-related social stressors (CSS) concept across service jobs, to investigate their consequences for service providers’ well-being, and to examine emotional dissonance as mediator. Data of 20 studies comprising of different service jobs (N = 4,199) were integrated into a single data set and meta-analyzed. Confirmatory factor analyses and explorative principal component analysis confirmed four CSS scales: disproportionate expectations, verbal aggression, ambiguous expectations, disliked customers. These CSS scales were associated with burnout and job satisfaction. Most of the effects were partially mediated by emotional dissonance. Further analyses revealed that differences among jobs exist with regard to the factor solution. However, associations between CSS and outcomes are mainly invariant across service jobs.

Spectroscopy-Based Mapping with Scanning Microwave Impedance Microscopy

2018 ◽  
Author(s):  
Peter De Wolf ◽  
Zhuangqun Huang ◽  
Bede Pittenger
Keyword(s):  
Single Point ◽  
Electrical Characterization ◽  
Principal Component ◽  
High Sensitivity ◽  
Data Cube ◽  
Nanometer Scale ◽  
Learning Approaches ◽  
Data Set ◽  
3D Data ◽  
Higher Dimensional

Abstract Methods are available to measure conductivity, charge, surface potential, carrier density, piezo-electric and other electrical properties with nanometer scale resolution. One of these methods, scanning microwave impedance microscopy (sMIM), has gained interest due to its capability to measure the full impedance (capacitance and resistive part) with high sensitivity and high spatial resolution. This paper introduces a novel data-cube approach that combines sMIM imaging and sMIM point spectroscopy, producing an integrated and complete 3D data set. This approach replaces the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube approach is also applicable to other AFM-based electrical characterization modes.

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