Fuzzy Divisive Hierarchical Associative-Clustering Applied to Different Varieties of White Wines According to Their Multi-Elemental Profiles

Wine data are usually characterized by high variability, in terms of compounds and concentration ranges. Chemometric methods can be efficiently used to extract and exploit the meaningful information contained in such data. Therefore, the fuzzy divisive hierarchical associative-clustering (FDHAC) method was efficiently applied in this study, for the classification of several varieties of Romanian white wines, using the elemental profile (concentrations of 30 elements analyzed by ICP-MS). The investigated wines were produced in four different geographical areas of Romania (Transylvania, Moldova, Muntenia and Oltenia). The FDHAC algorithm provided not only a fuzzy partition of the investigated white wines, but also a fuzzy partition of considered characteristics. Furthermore, this method is unique because it allows a 3D bi-plot representation of membership degrees corresponding to wine samples and elements. In this way, it was possible to identify the most specific elements (in terms of highest, smallest or intermediate concentration values) to each fuzzy partition (group) of wine samples. The chemical elements that appeared to be more powerful for the differentiation of the wines produced in different Romanian areas were: K, Rb, P, Ca, B, Na.

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Classification of monovarietal Argentinean white wines by their elemental profile

Food Control ◽

10.1016/j.foodcont.2015.04.025 ◽

2015 ◽

Vol 57 ◽

pp. 268-274 ◽

Cited By ~ 27

Author(s):

Silvana M. Azcarate ◽

Luis D. Martinez ◽

Marianela Savio ◽

José M. Camiña ◽

Raúl A. Gil

Keyword(s):

White Wines ◽

Elemental Profile

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Classification of Chemical Elements in the Reaction Cell of ICP-MS Based on the Affinities with Sulfur, Oxygen, and Fluorine

Chemistry Letters ◽

10.1246/cl.180192 ◽

2018 ◽

Vol 47 (6) ◽

pp. 740-743

Author(s):

Yanbei Zhu

Keyword(s):

Chemical Elements ◽

Reaction Cell ◽

Icp Ms

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Classification of Spanish DO white wines according to their elemental profile by means of support vector machines

Food Chemistry ◽

10.1016/j.foodchem.2012.06.017 ◽

2012 ◽

Vol 135 (3) ◽

pp. 898-903 ◽

Cited By ~ 21

Author(s):

José Marcos Jurado ◽

Ángela Alcázar ◽

Ana Palacios-Morillo ◽

Fernando de Pablos

Keyword(s):

Support Vector Machines ◽

Support Vector ◽

White Wines ◽

Elemental Profile ◽

Vector Machines

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Fingerprint of PGI Mantova Cucumis melo by ICP-MS and Chemometric Analysis

Current Nutrition & Food Science ◽

10.2174/1573401316999200504094207 ◽

2020 ◽

Vol 17 (1) ◽

pp. 94-104

Author(s):

Antonio F. Mottese ◽

Maria R. Fede ◽

Francesco Caridi ◽

Giuseppe Sabatino ◽

Giuseppe Marcianò ◽

...

Keyword(s):

Cucumis Melo ◽

Principal Component ◽

Inorganic Elements ◽

Discrimination Power ◽

Icp Ms ◽

High Discrimination ◽

Strong Relation ◽

Trace Element Contents ◽

Safe Food

Background and Objectives: In this work, yellow and green varieties of Cucumis melo fruits belonging to different cultivars were studied. In detail, three Sicilian cultivars of winter melons tutelated by TAP (Traditional agro-alimentary products) labels were considered, whereas asun protected the Calabrian winter melon was studied too. With the aim to compare the selective uptakes of inorganic elements among winter and summer fruits, the “PGI Melone Mantovano” was investigated. The purpose of this work was to apply the obtained results i) to guarantee the quality and healthiness of fruits, ii) to producers defend, iii) to help the customers in safe food purchase. Method: All samples were analyzed by ICP-MS and the obtained results, subsequently, were subjected to Cluster analysis (CA), Principal component analysis (PCA) and Canonical discriminant analysis (CDA). Results: CA results were generally in agreement with samples origin, whereas the PCA elaboration has confirmed the presence of a strong relation between fruit origins and trace element contents. In particular, two principal components justified the 57.32% of the total variance (PC1= 40.95%, PC2= 16.37%). Finally, the CDA approach has provided several functions with high discrimination power, confirmed by the correct classification of all samples (100%). Conclusions: CA, PCA and CDA could represent an integrated to label to discriminate the origin of agri-food products and, thus, protect and guarantee their healthiness.

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Quantification and classification of cotton biodiesel content in diesel blends, using mid-infrared spectroscopy and chemometric methods

Fuel ◽

10.1016/j.fuel.2018.10.011 ◽

2019 ◽

Vol 237 ◽

pp. 373-379 ◽

Cited By ~ 7

Author(s):

Ademar Domingos Viagem Máquina ◽

Baltazar Vasco Sitoe ◽

José Eduardo Buiatte ◽

Douglas Queiroz Santos ◽

Waldomiro Borges Neto

Keyword(s):

Infrared Spectroscopy ◽

Chemometric Methods ◽

Mid Infrared ◽

Mid Infrared Spectroscopy

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Classification of South African red and white wines according to grape variety based on the non-coloured phenolic content

European Food Research and Technology ◽

10.1007/s00217-005-1169-5 ◽

2005 ◽

Vol 221 (3-4) ◽

pp. 520-528 ◽

Cited By ~ 47

Author(s):

André de Villiers ◽

Pavel Majek ◽

Frederic Lynen ◽

Andrew Crouch ◽

Henk Lauer ◽

...

Keyword(s):

South African ◽

Phenolic Content ◽

Grape Variety ◽

White Wines

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Antimony and Nickel Impurities in Blue and Green Copper Pigments

Minerals ◽

10.3390/min11111236 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1236

Author(s):

Sylwia Svorová Pawełkowicz ◽

Barbara Wagner ◽

Jakub Kotowski ◽

Grażyna Zofia Żukowska ◽

Bożena Gołębiowska ◽

...

Keyword(s):

Inductively Coupled Plasma ◽

Ore Deposits ◽

Copper Ore ◽

Inductively Coupled ◽

Parish Church ◽

Icp Ms ◽

Plasma Mass Spectrometry ◽

Quantitative Analyses ◽

First Time

Impurities in paint layers executed with green and blue copper pigments, although relatively common, have been studied only little to date. Yet, their proper identification is a powerful tool for classification of paintings, and, potentially, for future provenance studies. In this paper, we present analyses of copper pigments layers from wall paintings situated in the vicinity of copper ore deposits (the palace in Kielce, the palace in Ciechanowice, and the parish church in Chotków) located within the contemporary borders of Poland. We compare the results with the analyses of copper minerals from three deposits, two local, and one historically important for the supply of copper in Europe, i.e., Miedzianka in the Holy Cross Mountains, Miedzianka in the Sudetes, and, as a reference, Špania Dolina in the Slovakian Low Tatra. Optical (OM) and electron microscopy (SEM-EDS), Raman spectroscopy, and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) have been used for a detailed investigation of the minute grains. Special attention has been devoted to antimony and nickel phases, as more unusual than the commonly described iron oxides. Analyses of minerals from the deposits helped to interpret the results obtained from the paint samples. For the first time, quantitative analyses of copper pigments’ impurities have been described.

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News Classification Using Machine Learning

International Journal on Recent and Innovation Trends in Computing and Communication ◽

10.17762/ijritcc.v9i5.5464 ◽

2021 ◽

Vol 9 (5) ◽

pp. 23-27

Author(s):

SHWETA MAHAJAN

Keyword(s):

Machine Learning ◽

Social Media ◽

Performance Improvement ◽

Vital Role ◽

Learning Approach ◽

Entertainment Education ◽

Meaningful Information ◽

Textual Data ◽

Machine Learning Approach

There are plenty of social media webpages and platforms producing the textual data. These different kind of a data needs to be analysed and processed to extract meaningful information from raw data. Classification of text plays a vital role in extraction of useful information along with summarization, text retrieval. In our work we have considered the problem of news classification using machine learning approach. Currently we have a news related dataset which having various types of data like entertainment, education, sports, politics, etc. On this data we have applying classification algorithm with some word vectorizing techniques in order to get best result. The results which we got that have been compared on different parameters like Precision, Recall, F1 Score, accuracy for performance improvement.

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