scholarly journals Wine Traceability Using Chemical Analysis, Isotopic Parameters, and Sensory Profiles

Beverages ◽  
2018 ◽  
Vol 4 (3) ◽  
pp. 54 ◽  
Author(s):  
Federica Bonello ◽  
Maria Cravero ◽  
Valentina Dell’Oro ◽  
Christos Tsolakis ◽  
Aldo Ciambotti
Keyword(s):  
Statistical Analysis ◽  
Geographical Origin ◽  
Central Italy ◽  
Principal Component ◽  
Climatic Conditions ◽  
Sensory Profile ◽  
Red Wines ◽  
Multivariate Statistical ◽  
Sensory Parameters ◽  
Sensory Profiles

NMR/IRMS techniques are now widely used to assess the geographical origin of wines. The sensory profile of a wine is also an interesting method of characterizing its origin. This study aimed at elaborating chemical, isotopic, and sensory parameters by means of statistical analysis. The data were determined in some Italian white wines—Verdicchio and Fiano—and red wines—Refosco dal Peduncolo Rosso and Nero d’Avola—produced from grapes grown in two different regions with different soil and climatic conditions during the years 2009–2010. The grapes were cultivated in Veneto (northwest Italy) and Marches (central Italy). The results show that the multivariate statistical analysis PCA (Principal Component Analysis) of all the data can be a useful tool to characterize the vintage and identify the origin of wines produced from different varieties. Moreover, it could discriminate wines of the same variety produced in regions with different soil and climatic conditions.

Statistical Analsysis to Evaluate Heavy Metal Pollution in the Air Obatained by Moss Technique in Hanoi and its Surrounding Region

2019 ◽  
Vol 29 (3SI) ◽  
pp. 411
Author(s):  
N. H. Quyet ◽  
Le Hong Khiem ◽  
V. D. Quan ◽  
T. T. T. My ◽  
M. V. Frontasieva ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Statistical Analysis ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Five Factors ◽  
Heavy Metal Elements ◽  
Surrounding Areas ◽  
Potential Pollution

The aim of this paper was the application of statistical analysis including principal component analysis to evaluate heavy metal pollution obtained by moss technique in the air of Ha Noi and its surrounding areas and to evaluate potential pollution sources. The concentrations of 33 heavy metal elements in 27 samples of Barbula Indica moss in the investigated region collected in December of 2016 in the investigated area have been examined using multivariate statistical analysis. Five factors explaining 80% of the total variance were identified and their potential sources have been discussed.

scholarly journals Screening of Twelve Pea (Pisum sativum L.) Cultivars and Their Isolates Focusing on the Protein Characterization, Functionality, and Sensory Profiles

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 758
Author(s):  
Verónica García Arteaga ◽  
Sonja Kraus ◽  
Michael Schott ◽  
Isabel Muranyi ◽  
Ute Schweiggert-Weisz ◽  
...  
Keyword(s):  
Pisum Sativum ◽  
Functional Properties ◽  
Protein Extraction ◽  
Raw Materials ◽  
Principal Component ◽  
Protein Characterization ◽  
Sensory Profile ◽  
Food Ingredients ◽  
Pisum Sativum L ◽  
Sensory Profiles

Pea protein concentrates and isolates are important raw materials for the production of plant-based food products. To select suitable peas (Pisum sativum L.) for protein extraction for further use as food ingredients, twelve different cultivars were subjected to isoelectric precipitation and spray drying. Both the dehulled pea flours and protein isolates were characterized regarding their chemical composition and the isolates were analyzed for their functional properties, sensory profiles, and molecular weight distributions. Orchestra, Florida, Dolores, and RLPY cultivars showed the highest protein yields. The electrophoretic profiles were similar, indicating the presence of all main pea allergens in all isolates. The colors of the isolates were significantly different regarding lightness (L*) and red-green (a*) components. The largest particle size was shown by the isolate from Florida cultivar, whereas the lowest was from the RLPY isolate. At pH 7, protein solubility ranged from 40% to 62% and the emulsifying capacity ranged from 600 to 835 mL g−1. The principal component analysis revealed similarities among certain pea cultivars regarding their physicochemical and functional properties. The sensory profile of the individual isolates was rather similar, with an exception of the pea-like and bitter attributes, which were significantly different among the isolates.

Risk estimation and multivariate statistical analysis of the heavy metal content of drinking water samples

2018 ◽  
Vol 34 (10) ◽  
pp. 714-725
Author(s):  
Rajan Jakhu ◽  
Rohit Mehra
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Drinking Water ◽  
Statistical Analysis ◽  
Water Samples ◽  
Multivariate Statistical Analysis ◽  
Inductively Coupled Plasma ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Drinking Water Samples

Drinking water samples of Jaipur and Ajmer districts of Rajasthan, India, were collected and analyzed for the measurement of concentration of heavy metals. The purpose of this study was to determine the sources of the heavy metals in the drinking water. Inductively coupled plasma mass spectrometry was used for the determination of the heavy metal concentrations, and for the statistical analysis of the data, principal component analysis and cluster analysis were performed. It was observed from the results that with respect to WHO guidelines, the water samples of some locations exceeded the contamination levels for lead (Pb), selenium (Se), and mercury (Hg), and with reference to the EPA guidelines, the samples were determined unsuitable for drinking because of high concentrations of Pb and Hg. Using multivariate statistical analysis, we determined that copper, manganese, arsenic, Se, and Hg were of anthropogenic origin, while Pb, copper, and cadmium were of geogenic origin. The present study reports the dominance of the anthropogenic contributions over geogenics in the studied area. The sources of the anthropogenic contaminants need to be investigated in a future study.

Metabolite profiling of isoflavones and anthocyanins in black soybean [Glycine max (L.) Merr.] seeds by HPLC-MS and geographical differentiation analysis in Southwest China

2017 ◽  
Vol 9 (5) ◽  
pp. 792-802 ◽  
Author(s):  
Hai-jun Wu ◽  
Jun-cai Deng ◽  
Cai-qiong Yang ◽  
Jing Zhang ◽  
Qing Zhang ◽  
...  
Keyword(s):  
Glycine Max ◽  
Statistical Analysis ◽  
Metabolite Profiling ◽  
Southwest China ◽  
Geographical Origin ◽  
Soybean Seeds ◽  
Geographical Differentiation ◽  
Multivariate Statistical ◽  
Black Soybean ◽  
Glycine Max L

Twelve isoflavones and eight anthocyanins were quantified in black soybean seeds by HPLC-MS analyses; the coupled OPLS-DA multivariate statistical analysis helped us to determine their geographical origin.

scholarly journals Seasonal distribution and population dynamics of limnic microalgae and their association with physico-chemical parameters of river Noyyal through multivariate statistical analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Pandian Suresh Kumar ◽  
Jibu Thomas
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Sampling Site ◽  
Principal Component ◽  
Environmental Parameters ◽  
Diversity Indices ◽  
Population Diversity ◽  
Chemical Parameters ◽  
Multivariate Statistical ◽  
Physico Chemical

Abstract The present investigation embarks on understanding the relationship between microalgal species assemblages and their associated physico-chemical parameter dynamics of the catchment region of river Noyyal. Totally, 142 microalgae cultures belonging to 10 different families were isolated from five different sites during four seasons and relative percentage distribution showed that Scenedesmaceae (36.6%) and site S1 (26.4%) with predominant microalgae population. Diversity indices revealed that microalgae communities were characterized by high Hʹ index, lower Simpson dominance, and Margalef index value with indefinite patterns of annual variations. Results showed that variation in the physico-chemical parameters in each sampling site has its impact on the microalgae population during each season. Multivariate statistical analysis viz., Karl Pearson’s correlation coefficient, principal component analysis, and canonical correspondence analysis were applied on microalgae species data, to evaluate the seasonal relationship between microalgae and physico-chemical parameters. The findings of our study concluded that the physico- chemical parameters influenced the dominant taxa of microalgae Chlorellaceae, Scenedesmaceae and Chlorococcaceae in river Noyyal and gives a base data for the seasonal and dynamic relationship between environmental parameters and microalgae population.

Rapid Differentiation and Identification of Shigella sonnei and Escherichia coli O157: H7 by Fourier Transform Infrared Spectroscopy and Multivariate Statistical Analysis

2014 ◽  
Vol 926-930 ◽  
pp. 1116-1119 ◽  
Author(s):  
Li Jun Yang ◽  
Jing Wang ◽  
Zhao Jie Li ◽  
Xiao Hua Song ◽  
Yu Min Liu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Statistical Analysis ◽  
Fourier Transform Infrared Spectroscopy ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Shigella Sonnei ◽  
Multivariate Statistical ◽  
Simple Method

Fourier transform infrared spectroscopy (FTIR) combined with multivariate statistical analysis was applied to differentiate and identify Shigella sonnei and Escherichiacoli O157: H7. FTIR absorption spectra from 4000-600 cm-1 were collected from sampling 10 μL of bacterial suspention. The spectra between 1800 and 900 cm-1 highlighted the most distinctive variations and were the most useful for characterizing the selected microorganisms. Spectra of the two bacteria were noticeably segregated with distinct clustering by principal component analysis (PCA). Further more, another cluster model of hierarchical cluster analysis (HCA) was established and could also gave a good separation between the two bacteria. These results demonstrate that FTIR technology has considerable potential as a rapid, accurate and simple method for differentiating and identifying bacteria.

scholarly journals LC–MS based metabolic fingerprinting of apricot pistils after self-compatible and self-incompatible pollinations

2020 ◽  
Author(s):  
József Lénárt ◽  
Attila Gere ◽  
Tim Causon ◽  
Stephan Hann ◽  
Mihály Dernovics ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
High Performance ◽  
Differential Expression Analysis ◽  
Principal Component ◽  
Prunus Armeniaca ◽  
Metabolic Fingerprinting ◽  
Negative Ion ◽  
Multivariate Statistical ◽  
Flight Mass Spectrometry ◽  
Incompatibility Reactions

Abstract Key message LC-MS based metabolomics approach revealed that putative metabolites other than flavonoids may significantly contribute to the sexual compatibility reactions in Prunus armeniaca. Possible mechanisms on related microtubule-stabilizing effects are provided. Abstract Identification of metabolites playing crucial roles in sexual incompatibility reactions in apricot (Prunus armeniaca L.) was the aim of the study. Metabolic fingerprints of self-compatible and self-incompatible apricot pistils were created using liquid chromatography coupled to time-of-flight mass spectrometry followed by untargeted compound search. Multivariate statistical analysis revealed 15 significant differential compounds among the total of 4006 and 1005 aligned metabolites in positive and negative ion modes, respectively. Total explained variance of 89.55% in principal component analysis (PCA) indicated high quality of differential expression analysis. The statistical analysis showed significant differences between genotypes and pollination time as well, which demonstrated high performance of the metabolic fingerprinting and revealed the presence of metabolites with significant influence on the self-incompatibility reactions. Finally, polyketide-based macrolides similar to peloruside A and a hydroxy sphingosine derivative are suggested to be significant differential metabolites in the experiment. These results indicate a strategy of pollen tubes to protect microtubules and avoid growth arrest involved in sexual incompatibility reactions of apricot.

Rapid Method for the Discrimination of Romanian Wines Based on Mid-Infrared Spectroscopy and Chemometrics

2018 ◽  
Vol 69 (2) ◽  
pp. 469-473
Author(s):  
Carmen Mihaela Topala ◽  
Lavinia Diana Tataru
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Multivariate Data Analysis ◽  
Geographical Origin ◽  
Principal Component ◽  
Component Analysis ◽  
Red Wines ◽  
Chemical Parameters ◽  
Mid Infrared ◽  
Mir Spectra

ATR-FTIR Spectroscopy combined with multivariate data analysis have been applied for the discrimination of 10 different Romanian wines (white and red wines), produced in 2 wineries from Romania: Reca� and Stefanesti-Arge�s from different cultivars. Principal Component Analysis were performed using different regions of FT-MIR spectra for all wines. Principal Component Analysis of their chemical parameters indicated that the wines can be discriminated based on their different phenolic, glucides, acidity content and geographical origin.

Characterization of Polycyclic Aromatic Hydrocarbon Profiles by Multivariate Statistical Analysis

2013 ◽  
pp. 102-116
Author(s):  
D. J. Marino ◽  
E. A. Castro ◽  
L. Massolo ◽  
A. Mueller ◽  
O. Herbarth ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Principal Component ◽  
Emission Sources ◽  
Multivariate Statistical ◽  
Data Set ◽  
Polycyclic Aromatic ◽  
The City ◽  
Sampling Times ◽  
Sample Identity

In the present study, statistical methods based on multivariate analyses such as the Descriptive Discriminant Analysis (DDA) and Principal Component Analysis (PCA) were applied to determine relationships between particle sizes and the composition of the associated semi-volatile compounds, in addition to evaluating these observations in relation to the emission sources, study areas, sampling campaigns and season. Results from the DDA showed that the PAHs distributions give the best discrimination capacity within the data set, whereas the PAH distribution in intermediate particle fractions incorporates noise in the statistical analysis. The PCA was useful in identifying the main emission sources in each study area. It showed that in the city of La Plata the most important pollution sources are traffic emissions and the industrial activity associated with oil and petrochemical plants. In Leipzig, the main sources are those associated with traffic and also a power plant. The combined PCA and DDA methods applied to PAH distributions is a valuable tool in characterizing types of emissions burdens and also in obtaining a differentiation of sample identity according to study areas and sampling times.

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