scholarly journals Long-Term Ripening Evaluation of Ewes’ Cheeses by Fourier-Transformed Infrared Spectroscopy under Real Industrial Conditions

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Jonathan Andrade ◽  
Cristina Guimarães Pereira ◽  
Thamiris Ranquine ◽  
Cosme Antonio Azarias ◽  
Maria José Valenzuela Bell ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
First Trimester ◽  
Multivariate Statistical ◽  
Peptide Bonds ◽  
Physicochemical Changes ◽  
Fast Pace ◽  
The Times ◽  
Atr Spectroscopy

The ripening changes over time of special cheeses (Pecorino, ewes’ ripe, and Gouda) made with ewes’ milk were evaluated using FTIR/ATR spectroscopy during approximately one year. The midinfrared FTIR/ATR analyses were carried out in different ripening times between the cheese varieties and processed by means of multivariate statistical approaches. Overall, during the maturation, we observed a downward trend of the absorbance intensity of the amide group peaks (1700 to 1500 cm−1), which is linked to the breakdown of peptide bonds. Similar behavior was obtained for the lipidic region (3000 to 2800 cm−1 and 1765 to 1730 cm−1). Hierarchical cluster analysis and principal component analysis allowed the evaluation of the physicochemical changes of the cheeses. The proteolysis occurs in a fast pace during the first trimester of the ripening process, and the lipids are converted to smaller species as the times goes by. Our results indicate that infrared spectroscopy can be a useful tool in determining optimal temporal parameters in stages involving the development, production, and even a possible estimation of shelf life of cheeses.

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 Groundwater quality assessment using principal component analysis and hierarchical cluster analysis in Alaçam, Turkey

2021 ◽  
Author(s):  
Mehmet Taşan ◽  
Yusuf Demir ◽  
Sevda Taşan
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Groundwater Quality ◽  
Seawater Intrusion ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Quality Parameters ◽  
Multivariate Statistical

Abstract This study assessed groundwater quality in Alaçam, where irrigations are performed solely with groundwaters and samples were taken from 35 groundwater wells at pre and post irrigation seasons in 2014. Samples were analyzed for 18 water quality parameters. SAR, RSC and %Na values were calculated to examine the suitability of groundwater for irrigation. Hierarchical cluster analysis and principal component analysis were used to assess the groundwater quality parameters. The average EC value of groundwater in the pre-irrigation period was 1.21 dS/m and 1.30 dS/m after irrigation in the study area. It was determined that there were problems in two wells pre-irrigation and one well post-irrigation in terms of RSC, while there was no problem in the wells in terms of SAR. Piper diagram and cluster analysis showed that most groundwaters had CaHCO3 type water characteristics and only 3% was NaCl- as the predominant type. Seawater intrusion was identified as the primary factor influencing groundwater quality. Multivariate statistical analyses to evaluate polluting sources revealed that groundwater quality is affected by seawater intrusion, ion exchange, mineral dissolution and anthropogenic factors. The use of multivariate statistical methods and geographic information systems to manage water resources will be beneficial for both planners and decision-makers.

scholarly journals Water quality assessment and geochemical processes in the unconfined coastal Boa Viagem Aquifer, Recife, NE Brazil

2021 ◽  
pp. 56-77
Author(s):  
Thyego Silva ◽  
Mariucha Lima ◽  
Teresa Leitão ◽  
Tiago Martins ◽  
Mateus Albuquerque
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Water Quality Assessment ◽  
Multivariate Statistical Analyses ◽  
Multivariate Statistical ◽  
Rock Interaction ◽  
Groundwater Samples ◽  
Water Rock Interaction

A hydrochemical study was conducted on the Quaternary Aquifer, in Recife, Brazil. Groundwater samples were collected in March–April 2015, at the beginning of the rainy season. Conventional graphics, ionic ratios, saturation indices, GIS mapping, and geostatistical and multivariate statistical analyses were used to water quality assessment and to characterize the main hydrochemical processes controlling groundwater’s chemistry. Q-mode hierarchical cluster analysis separated the samples into three clusters and five sub-clusters according to their hydrochemical similarities and facies. Principal Component Analysis (PCA) was employed to the studied groundwater samples where a three-factor model explains 80% of the total variation within the dataset. The PCA results revealed the influence of seawater intrusion, water-rock interaction, and nitrate contamination. The physico-chemical parameters of ~30% groundwaters exceed the World Health Organization (WHO) guidelines for drinking water quality. Nitrate was found at a concentration >10 mg NO3−/L in ~21% of the wells and exceeded WHO reference values in one. The integrated approach indicates the occurrence of the main major hydrogeochemical processes occurring in the shallow marine to alluvial aquifer as follow: 1) progressive freshening of remaining paleo-seawater accompanying cation exchange on fine sediments, 2) water-rock interaction (i.e., dissolution of silicates), and 3) point and diffuse wastewater contamination, and sulfate dissolution. This study successfully highlights the use of classical geochemical methods, GIS techniques, and multivariate statistical analyses (hierarchical cluster and principal component analyses) as complementary tools to understand hydrogeochemical processes and their influence on groundwater quality status to management actions, which could be used in similar alluvial coastal aquifers.

Spatial distribution and sources of dissolved trace metals in surface water of the Wei River, China

2013 ◽  
Vol 67 (4) ◽  
pp. 817-823 ◽  
Author(s):  
Li Jing ◽  
Li Fadong ◽  
Liu Qiang ◽  
Song Shuai ◽  
Zhao Guangshuai
Keyword(s):  
Drinking Water ◽  
Trace Metals ◽  
Trace Metal ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Environmental Data ◽  
Multivariate Statistical ◽  
Drinking Water Standard ◽  
The Impact ◽  
Wei River

For this study, 34 water samples were collected along the Wei River and its tributaries. Multivariate statistical analyses were employed to interpret the environmental data and to identify the natural and anthropogenic trace metal inputs to the surface waters of the river. Our results revealed that Zn, Se, B, Ba, Fe, Mn, Mo, Ni and V were all detected in the Wei River. Compared to drinking water guidelines, the primary trace metal pollution components (B, Ni, Zn and Mn) exceeded drinking water standard levels by 47.1, 50.0, 44.1 and 26.5%, respectively. Inter-element relationships and landscape features of trace metals conducted by hierarchical cluster analysis (HCA) identified a uniform source of trace metals for all sampling sites, excluding one site that exhibited anomalous concentrations. Based on the patterns of relative loadings of individual metals calculated by principal component analysis (PCA), the primary trace metal sources were associated with natural/geogenic contributions, agro-chemical processes and discharge from local industrial sources. These results demonstrated the impact of human activities on metal concentrations in the Wei River.

scholarly journals Geochemical classification of groundwater using multivariate statistical analysis in Latvia

2016 ◽  
Vol 47 (4) ◽  
pp. 799-813 ◽  
Author(s):  
Inga Retike ◽  
Andis Kalvans ◽  
Konrads Popovs ◽  
Janis Bikse ◽  
Alise Babre ◽  
...  
Keyword(s):  
Trace Elements ◽  
Statistical Methods ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Ion Chemistry ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Gypsum Dissolution ◽  
Major Ion

Multivariate statistical methods – principal component analysis (PCA) and hierarchical cluster analysis (HCA) – are applied to identify geochemically distinct groundwater groups in the territory of Latvia. The main processes observed to be responsible for groundwater chemical composition are carbonate and gypsum dissolution, fresh and saltwater mixing and ion exchange. On the basis of major ion concentrations, eight clusters (C1–C8) are identified. C6 is interpreted as recharge water not in equilibrium with most sediment forming minerals. Water table aquifers affected by diffuse agricultural influences are found in C3. Groundwater in C4 reflects brine or seawater admixture and gypsum dissolution in C5. C7 and C2 belong to typical bicarbonate groundwater resulting from calcite and dolomite weathering. Extremely low Cl− and SO42− are observed in C8 and described as pre-industrial groundwater or a solely carbonate weathering result. Finally, C1 seems to be a poorly defined subgroup resulting from mixing between other groups. This research demonstrates the validity of applying multivariate statistical methods (PCA and HCA) on major ion chemistry to distribute characteristic trace elements in each cluster even when incomplete records of trace elements are present.

scholarly journals Pyrolysis - Field lonization Mass Spectrometry - A New Method for Direct, Rapid Characterization of Tobacco

1986 ◽  
Vol 13 (5) ◽  
pp. 219-227 ◽  
Author(s):  
HR Schulten
Keyword(s):  
Mass Spectrometry ◽  
High Vacuum ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Mass Range ◽  
Linear Mapping ◽  
Mapping Technique ◽  
Ionization Mass ◽  
Multivariate Statistical ◽  
Rapid Characterization

AbstractFor the first time, pyrolysis - field ionization (Py-FI) mass spectra of the tobacco blends of three different cigarette brands have been recorded in the mass range up to 1000 mass units and evaluated by operational fingerprinting techniques. Due to the high reproducibility of the applied methods, all three tobacco blends could be differentiated clearly with several univariate or multivariate statistical methods. Feature scaling with Fisher ratios revealed that the signal at m/z 93, mainly due to aniline, is the most suited to distinguishing the tobacco blends analysed. Principal component analysis showed the variety of pyrolytic reactions during the thermal decomposition of tobacco in high vacuum. It revealed that, in addition to aniline, lignin-related signals can also be used for a clear differentiation. From the whole pattern of Py-FI mass spectrum, nearest-neighbour relationships are visualized by the non-linear mapping technique and further classification of tobacco blends is obtained by hierarchical cluster analysis. A thorough chemical interpretation of the data obtained should give new insights into the structure of tobacco and its pyrolytic decomposition. Pyrolysis - soft ionization mass spectrometry in combination with pattern recognition techniques appears to provide a useful tool for future investigations connected with the quality control of commercial tobacco products.

scholarly journals Hydrochemical Characteristics and Identification of Groundwater Pollution Sources in Tropical Savanna

2021 ◽  
Author(s):  
Abduljalal Abdulsalam ◽  
Mohammad Ramli ◽  
Nor Jamil ◽  
Zulfa Ashaari ◽  
Da’u Umar
Keyword(s):  
Groundwater Quality ◽  
Groundwater Pollution ◽  
Groundwater Resources ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Pollution Sources ◽  
Water Type ◽  
Multivariate Statistical ◽  
Factors Affecting ◽  
Anthropogenic Processes

Groundwater pollution of the watershed is mainly influenced by the multifaceted interactions of geogenic and anthropogenic processes. In this study, classic chemical and multivariate statistical methods were used to assess the groundwater quality and identify the potential pollution sources affecting the groundwater quality of Galma sub-watershed in a tropical savannah. For this purpose, the dataset of 18 groundwater quality variables covering 57 different sampling boreholes (BH) was used. The order of abundance of the main cations and anions in the samples are Ca2+ > Na+ > Mg2+ > K+ and HCO3− > Cl− > SO4−2 > NO3− respectively. Piper diagram classified the groundwater types of the watershed into mixed Ca–Mg–Cl type of water, which means no cations and anions exceeds 50%. The second dominant water type was Ca–Cl. The Mg–HCO3 water type was found in BH 9, and Na–Cl water type in BH 29 respectively. Hierarchical cluster analysis grouped the sampling boreholes into five statistically significant clusters based on similarities of groundwater quality characteristics. Principal component extracted two principal components that explained around 65% of the total variance, which natural and anthropogenic processes especially agricultural activities as the dominant factors affecting the groundwater quality. The findings of this study are useful to the policy and decision-makers for formulating efficient groundwater utilization and management plans for the groundwater resources.

scholarly journals Application of Multivariate Analysis in the Evaluation of Metals Distribution in Soil from Awwal Mining Site Kebbi, Nigeria

2020 ◽  
Vol 27 (1) ◽  
pp. 17-24
Author(s):  
G. Shehu ◽  
I.B. Koki
Keyword(s):  
Multivariate Analysis ◽  
Health And Safety ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Underground Water ◽  
Soil Samples ◽  
Toxic Heavy Metals ◽  
Multivariate Statistical ◽  
Mining Site ◽  
Metal Levels

Multivariate statistical techniques such as principal component analysis (PCA), factor analysis (FA), and hierarchical cluster analysis (HCA) were utilized for the evaluation of metal distribution and variations in the soil at Awwal mining site. PCA was used to determine a reduced number of three principal components (PC) indicating about 82% of the total variation in the soil samples. The result of FA justifies the results of the PCA obtained. HCA classified the soil samples at the sites into two clusters, with cluster one having the higher metal levels, while cluster two had low metal levels but characterized with dominant toxic heavy metals (As and Pb). The results of the multivariate analysis showed that natural percentage abundance in soil and mineral composition of the mining ores were the main sources of the metals under study. Due to high metal levels in the soils, disposal and management of the mining waste/tailings and rehabilitation of the mining site after closure of mining should be done with care and caution to avoid leaching of the toxic metals to surface and underground water for the protection of health and safety of the neighboring community. Keywords: Soil, Metals, Mining, Multivariate analysis, Awwal.

Spectral Comparison of Ruminant Feeds Originating from Mediterranean Regions

1998 ◽  
Vol 6 (A) ◽  
pp. A79-A82
Author(s):  
S.J. Lister ◽  
M.S. Dhanoa ◽  
W. Ebenezer ◽  
S. Lopez ◽  
J. France
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Fats And Oils ◽  
Multivariate Statistical ◽  
Beet Root ◽  
Mediterranean Regions ◽  
Sugar Beet Root ◽  
By Products

The potential of near infrared (NIR) spectroscopy and multivariate statistical techniques for the identification of new feed resources for ruminants was examined. Fifty diverse Mediterranean feeds including cereal fodder, legume fodder, vetch fodder, permanent meadow hay, cereal straw, legume straw, sugar beet root by-products, concentrates and agricultural by-products were used in this study. Principal component analysis and hierarchical cluster analysis were used to examine the distribution and inter-relationships between the different feeds. Overlap was observed between the different categories of feeds and several concentrate samples and by-products appeared at the extremes of the population. Spectral regions characteristic of fats and oils were associated with the discrimination. NIR spectra may be used to highlight differences and differentiate between different ruminant feeds. Qualitative analysis allows for comparison of samples on the basis of their spectral chemistry alone.

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