Examining large databases: a chemometric approach using principal component analysis

Meglen, Robert R.

doi:10.1016/0304-4203(92)90103-h

Cited by 234 publications

(95 citation statements)

References 4 publications

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“…These data indicate that the ADV data are sufficiently 'clean' to be used in calculating SSC and velocity variability. Principal component analysis PCA provides information on the most meaningful parameters and helps reduce large, multivariate databases to the principal factors controlling data variability (Meglen 1992;Beaugrand 2004). PCA transforms a large set of inter-correlated variables into a small number of statistically independent variables or principal components (PCs; Jolliffe 2002; Singh et al 2005;Kannel et al 2007).…”

Section: Effective Density Fractal Dimension Settling Velocitymentioning

confidence: 99%

On the variability of near-bed floc size due to complex interactions between turbulence, SSC, settling velocity, effective density and the fractal dimension of flocs

Yang

Wang

et al. 2016

Geo-Mar Lett

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Interactions between turbulence, suspended sediment concentration (SSC), settling velocity, effective density, fractal dimension, and floc size were studied on the tide-dominated, muddy coastal shelf of the southwestern Yellow Sea, China. The measurements were carried out in July 2013 at two sites located in water depths of 21.2 and 22.1 m. Negative correlations were observed between shear rate, SSC, effective density, and mean floc size, which supports the results of previous numerical, experimental, and field studies. A significant positive correlation was observed between near-bed SSC and shear rate, an indication that SSC variations are controlled by turbulence and re-suspension. In addition, significant linear relationships were found between settling velocity and other parameters (floc size, turbulence, SSC, effective density, and fractal dimension) at the two sites, indicating that the controlling factors on settling velocity are spatially variable. Principal component analysis was applied to determine the relative importance of turbulence, flocculation ability, and SSC as controls on floc size in situ. The relative contributions of turbulence, flocculation ability, and SSC to floc size (at both sites) were~33.0%, 30.3%, and 29.7%, respectively, this being a new field-based quantitative analysis of the controls on floc size. The findings demonstrate that, in nature, flocculation ability affects floc size to the same degree as turbulence and SSC. Therefore, predictions of floc size in coastal marine environments require constraints not only on turbulence and SSC, but also on flocculation ability.

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Section: Effective Density Fractal Dimension Settling Velocitymentioning

confidence: 99%

On the variability of near-bed floc size due to complex interactions between turbulence, SSC, settling velocity, effective density and the fractal dimension of flocs

Yang

Wang

et al. 2016

Geo-Mar Lett

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“…Only variables with correlation coefficients ≥0.7 were included on Figs. 1 and 2 (Meglen 1992). PCA clusters were determined via hierarchical cluster analysis on PC1 and PC2 scores.…”

Section: Principal Components Analysismentioning

confidence: 99%

Tissue-specific fatty acid profiles of vent-obligate tonguefishes (Symphurus spp.) on volcanic arcs in the western Pacific Ocean

et al. 2016

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trends could not be discerned for S. thermophilus (Mariana Arc). The fatty acid composition of Symphurus spp. suggests that PUFA-rich prey were not limiting at the sampled vents, although we lack information about how these fishes metabolize lipid. Stable carbon isotope ratios (δ 13 C) of several individual fatty acids in fish from Nikko and Daikoku suggest a photic zone origin. Benthic-pelagic coupling on intra-oceanic volcanic arcs may strongly influence the presence and persistence of resident vertebrates.

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“…Biochemical oxygen demand (BOD5) was estimated following the Winkler's method after incubating a water sample for 5 days. Total alkalinity was measured using titrimetric method.NO2 --N, NO3 --N, PO4 (Mellinger 1987;Meglen 1992;Wenning and Erickson 1994).Correlation of principal components and original variables are given by loadings. Cluster analysis uncovers intrinsic structure or underlying behaviour of a dataset without making a priori assumptions about the data.…”

Section: Sampling Locationsmentioning

confidence: 99%

Water quality of Nilwala River, Sri Lanka in relation to land use practices

Jayawardana¹,

jayathunga²,

Edirisinghe³

2016

Sri Lanka J. Aquat.

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The Nilwala River is the third largest in the southern province of Sri Lanka which runs through 72 km along its cause. Conversion of forest lands to agricultural lands, expansion of urban areas and clearing of land for construction purposes in the catchment are major issues affecting water quality in Nilwala River. The water quality of the Nilwala River lower basin at 16 locations was estimated for 4 months from September2010 to December 2010 in two-week intervals. Water quality parameters pH, Electrical conductivity(EC), Dissolved oxygen (DO), Biochemical Oxygen demand (BOD5), turbidity, colour, Total Dissolved Solids (TDS), total hardness, alkalinity, Nitrite-N (NO2 --N), Nitrate -N (NO3 --N), Phosphate (PO4 3--P), Sulphate (SO4 3-), Total Fe, Ca 2+ , F -and Cl -were evaluated in the sampling locations. The spatial and temporal variation of water quality associated with different land use categories were analyzed using Two-way ANOVA. Principal Component Analysis (PCA) and Cluster Analysis (CA) were also conducted to determine the association between water quality variation and land use impacts of the catchment. A significant (p <0.05) spatial variation of water quality parameters, except the hardness, Fluoride and Ca +2 among sites were observed to be associated with different land use categories. No significant (p >0.05) temporal variability in water quality parameters except Nitrite and hardness was detected. PCA and CA also indicated a strong association between the water quality variables and the land use types of the river basin. Urban impacts, land clearance in the catchment and sand mining activities of the river bed were found to be the most significant impacts to the water quality of the river and areas with forest cover having positive impact to the water quality. These findings emphasize the need of better catchment management approaches and remediation of urban impacts on river water quality.

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Examining large databases: a chemometric approach using principal component analysis

Cited by 234 publications

References 4 publications

On the variability of near-bed floc size due to complex interactions between turbulence, SSC, settling velocity, effective density and the fractal dimension of flocs

On the variability of near-bed floc size due to complex interactions between turbulence, SSC, settling velocity, effective density and the fractal dimension of flocs

Tissue-specific fatty acid profiles of vent-obligate tonguefishes (Symphurus spp.) on volcanic arcs in the western Pacific Ocean

Water quality of Nilwala River, Sri Lanka in relation to land use practices

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