Discrimination of management effects on soil parameters by using principal component analysis: a multivariate analysis case study

Sena, Marcelo M.; Frighetto, R. T. S.; Valarini, P. J.; Tokeshi, H.; Poppi, Ronei J.

doi:10.1016/s0167-1987(02)00063-6

Cited by 156 publications

(78 citation statements)

References 28 publications

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“…Similar results were obtained with FDA and urease which participate in esterase activity and nitrogen cycle respectively. These results are supported by Gupta and Germida (1988), Bandick and Dick (1999), Riffaldi et al, (2002), and Sena et al, (2002) under organic practices. The soil enzyme activities were due to biochemical activities of microorganism present in soil.…”

Section: Agronomical Parameters and Crop Yieldsupporting

confidence: 65%

Evaluation of Soil Health in Short Term Organic Vs Conventional Practices during Paddy Cultivation

Jaggi¹,

Joshi²,

Dasila³

et al. 2018

Int.J.Curr.Microbiol.App.Sci

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Section: Agronomical Parameters and Crop Yieldsupporting

confidence: 65%

Evaluation of Soil Health in Short Term Organic Vs Conventional Practices during Paddy Cultivation

Jaggi¹,

Joshi²,

Dasila³

et al. 2018

Int.J.Curr.Microbiol.App.Sci

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“…This analysis selected a set of soil properties, subsequently used in cluster analysis (Sena et al, 2002). Cluster analysis is a hierarchical grouping analysis, which serves to group more similar accessions (Freddi et al, 2008).…”

Section: Methodsmentioning

confidence: 99%

Magnetic susceptibility to identify landscape segments on a detailed scale in the region of Jaboticabal, São Paulo, Brazil

Barrios

Marques

Panosso

et al. 2012

Rev. Bras. Ciênc. Solo

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SUMMARYThe agricultural potential is generally assessed and managed based on a onedimensional vision of the soil profile, however, the increased appreciation of sustainable production has stimulated studies on faster and more accurate evaluation techniques and methods of the agricultural potential on detailed scales. The objective of this study was to investigate the possibility of using soil magnetic susceptibility for the identification of landscape segments on a detailed scale in the region of Jaboticabal, São Paulo State. The studied area has two slope curvatures: linear and concave, subdivided into three landscape segments: upper slope (US, concave), middle slope (MS, linear) and lower slope (LS, linear). In each of these segments, 20 points were randomly sampled from a database with 207 samples forming a regular grid installed in each landscape segment. The soil physical and chemical properties, CO 2 emissions (FCO 2 ) and magnetic susceptibility (MS) of the samples were evaluated represented by: magnetic susceptibility of air-dried fine earth (MS ADFE ), magnetic susceptibility of the total sand fraction (MS TS ) and magnetic susceptibility of the clay fraction (MS Cl ) in the 0.00 0.15 m layer. The principal component analysis showed that MS is an important property that can be used to identify landscape segments, because the correlation of this property within the first principal component was high. The hierarchical cluster analysis method identified two groups based on the variables selected by principal component analysis; of the six selected variables, three were related to magnetic susceptibility. The landscape segments were differentiated similarly by

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“…15,16 HCA was performed on the scores of a PCA model, with two PCs, and using Mahalanobis distance and the Ward's method as the similarity criterion. The resulting dendrogram is shown in Figure 8, in which it is possible to observe three clusters corresponding to the same groups obtained by PCA ( Figure 5).…”

Section: An Exploratory Pca Model For the Atr-ftir Datamentioning

confidence: 99%

“…PCA 15,16 allows a summarized interpretation of a data set, revealing latent structures through new independent vectors called principal components (PCs). The combined analysis of score and loading plots allows finding out spectral regions related to sample groups.…”

Section: Introductionmentioning

confidence: 99%

Analysis of seized cocaine samples by using chemometric methods and FTIR spectroscopy

Rodrigues¹,

Cardoso²,

Andrade³

et al. 2013

J. Braz. Chem. Soc.

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O objetivo deste trabalho foi desenvolver uma metodologia quimiométrica para descrever o perfil químico de amostras de cocaína apreendidas no estado de Minas Gerais (Brasil). A identificação de adulterantes e a quantificação do teor de cocaína foram feitas por cromatografia gasosa acoplada à espectrometria de massa (GC-MS). Espectros de 91 amostras foram obtidos por espectroscopia no infravermelho usando refletância total atenuada (ATR-FTIR), e utilizados na construção de um modelo de análise de componentes principais (PCA). A primeira componente principal (PC1) discriminou as amostras de maior pureza das mais diluídas/adulteradas, nas quais foram identificadas lidocaína, cafeína e benzocaína. PC2 discriminou a forma química da cocaína, cloridrato ou base. Além disso, dois modelos supervisionados discriminantes (mínimos quadrados parciais para análise discriminante, PLS-DA) foram desenvolvidos para classificar as amostras em função de sua diluição (abaixo e acima de 15% m/m) e de sua forma química, apresentando taxas de acerto que variaram entre 83 e 97%. Os modelos de classificação constituem uma ferramenta simples, rápida e não destrutiva, de grande valor para peritos forenses e investigadores criminais.The aim of this article was to develop a chemometric methodology for determining the chemical profile of cocaine samples seized in Minas Gerais State, Brazil. The adulterant detection and the cocaine determination were performed by gas chromatography-mass spectrometry (GC-MS). Spectra of 91 samples were obtained by attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) and used to build an exploratory principal component analysis (PCA) model. The first principal component (PC1) discriminated samples of more purity from the more diluted/adulterated ones, which were characterized by the presence of lidocaine, caffeine and benzocaine. PC2 discriminated the two chemical forms of cocaine, hydrochloride and base. In addition, two supervised discriminant partial least-squares models (partial least-squares discriminant analysis, PLS-DA) were developed for classifying the samples according to dilution (above and below 15% m/m) and chemical form, with a rate of success that varied between 83 and 97%. The classification models constitute a simple, rapid and non-destructive tool, of great value for both forensic experts and criminal investigators. Keywords: illicit drugs, MID infrared, principal component analysis, cocaine, chemometrics IntroductionCocaine is an alkaloid extracted from plants of the genera Erythroxylum (E. novagranatense and E. coca). United Nations Office on Drugs and Crime (UNODC) considers cocaine, after heroin, the second most problematic drug worldwide in terms of negative health consequences and probably the most problematic drug in terms of trafficking-related violence. Estimates suggest that 440 t of pure cocaine were consumed in the world in 2009, which would be in line with a total production estimate of 1,111 t, wholesale purity-adjusted seizures Analysis of Seized Cocain...

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Discrimination of management effects on soil parameters by using principal component analysis: a multivariate analysis case study

Cited by 156 publications

References 28 publications

Evaluation of Soil Health in Short Term Organic Vs Conventional Practices during Paddy Cultivation

Evaluation of Soil Health in Short Term Organic Vs Conventional Practices during Paddy Cultivation

Magnetic susceptibility to identify landscape segments on a detailed scale in the region of Jaboticabal, São Paulo, Brazil

Analysis of seized cocaine samples by using chemometric methods and FTIR spectroscopy

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