scholarly journals Application of Unsupervised Multivariate Analysis Methods to Raman Spectroscopic Assessment of Human Dental Enamel

Computers ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 5
Author(s):  
Iulian Otel ◽  
Joao Silveira ◽  
Valentina Vassilenko ◽  
António Mata ◽  
Maria Luísa Carvalho ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Raman Spectra ◽  
Dental Enamel ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Raman Spectroscopic ◽  
Data Treatment ◽  
Analysis Methods ◽  
Before And After ◽  
Protective Treatment

This work explores the suitability of data treatment methodologies for Raman spectra of teeth using multivariate analysis methods. Raman spectra were measured in our laboratory and obtained from control enamel samples and samples with a protective treatment before and after an erosive attack. Three different approaches for data treatment were undertaken in order to evaluate the aptitude of distinguishing between groups: A—Principal Component Analysis of the numerical parameters derived from deconvoluted spectra; B—PCA of average Raman spectra after baseline correction; and C—PCA of average raw Raman spectra. Additionally, Hierarchical Cluster Analysis were applied to Raman spectra of enamel measured with different laser wavelengths (638 nm or 785 nm) to evaluate the most suitable choice of illumination. According to the different approaches, PC1 scores obtained between control and treatment group were A—50.5%, B—97.1% and C—83.0% before the erosive attack and A—55.2%, B—93.2% and C—87.8% after an erosive attack. The obtained results showed that performing PCA analysis of raw or baseline corrected Raman spectra of enamel was not as efficient in the evaluation of samples with different treatments. Moreover, acquiring Raman spectra with a 785 nm laser increases precision in the data treatment methodologies.

scholarly journals EFEITO DE DOSES DE INSETICIDA E ADJUVANTE NO CONTROLE DE PHYLLOCNISTIS CITRELLASTAINTON (LEPIDOPTERA: GRACILLARIIDAE) EM LIMA ÁCIDA TAHITI

2018 ◽  
Vol 14 (3) ◽  
pp. 72-81
Author(s):  
Marcos Doniseti Michelotto ◽  
Willians César Carrega ◽  
Juliana Altafin Galli ◽  
Maycon Ferraz ◽  
Tamiris Marion de Souza ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Statistical Design ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Fruit Production ◽  
Leaf Miner ◽  
Control Treatment ◽  
Citrus Leaf ◽  
Acid Lime ◽  
And Control

Citrus leaf miner, Phyllocnistis citrellaStainton,1856 (Lepidoptera,Gracillariidae), is one of the main pests of the culture of acid lime Tahiti and control chemical is the primary method of control. The objective of this study was to testthe effect of insecticide treatments associated with adjuvant, in increasing the efficiency of control of citrus leaf miner in acid lime Tahiti. An experiment was installed in statistical design of randomized blocks with five treatments and four repetitions. The treatments consisted of a control treatment without application of pesticides and the use of the insecticide Ampligo® (clorantraniliprole + lambda-cialotrina) at the recommended dosage, at half the recommended dose and associated with the adjuvant. Three applications were carried out respecting the shortage period of the product. The damages of citrus leaf miner wereassessed in five branches of the central plant.Fruits wereharvest and determining the number and the mass (kg) of fruit per plant.The data were subjected to analysis of variance (F test) and the means were compared by Tukey test (P ≤ 0.05). Also the data were submitted to multivariate analysis using hierarchical cluster analysis techniques and principal component analysis (PCA). All treatments were effective in reducing the attack symptoms of citrus leaf miner, highlighting insecticide in half dosage and application of insecticide in half dosage with the adjuvant.The multivariate analysis was effective in discriminating application treatments of insecticides and adjuvant, and showa direct relationship between the citrus leaf miner and the decrease in fruit production.

scholarly journals A study of new antimalarial artemisinins through molecular modeling and multivariate analysis

2010 ◽  
Vol 75 (11) ◽  
pp. 1533-1548 ◽  
Author(s):  
João Ferreira ◽  
Antonio Figueiredo ◽  
Jardel Barbosa ◽  
Maria Cristino ◽  
Williams Macedo ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Antimalarial Activity ◽  
External Validation ◽  
Geometry Optimization ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Partial Least Square ◽  
Least Square ◽  
Basis Set ◽  
Validation Parameters

Artemisinin and 18 derivatives with antimalarial activity against W-2 strains of Plasmodium falciparum were studied through quantum chemistry and multivariate analysis. The geometry optimization of the structures was realized with the Hartree-Fock (HF) theory and 3-21G basis set. Maps of molecular electrostatic potential (MEP) and molecular docking were used to investigate the interaction between the ligands and the receptor (heme). Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were employed to select the most important descriptors related to activity. A predictive model was generated by the Partial Least Square (PLS) method through 15 molecules and 4 used as an external validation set, which were selected in the training set, the validation parameters of which are Q2 = 0.85 and R2 = 0.86. The model included as molecular parameters, the radial distribution function, RDF060e, the hydration energy, HE, and the distance between the O1 atom from the ligand and the iron atom from heme, d(Fe-O1). Thus, the synthesis of new derivatives may follow the results of the MEP maps and the PLS analysis.

scholarly journals Analysis of Geomagnetic Ap Index on Worldwide Earthquake Occurrence using the Principal Component Analysis and Hierarchical Cluster Analysis

2021 ◽  
Vol 50 (4) ◽  
pp. 1157-1164
Author(s):  
Nur Hidayah Ismail ◽  
Nazhatulshima Ahmad ◽  
Nur Anisah Mohamed ◽  
Mohammad Redzuan Tahar
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Research Centre ◽  
Before And After ◽  
Solar Events ◽  
Ap Index

Geoeffective solar events, especially the coronal mass ejection (CME) and the high-speed solar wind (HSSW) will induce geomagnetic storm upon its arrival to Earth. The solar events could trigger an earthquake occurred during the arrival. In this study, the focus is on the proxy of the geoeffective solar events, which is the geomagnetic Ap index and the data of shallow worldwide earthquakes. The main objective was to investigate the impact of geomagnetic storms on the occurrences of earthquakes from 1994 to 2017 from a statistical perspective. The geomagnetic Ap index data was obtained from the Helmholtz-Centre Postdam - GFZ German Research Centre for Geosciences and the shallow worldwide earthquake data were from the United States Geological Survey (USGS) earthquake catalogue. The Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were used to analyse the data. Two groups were obtained from the PCA biplot: Group 1 - before the event (Day-4 to Day-1) and Group 2 - after the event group (Day 0 to Day+4). A two-cluster solution was obtained from the HCA, which shows that days before and after geostorm are divided into two main clusters. The statistical results show that earthquakes activity might have different behaviour before and after the geostorm occurred. In conclusion, the results emphasize that there are differences between days before and after the geostorm occurrence, hence, the solar influence upon earthquake occurrences cannot be neglected entirely.

QUANTITATIVE EVALUATION OF MULTIVARIATE ANALYSIS METHODS FOR EXCITATION–EMISSION SPECTROSCOPY

2013 ◽  
Vol 25 (02) ◽  
pp. 1250027 ◽  
Author(s):  
Chun Yu ◽  
Tzu-Chien Hsiao ◽  
Chii-Wann Lin
Keyword(s):  
Multivariate Analysis ◽  
System Integration ◽  
Multivariable Analysis ◽  
Principal Component ◽  
Automatic Measurement ◽  
Partial Least Square ◽  
Least Square ◽  
Prediction Ability ◽  
Analysis Methods ◽  
The Difference

Multivariable Analysis Methods have been used widely in Spectroscopy Analysis. Partial least square (PLS) and principal component analysis (PCA) are the two most popular methods due to their excellent ability in data components analysis and results prediction. This work derived 1D/2D PLS and 1D/2D PCA based on the viewpoint of three-layer artificial neural networks, and uses theoretical proving to figure out the essences of these two methods. Two 2D experimental dataset was used to verify the calibration and prediction ability, furthermore, the similarity and dissimilarity of PLS and PCA. The finding showed that both the 1D/2D PLS and 1D/2D PCA methods use maximum covariance and minimum sum of square error to figure out the relationship between independent and dependent variables. The dissimilarity is that, weight vectors calibration of PLS is cross-correlation, and that of PCA is autocorrelation. The difference causes that the PCA method would keep more principal characters than PLS under insufficient sample among and provides better calibration ability. PLS would provide higher performance in prediction under sufficient sample among. For the needs of system implementation of spectroscopic measurement and analysis, this study designed "Multivariate Analysis Toolkits for LabVIEW" for the convenient implementation in automatic measurement system integration.

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.

scholarly journals T-induced displacive phase transition of end-member Pb-lawsonite

2016 ◽  
Vol 80 (2) ◽  
pp. 249-267 ◽  
Author(s):  
Martin Ende ◽  
Bernd Wunder ◽  
Monika Koch-Müller ◽  
Thoma. Pippinger ◽  
Gernot Buth ◽  
...  
Keyword(s):  
Phase Transition ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Temperature Structure ◽  
X Ray Diffraction ◽  
Raman Spectroscopic ◽  
Piston Cylinder ◽  
Displacive Phase Transition ◽  
Structural Environment ◽  
Xrd Patterns

AbstractPb-lawsonite, PbAl2[(OH)2|Si2O7]·H2O, space group Pbnm, was synthesized as crystals up to 15 μm × 5 μm × 5 μm in size by a piston cylinder technique at a pressure of ∼4 GPa and a temperature of 873 ± 10 K. Temperature-dependent powder and single-crystal X-ray diffraction (XRD) analyses partly using synchrotron radiation as well as Raman spectroscopic investigations reveal a phase transition around 445 K resulting in the Cmcm high-temperature structure. The transformation temperature is considerably higher than that of lawsonite around 273 K, which is characterized predominantly by proton order/disorder. The transition is confirmed using principal component analysis and subsequent hierarchical cluster analysis on both the powder XRD patterns and the Raman spectra. Furthermore, a non-uniform change is observed around 355 K, which is not as pronounced as the 445 K transition and apparently comes from enhanced hydrogen bonding, which stops the atom shifts in Pb-lawsonite. These are the same bonds that mainly characterize the phase transition in lawsonite around 273 K. In contrast, the structural transition of Pblawsonite at 445 K seems to originate from the interaction of the SiO4tetrahedra and AlO6octahedra framework with the Pb2+cation. The structural environment of Pb2+can be described by a 12-fold coordination above 445 K, which changes towards irregular ten-fold coordination below this temperature. An assignment of the O–H stretching Raman bands confirms moderately strong H bonds in Pb-lawsonite, whereas both strong and weak H bonds exist in lawsonite. Therefore, a further phase transition of Pblawsonite, similar to that of lawsonite around 273 K, is not expected.

scholarly journals PHENETIC AND UNSUPERVISED MULTIVARIATE ANALYSIS SYZYGIUM POLYANTHUM (WIGHT) WALP

2021 ◽  
Vol 52 (1) ◽  
pp. 249-258
Author(s):  
Abdulrahman & et al.
Keyword(s):  
Multivariate Analysis ◽  
Woody Plants ◽  
De Novo ◽  
Morphological Variability ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Peninsular Malaysia ◽  
Data Matrix ◽  
Documented Information ◽  
Study Species

Myrtaceae family is widely distributed in Asia has been the largest group of plant; mainly trees and few shrubs. Distributed all over the world in tropical and subtropical areas. Syzygium is the largest genus with economical value found all over the Malaysian Peninsular. Evolutionary relationships within the Syzygium is unclear and there are currently no reliable criteria to divide the genus into manageable entities for systematic study. Species of Syzygium is the richest genus of woody plants in South East Asia with approximately 1000 or more species but little is known about the genus. Syzygium polyanthum Wight is one of the favourites Ulam that have been consumed for ages in Peninsular Malaysia and also as herbal medicine. The species is widely misunderstood due to extreme morphological variability, similarity in aroma and flavor. The species is substituted or adulterated with several other species. The study was aimed to construct phenetic tree and unsupervised multivariate analysis from morphological and anatomical the data matrix.  Phenetic analysis, Principal component and hierarchical cluster analysis revealed they are two different cultivars species  but inter variation exited among cultivars of same species.  The above documented information has added new  taxonomic information with regard to the identification of the cultivars in Peninsular Malaysia. The study recommends further study on de novo sequence of Serai kayu and Serai kayu hutan.

scholarly journals Total luminescence spectroscopy for differentiating between brandies and wine distillates

2009 ◽  
Vol 27 (No. 6) ◽  
pp. 425-432 ◽  
Author(s):  
J. Tóthová ◽  
J. Sádecká ◽  
P. Májek
Keyword(s):  
Multivariate Analysis ◽  
Emission Spectra ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Luminescence Spectroscopy ◽  
Luminescence Spectra ◽  
Classification Error ◽  
Excitation Wavelength ◽  
Data Sets ◽  
Excitation Spectra

In this study, the differentiation was investigated between brandy and wine distillate samples by fluorescence spectroscopy in combination with multivariate analysis. The samples corresponding to eight brandies from three producers and sixteen wine distillates from five producers were acquired in the local supermarkets. Total luminescence spectra of diluted and undiluted samples were recorded. In order to extract reliable information from the data sets, two multivariate analysis methods, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), were applied separately on the excitation and emission spectra. The best differentiation was achieved using the emission spectra (400–470 nm) recorded at the excitation wavelength of 340 nm, or the excitation spectra (240–380 nm) recorded at the emission wavelength of 450 nm. The similarity map defined by the PC1 and PC2 of the PCA performed on the excitation spectra accounted for 94.9% of the total variance (PC1 90.3%, PC2 4.6%) and allowed a good discrimination between the beverages. Although the PCA similarity map defined by the PC1 (84.2%) and PC2 (13.0%) performed on the emission spectra did not lead to a clear discrimination between the beverages, a general trend pointing out the brandies and wine distillates was observed on the map. HCA performed on the excitation spectra provided a better differentiation between the two classes, without any classification error, while HCA performed on the emission spectra allowed 95.8% correct classification.

scholarly journals Selection of Experimental Hybrids of Strawberry Using Multivariate Analysis

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 598 ◽  
Author(s):  
Eneide Barth ◽  
Juliano Tadeu Vilela de Resende ◽  
Aline Fabiana Paladini Moreira ◽  
Keny Henrique Mariguele ◽  
André Ricardo Zeist ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Block Design ◽  
Selection Index ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
High Variability ◽  
Soluble Solids ◽  
Fresh Market ◽  
Commercial Cultivars ◽  
Selection Of

The selection of superior strawberry genotypes is a complex process due to the high variability after hybridization that is caused by the octoploid nature and the heterozygosis, making the selection of multiple traits difficult. This study aimed to select strawberry hybrids with the potential for fresh consumption and/or processing by applying multivariate analysis to obtain traits of interest simultaneously. Hybrids were obtained from the crossing among seven commercial cultivars, defining a selection of 10% of them. The experimental design consisted of an augmented block design, with two commercial cultivars, Camarosa and Camino Real, as the controls. Different variables, including the number and average mass of commercial fruits, total fruit mass, pH, soluble solids (SS), titratable acidity (TA), SS/TA ratio, reducing sugars, pectin, ascorbic acid, phenolic compounds, and anthocyanin’s, were assessed. The selection of hybrids was based on the Mulamba and Mock rank-summation index, principal component analysis, and Ward’s hierarchical cluster analysis. The selection index was based on different weights being adopted for fresh market and processing. The assessed traits had high variability between hybrids. The highest selection gains were obtained for production traits, but the different weight assignment resulted in different classifications of hybrids for both fresh consumption and processing. Most of the hybrids selected by the index remained in the same group in the principal component and hierarchical cluster analyses, which indicates that multivariate analysis is a valuable tool for assisting in the selection of superior hybrids in the strawberry crop.

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