UV-visible scanning spectrophotometry and chemometrics analysis as tools to build descriptive and classification models for propolis from southern Brazil
Maíra Maciel Tomazzoli
Tuesday, January 20, 2015
setwd("C:/Users/Maira/Desktop/Aula Miguel R/Metabolomics-package/Metabolomics-package")
source("scripts/init.R")
uv.propolis.metadata.file = "Datasets/Propolis/UVV/metadata/varredura_metadata3.csv"
uv.propolis.data.file = "Datasets/Propolis/UVV/data/varredurapositiva2.csv"
label.x = "wavelength(nm)"
label.val = "absorbance"
uv.propolis.ds = read.dataset.csv(uv.propolis.data.file, uv.propolis.metadata.file,
description = "UV data for propolis", type = "uvv-spectra", format = "row",
label.x = label.x, label.values = label.val, sep.meta = ";", sep = ";")Inspeção preliminar de dados
sum.dataset(uv.propolis.ds)## Dataset summary:
## Valid dataset
## Description: UV data for propolis
## Type of data: uvv-spectra
## Number of samples: 195
## Number of data points 521
## Number of metadata variables: 5
## Label of x-axis values: wavelength(nm)
## Label of data points: absorbance
## Number of missing values in data: 0
## Mean of data values: 0.1777
## Median of data values: 1e-04
## Standard deviation: 0.56
## Range of values: 0 4.499
## Quantiles:
## 0% 25% 50% 75% 100%
## 0.0000 0.0001 0.0001 0.0200 4.4990get.metadata(uv.propolis.ds)## names group color seasons replicates
## VeD131_1 VeD131 sumdez red summer 1
## VeD131_2 VeD131 sumdez red summer 2
## VeD131_3 VeD131 sumdez red summer 3
## VeD132_1 VeD132 sumdez red summer 1
## VeD132_2 VeD132 sumdez red summer 2
## VeD132_3 VeD132 sumdez red summer 3
## VeD133_1 VeD133 sumdez green summer 1
## VeD133_2 VeD133 sumdez green summer 2
## VeD133_3 VeD133 sumdez green summer 3
## VeD134_1 VeD134 sumdez green summer 1
## VeD134_2 VeD134 sumdez green summer 2
## VeD134_3 VeD134 sumdez green summer 3
## VeF141_1 VeF141 sumfev green summer 1
## VeF141_2 VeF141 sumfev green summer 2
## VeF141_3 VeF141 sumfev green summer 3
## VeF142_1 VeF142 sumfev green summer 1
## VeF142_2 VeF142 sumfev green summer 2
## VeF142_3 VeF142 sumfev green summer 3
## VeF143_1 VeF143 sumfev green summer 1
## VeF143_2 VeF143 sumfev green summer 2
## VeF143_3 VeF143 sumfev green summer 3
## VeF144_1 VeF144 sumfev green summer 1
## VeF144_2 VeF144 sumfev green summer 2
## VeF144_3 VeF144 sumfev green summer 3
## VeF145_1 VeF145 sumfev green summer 1
## VeF145_2 VeF145 sumfev green summer 2
## VeF145_3 VeF145 sumfev green summer 3
## VeF146_1 VeF146 sumfev green summer 1
## VeF146_2 VeF146 sumfev green summer 2
## VeF146_3 VeF146 sumfev green summer 3
## VeF147_1 VeF147 sumfev green summer 1
## VeF147_2 VeF147 sumfev green summer 2
## VeF147_3 VeF147 sumfev green summer 3
## VeJ1411_1 VeJ1411 sumjan green summer 1
## VeJ1411_2 VeJ1411 sumjan green summer 2
## VeJ1411_3 VeJ1411 sumjan green summer 3
## VeJ1412_1 VeJ1412 sumjan green summer 1
## VeJ1412_2 VeJ1412 sumjan green summer 2
## VeJ1412_3 VeJ1412 sumjan green summer 3
## VeJ1413_1 VeJ1412 sumjan green summer 1
## VeJ1413_2 VeJ1412 sumjan green summer 2
## VeJ1413_3 VeJ1412 sumjan green summer 3
## VeJ1414_1 VeJ1414 sumjan green summer 1
## VeJ1414_2 VeJ1414 sumjan green summer 2
## VeJ1414_3 VeJ1414 sumjan green summer 3
## VeJ1421_1 VeJ1421 sumjan green summer 1
## VeJ1421_2 VeJ1421 sumjan green summer 2
## VeJ1421_3 VeJ1421 sumjan green summer 3
## VeJ1422_1 VeJ1422 sumjan green summer 1
## VeJ1422_2 VeJ1422 sumjan green summer 2
## VeJ1422_3 VeJ1422 sumjan green summer 3
## VeJ1423_1 VeJ1423 sumjan green summer 1
## VeJ1423_2 VeJ1423 sumjan green summer 2
## VeJ1423_3 VeJ1423 sumjan green summer 3
## VeJ1424_1 VeJ1424 sumjan green summer 1
## VeJ1424_2 VeJ1424 sumjan green summer 2
## VeJ1424_3 VeJ1424 sumjan green summer 3
## VeJ1431_1 VeJ1431 sumjan brown summer 1
## VeJ1431_2 VeJ1431 sumjan brown summer 2
## VeJ1431_3 VeJ1431 sumjan brown summer 3
## VeJ1432_1 VeJ1432 sumjan green summer 1
## VeJ1432_2 VeJ1432 sumjan green summer 2
## VeJ1432_3 VeJ1432 sumjan green summer 3
## VeJ1433_1 VeJ1433 sumjan green summer 1
## VeJ1433_2 VeJ1433 sumjan green summer 2
## VeJ1433_3 VeJ1433 sumjan green summer 3
## VeJ1434_1 VeJ1434 sumjan green summer 1
## VeJ1434_2 VeJ1434 sumjan green summer 2
## VeJ1434_3 VeJ1434 sumjan green summer 3
## MVeJ141_1 MVeJ141 sumjan2 brownwhite summer 1
## MVeJ141_2 MVeJ141 sumjan2 brownwhite summer 2
## MVeJ141_3 MVeJ141 sumjan2 brownwhite summer 3
## MVeJ142_1 MVeJ142 sumjan2 brownwhite summer 1
## MVeJ142_2 MVeJ142 sumjan2 brownwhite summer 2
## MVeJ142_3 MVeJ142 sumjan2 brownwhite summer 3
## MVeJ143_1 MVeJ143 sumjan2 brown summer 1
## MVeJ143_2 MVeJ143 sumjan2 brown summer 2
## MVeJ143_3 MVeJ143 sumjan2 brown summer 3
## MVeJ144_1 MVeJ144 sumjan2 brownwhite summer 1
## MVeJ144_2 MVeJ144 sumjan2 brownwhite summer 2
## MVeJ144_3 MVeJ144 sumjan2 brownwhite summer 3
## MVeJ145_1 MVeJ145 sumjan2 brownwhite summer 1
## MVeJ145_2 MVeJ145 sumjan2 brownwhite summer 2
## MVeJ145_3 MVeJ145 sumjan2 brownwhite summer 3
## OutM141_1 OutM141 autmay brown autumn 1
## OutM141_2 OutM141 autmay brown autumn 2
## OutM141_3 OutM141 autmay brown autumn 3
## OutM142_1 OutM142 autmay brownwhite autumn 1
## OutM142_2 OutM142 autmay brownwhite autumn 2
## OutM142_3 OutM142 autmay brownwhite autumn 3
## OutM143_1 OutM143 autmay brown autumn 1
## OutM143_2 OutM143 autmay brown autumn 2
## OutM143_3 OutM143 autmay brown autumn 3
## OutM144_1 OutM144 autmay brown autumn 1
## OutM144_2 OutM144 autmay brown autumn 2
## OutM144_3 OutM144 autmay brown autumn 3
## OutM145_1 OutM145 autmay red autumn 1
## OutM145_2 OutM145 autmay red autumn 2
## OutM145_3 OutM145 autmay red autumn 3
## In14Ap11_1 In14Ap11 wint green winter 1
## In14Ap11_2 In14Ap11 wint green winter 2
## In14Ap11_3 In14Ap11 wint green winter 3
## In14Ap12_1 In14Ap12 wint brown winter 1
## In14Ap12_2 In14Ap12 wint brown winter 2
## In14Ap12_3 In14Ap12 wint brown winter 3
## In14Ap21_1 In14Ap21 wint green winter 1
## In14Ap21_2 In14Ap21 wint green winter 2
## In14Ap21_3 In14Ap21 wint green winter 3
## In14Ap22_1 In14Ap22 wint green winter 1
## In14Ap22_2 In14Ap22 wint green winter 2
## In14Ap22_3 In14Ap22 wint green winter 3
## In14Ap23_1 In14Ap23 wint green winter 1
## In14Ap23_2 In14Ap23 wint green winter 2
## In14Ap23_3 In14Ap23 wint green winter 3
## In14Ap24_1 In14Ap24 wint brown winter 1
## In14Ap24_2 In14Ap24 wint brown winter 2
## In14Ap24_3 In14Ap24 wint brown winter 3
## In14Ap31_1 In14Ap31 wint green winter 1
## In14Ap31_2 In14Ap31 wint green winter 2
## In14Ap31_3 In14Ap31 wint green winter 3
## In14Ap32_1 In14Ap32 wint green winter 1
## In14Ap32_2 In14Ap32 wint green winter 2
## In14Ap32_3 In14Ap32 wint green winter 3
## In14Ap33_1 In14Ap33 wint green winter 1
## In14Ap33_2 In14Ap33 wint green winter 2
## In14Ap33_3 In14Ap33 wint green winter 3
## In14Ap34_1 In14Ap34 wint green winter 1
## In14Ap34_2 In14Ap34 wint green winter 2
## In14Ap34_3 In14Ap34 wint green winter 3
## In14Ap41_1 In14Ap41 wint green winter 1
## In14Ap41_2 In14Ap41 wint green winter 2
## In14Ap41_3 In14Ap41 wint green winter 3
## In14Ap42_1 In14Ap42 wint brown winter 1
## In14Ap42_2 In14Ap42 wint brown winter 2
## In14Ap42_3 In14Ap42 wint brown winter 3
## Pri14Ap11_1 Pri14Ap11 spri green spring 1
## Pri14Ap11_2 Pri14Ap11 spri green spring 2
## Pri14Ap11_3 Pri14Ap11 spri green spring 3
## Pri14Ap12_1 Pri14Ap12 spri brownwhite spring 1
## Pri14Ap12_2 Pri14Ap12 spri brownwhite spring 2
## Pri14Ap12_3 Pri14Ap12 spri brownwhite spring 3
## Pri14Ap21_1 Pri14Ap21 spri green spring 1
## Pri14Ap21_2 Pri14Ap21 spri green spring 2
## Pri14Ap21_3 Pri14Ap21 spri green spring 3
## Pri14Ap22_1 Pri14Ap22 spri brown spring 1
## Pri14Ap22_2 Pri14Ap22 spri brown spring 2
## Pri14Ap22_3 Pri14Ap22 spri brown spring 3
## Pri14Ap23_1 Pri14Ap23 spri brown spring 1
## Pri14Ap23_2 Pri14Ap23 spri brown spring 2
## Pri14Ap23_3 Pri14Ap23 spri brown spring 3
## Pri14Ap24_1 Pri14Ap24 spri green spring 1
## Pri14Ap24_2 Pri14Ap24 spri green spring 2
## Pri14Ap24_3 Pri14Ap24 spri green spring 3
## Pri14Ap31_1 Pri14Ap31 spri green spring 1
## Pri14Ap31_2 Pri14Ap31 spri green spring 2
## Pri14Ap31_3 Pri14Ap31 spri green spring 3
## Pri14Ap32_1 Pri14Ap32 spri brownwhite spring 1
## Pri14Ap32_2 Pri14Ap32 spri brownwhite spring 2
## Pri14Ap32_3 Pri14Ap32 spri brownwhite spring 3
## Pri14Ap41_1 Pri14Ap41 spri brown spring 1
## Pri14Ap41_2 Pri14Ap41 spri brown spring 2
## Pri14Ap41_3 Pri14Ap41 spri brown spring 3
## Pri14Ap42_1 Pri14Ap42 spri brown spring 1
## Pri14Ap42_2 Pri14Ap42 spri brown spring 2
## Pri14Ap42_3 Pri14Ap42 spri brown spring 3
## EpaP111_1 EpaP111 epagri brown spring 1
## EpaP111_2 EpaP111 epagri brown spring 2
## EpaP111_3 EpaP111 epagri brown spring 3
## EpaP112_1 EpaP112 epagri green spring 1
## EpaP112_2 EpaP112 epagri green spring 2
## EpaP112_3 EpaP112 epagri green spring 3
## EpaP113_1 EpaP113 epagri brown spring 1
## EpaP113_2 EpaP113 epagri brown spring 2
## EpaP113_3 EpaP113 epagri brown spring 3
## EpaP114_1 EpaP114 epagri green spring 1
## EpaP114_2 EpaP114 epagri green spring 2
## EpaP114_3 EpaP114 epagri green spring 3
## EpaP115_1 EpaP115 epagri red spring 1
## EpaP115_2 EpaP115 epagri red spring 2
## EpaP115_3 EpaP115 epagri red spring 3
## EpaV111_1 EpaV111 epagri green summer 1
## EpaV111_2 EpaV111 epagri green summer 2
## EpaV111_3 EpaV111 epagri green summer 3
## EpaV112_1 EpaV112 epagri green summer 1
## EpaV112_2 EpaV112 epagri green summer 2
## EpaV112_3 EpaV112 epagri green summer 3
## EpaV113_1 EpaV113 epagri green summer 1
## EpaV113_2 EpaV113 epagri green summer 2
## EpaV113_3 EpaV113 epagri green summer 3
## EpaV114_1 EpaV114 epagri green summer 1
## EpaV114_2 EpaV114 epagri green summer 2
## EpaV114_3 EpaV114 epagri green summer 3
## EpaV115_1 EpaV115 epagri brown summer 1
## EpaV115_2 EpaV115 epagri brown summer 2
## EpaV115_3 EpaV115 epagri brown summer 3Dados de UV-visível (400-500 nm)
Plotando o espectro
plot.spectra(uv.propolis.ds,"seasons")
Pré-processamento de dados
Smoothing e correção da linha de base
uv.propolis.wavelens = get.x.values.as.num(uv.propolis.ds)
x.axis.sm = seq(min(uv.propolis.wavelens), max(uv.propolis.wavelens),10)
uv.propolis.smooth = smoothing.interpolation(uv.propolis.ds, method = "loess", x.axis = x.axis.sm)
plot.spectra(uv.propolis.smooth, "seasons")
uv.propolis.bg = data.correction(uv.propolis.smooth,"background")
uv.propolis.offset = data.correction(uv.propolis.bg, "offset")
uv.propolis.baseline = data.correction(uv.propolis.offset, "baseline")
sum.dataset(uv.propolis.baseline)## Dataset summary:
## Valid dataset
## Description: UV data for propolis-smoothed with hyperSpec spc.loess; background correction; offset correction; baseline correction
## Type of data: undefined
## Number of samples: 195
## Number of data points 53
## Number of metadata variables: 5
## Label of x-axis values: wavelength(nm)
## Label of data points: absorbance
## Number of missing values in data: 0
## Mean of data values: 0.1701
## Median of data values: 0.05404
## Standard deviation: 0.3568
## Range of values: -0.0003441 3.201
## Quantiles:
## 0% 25% 50% 75% 100%
## -0.0003441 0.0125231 0.0540368 0.1703715 3.2009635plot.spectra(uv.propolis.baseline, "seasons")
Análise de Cluster
uv.propolis.hc = clustering(uv.propolis.ds, method = "hc", distance = "euclidean")
dendrogram.plot(uv.propolis.ds, uv.propolis.hc)
dendrogram.plot.col(uv.propolis.ds, uv.propolis.hc, "seasons")
Análise Componentes Principais
uv.propolis.pca = pca.analysis.dataset(uv.propolis.ds)
summary(uv.propolis.pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 18.712 8.400 7.946 5.255 2.3573 1.00080 0.89599
## Proportion of Variance 0.672 0.135 0.121 0.053 0.0107 0.00192 0.00154
## Cumulative Proportion 0.672 0.807 0.929 0.982 0.9923 0.99426 0.99580
## PC8 PC9 PC10 PC11 PC12 PC13
## Standard deviation 0.74847 0.63524 0.4564 0.41450 0.37343 0.30738
## Proportion of Variance 0.00108 0.00077 0.0004 0.00033 0.00027 0.00018
## Cumulative Proportion 0.99687 0.99765 0.9980 0.99838 0.99864 0.99883
## PC14 PC15 PC16 PC17 PC18 PC19
## Standard deviation 0.26591 0.23913 0.20958 0.19265 0.18406 0.17072
## Proportion of Variance 0.00014 0.00011 0.00008 0.00007 0.00007 0.00006
## Cumulative Proportion 0.99896 0.99907 0.99916 0.99923 0.99929 0.99935
## PC20 PC21 PC22 PC23 PC24 PC25
## Standard deviation 0.16001 0.15691 0.14466 0.14103 0.13426 0.12533
## Proportion of Variance 0.00005 0.00005 0.00004 0.00004 0.00003 0.00003
## Cumulative Proportion 0.99940 0.99944 0.99948 0.99952 0.99956 0.99959
## PC26 PC27 PC28 PC29 PC30 PC31
## Standard deviation 0.11924 0.11785 0.11037 0.10466 0.10069 0.09908
## Proportion of Variance 0.00003 0.00003 0.00002 0.00002 0.00002 0.00002
## Cumulative Proportion 0.99961 0.99964 0.99966 0.99969 0.99970 0.99972
## PC32 PC33 PC34 PC35 PC36 PC37
## Standard deviation 0.09703 0.09146 0.08563 0.08297 0.07915 0.07785
## Proportion of Variance 0.00002 0.00002 0.00001 0.00001 0.00001 0.00001
## Cumulative Proportion 0.99974 0.99976 0.99977 0.99979 0.99980 0.99981
## PC38 PC39 PC40 PC41 PC42 PC43
## Standard deviation 0.07460 0.07246 0.07190 0.07074 0.06563 0.06411
## Proportion of Variance 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001
## Cumulative Proportion 0.99982 0.99983 0.99984 0.99985 0.99986 0.99987
## PC44 PC45 PC46 PC47 PC48 PC49
## Standard deviation 0.06355 0.06212 0.06104 0.05770 0.05625 0.05401
## Proportion of Variance 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001
## Cumulative Proportion 0.99987 0.99988 0.99989 0.99989 0.99990 0.99991
## PC50 PC51 PC52 PC53 PC54 PC55 PC56
## Standard deviation 0.05266 0.0508 0.0489 0.0482 0.0474 0.0469 0.0444
## Proportion of Variance 0.00001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## Cumulative Proportion 0.99991 0.9999 0.9999 0.9999 0.9999 0.9999 0.9999
## PC57 PC58 PC59 PC60 PC61 PC62 PC63
## Standard deviation 0.0437 0.0422 0.041 0.0396 0.0389 0.0384 0.0366
## Proportion of Variance 0.0000 0.0000 0.000 0.0000 0.0000 0.0000 0.0000
## Cumulative Proportion 0.9999 0.9999 1.000 1.0000 1.0000 1.0000 1.0000
## PC64 PC65 PC66 PC67 PC68 PC69 PC70
## Standard deviation 0.0349 0.0346 0.0344 0.0337 0.0325 0.0316 0.0308
## Proportion of Variance 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## Cumulative Proportion 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## PC71 PC72 PC73 PC74 PC75 PC76 PC77
## Standard deviation 0.0286 0.0269 0.0264 0.0257 0.0252 0.0246 0.0237
## Proportion of Variance 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## Cumulative Proportion 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## PC78 PC79 PC80 PC81 PC82 PC83 PC84
## Standard deviation 0.0231 0.0213 0.021 0.02 0.0193 0.0186 0.0184
## Proportion of Variance 0.0000 0.0000 0.000 0.00 0.0000 0.0000 0.0000
## Cumulative Proportion 1.0000 1.0000 1.000 1.00 1.0000 1.0000 1.0000
## PC85 PC86 PC87 PC88 PC89 PC90 PC91
## Standard deviation 0.0183 0.0177 0.0176 0.017 0.0167 0.016 0.0155
## Proportion of Variance 0.0000 0.0000 0.0000 0.000 0.0000 0.000 0.0000
## Cumulative Proportion 1.0000 1.0000 1.0000 1.000 1.0000 1.000 1.0000
## PC92 PC93 PC94 PC95 PC96 PC97 PC98
## Standard deviation 0.0151 0.0149 0.0147 0.0135 0.0129 0.0127 0.0126
## Proportion of Variance 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## Cumulative Proportion 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
## PC99 PC100 PC101 PC102 PC103 PC104 PC105
## Standard deviation 0.0121 0.0119 0.0118 0.0113 0.011 0.0108 0.0105
## Proportion of Variance 0.0000 0.0000 0.0000 0.0000 0.000 0.0000 0.0000
## Cumulative Proportion 1.0000 1.0000 1.0000 1.0000 1.000 1.0000 1.0000
## PC106 PC107 PC108 PC109 PC110 PC111
## Standard deviation 0.00988 0.0098 0.0094 0.00917 0.00897 0.00883
## Proportion of Variance 0.00000 0.0000 0.0000 0.00000 0.00000 0.00000
## Cumulative Proportion 1.00000 1.0000 1.0000 1.00000 1.00000 1.00000
## PC112 PC113 PC114 PC115 PC116 PC117
## Standard deviation 0.00862 0.00823 0.00812 0.0079 0.00777 0.00756
## Proportion of Variance 0.00000 0.00000 0.00000 0.0000 0.00000 0.00000
## Cumulative Proportion 1.00000 1.00000 1.00000 1.0000 1.00000 1.00000
## PC118 PC119 PC120 PC121 PC122 PC123
## Standard deviation 0.00734 0.00699 0.00683 0.00665 0.00629 0.00625
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## PC124 PC125 PC126 PC127 PC128 PC129
## Standard deviation 0.00619 0.00604 0.00598 0.00565 0.00549 0.00542
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## PC130 PC131 PC132 PC133 PC134 PC135
## Standard deviation 0.0053 0.00517 0.00498 0.00492 0.00474 0.00454
## Proportion of Variance 0.0000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion 1.0000 1.00000 1.00000 1.00000 1.00000 1.00000
## PC136 PC137 PC138 PC139 PC140 PC141
## Standard deviation 0.00449 0.00439 0.00422 0.00418 0.00407 0.00374
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## PC142 PC143 PC144 PC145 PC146 PC147
## Standard deviation 0.00365 0.00359 0.00345 0.00338 0.00327 0.00313
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## PC148 PC149 PC150 PC151 PC152 PC153
## Standard deviation 0.00303 0.00294 0.00286 0.00285 0.00279 0.00263
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## PC154 PC155 PC156 PC157 PC158 PC159
## Standard deviation 0.00251 0.00246 0.00241 0.00225 0.00217 0.00212
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## PC160 PC161 PC162 PC163 PC164 PC165
## Standard deviation 0.00205 0.00192 0.00189 0.00179 0.00171 0.00164
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## PC166 PC167 PC168 PC169 PC170 PC171
## Standard deviation 0.00151 0.00143 0.0014 0.00131 0.00124 0.00115
## Proportion of Variance 0.00000 0.00000 0.0000 0.00000 0.00000 0.00000
## Cumulative Proportion 1.00000 1.00000 1.0000 1.00000 1.00000 1.00000
## PC172 PC173 PC174 PC175 PC176 PC177
## Standard deviation 0.00111 0.00102 0.001 0.000905 0.000899 0.000864
## Proportion of Variance 0.00000 0.00000 0.000 0.000000 0.000000 0.000000
## Cumulative Proportion 1.00000 1.00000 1.000 1.000000 1.000000 1.000000
## PC178 PC179 PC180 PC181 PC182
## Standard deviation 0.000797 0.000756 0.000658 0.000604 0.00056
## Proportion of Variance 0.000000 0.000000 0.000000 0.000000 0.00000
## Cumulative Proportion 1.000000 1.000000 1.000000 1.000000 1.00000
## PC183 PC184 PC185 PC186 PC187
## Standard deviation 0.000529 0.000479 0.000432 0.000423 0.000378
## Proportion of Variance 0.000000 0.000000 0.000000 0.000000 0.000000
## Cumulative Proportion 1.000000 1.000000 1.000000 1.000000 1.000000
## PC188 PC189 PC190 PC191 PC192 PC193
## Standard deviation 0.000361 0.000225 7e-05 3.63e-05 8.31e-15 1.53e-15
## Proportion of Variance 0.000000 0.000000 0e+00 0.00e+00 0.00e+00 0.00e+00
## Cumulative Proportion 1.000000 1.000000 1e+00 1.00e+00 1.00e+00 1.00e+00
## PC194 PC195
## Standard deviation 1.53e-15 1.53e-15
## Proportion of Variance 0.00e+00 0.00e+00
## Cumulative Proportion 1.00e+00 1.00e+00pca.scoresplot3D(uv.propolis.ds, uv.propolis.pca, "seasons")## Loading required package: scatterplot3d
pca.scoresplot2D(uv.propolis.ds, uv.propolis.pca, "seasons", ellipses=T, pallette=2)
** String - Cluster e PCA**
uv.propolis.string = subset.x.values.by.interval(uv.propolis.ds, min.value = 280, max.value = 350)
sum.dataset(uv.propolis.string)## Dataset summary:
## Valid dataset
## Description: UV data for propolis
## Type of data: uvv-spectra
## Number of samples: 195
## Number of data points 71
## Number of metadata variables: 5
## Label of x-axis values: wavelength(nm)
## Label of data points: absorbance
## Number of missing values in data: 0
## Mean of data values: 1.106
## Median of data values: 0.665
## Standard deviation: 1.108
## Range of values: 0 4.499
## Quantiles:
## 0% 25% 50% 75% 100%
## 0.000 0.056 0.665 2.033 4.499plot.spectra(uv.propolis.string, "seasons", legend="topleft")
uv.propolis.string.pca = pca.analysis.dataset(uv.propolis.string, scale = T, center = T)
summary(uv.propolis.string.pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 7.730 3.203 0.59791 0.44638 0.36799 0.19167 0.17365
## Proportion of Variance 0.842 0.145 0.00504 0.00281 0.00191 0.00052 0.00042
## Cumulative Proportion 0.842 0.986 0.99124 0.99404 0.99595 0.99647 0.99689
## PC8 PC9 PC10 PC11 PC12 PC13
## Standard deviation 0.15274 0.14367 0.13105 0.12880 0.11482 0.11068
## Proportion of Variance 0.00033 0.00029 0.00024 0.00023 0.00019 0.00017
## Cumulative Proportion 0.99722 0.99751 0.99775 0.99799 0.99817 0.99835
## PC14 PC15 PC16 PC17 PC18 PC19
## Standard deviation 0.10471 0.09841 0.09156 0.08969 0.08706 0.08061
## Proportion of Variance 0.00015 0.00014 0.00012 0.00011 0.00011 0.00009
## Cumulative Proportion 0.99850 0.99864 0.99875 0.99887 0.99897 0.99907
## PC20 PC21 PC22 PC23 PC24 PC25
## Standard deviation 0.07872 0.07708 0.07112 0.06825 0.06699 0.06263
## Proportion of Variance 0.00009 0.00008 0.00007 0.00007 0.00006 0.00006
## Cumulative Proportion 0.99915 0.99924 0.99931 0.99937 0.99944 0.99949
## PC26 PC27 PC28 PC29 PC30 PC31
## Standard deviation 0.05985 0.05681 0.05558 0.05210 0.04974 0.04778
## Proportion of Variance 0.00005 0.00005 0.00004 0.00004 0.00003 0.00003
## Cumulative Proportion 0.99954 0.99959 0.99963 0.99967 0.99970 0.99974
## PC32 PC33 PC34 PC35 PC36 PC37
## Standard deviation 0.04636 0.04551 0.04270 0.03871 0.03720 0.03592
## Proportion of Variance 0.00003 0.00003 0.00003 0.00002 0.00002 0.00002
## Cumulative Proportion 0.99977 0.99980 0.99982 0.99984 0.99986 0.99988
## PC38 PC39 PC40 PC41 PC42 PC43
## Standard deviation 0.03221 0.03070 0.03039 0.02835 0.02554 0.02328
## Proportion of Variance 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001
## Cumulative Proportion 0.99990 0.99991 0.99992 0.99993 0.99994 0.99995
## PC44 PC45 PC46 PC47 PC48 PC49 PC50
## Standard deviation 0.02163 0.02151 0.01990 0.018 0.017 0.0155 0.0146
## Proportion of Variance 0.00001 0.00001 0.00001 0.000 0.000 0.0000 0.0000
## Cumulative Proportion 0.99996 0.99996 0.99997 1.000 1.000 1.0000 1.0000
## PC51 PC52 PC53 PC54 PC55 PC56 PC57
## Standard deviation 0.0139 0.0131 0.0125 0.0106 0.00989 0.00912 0.0083
## Proportion of Variance 0.0000 0.0000 0.0000 0.0000 0.00000 0.00000 0.0000
## Cumulative Proportion 1.0000 1.0000 1.0000 1.0000 0.99999 0.99999 1.0000
## PC58 PC59 PC60 PC61 PC62 PC63
## Standard deviation 0.00761 0.00707 0.00645 0.00609 0.00501 0.00456
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
## PC64 PC65 PC66 PC67 PC68 PC69
## Standard deviation 0.0039 0.00336 0.00294 0.00254 0.00224 0.002
## Proportion of Variance 0.0000 0.00000 0.00000 0.00000 0.00000 0.000
## Cumulative Proportion 1.0000 1.00000 1.00000 1.00000 1.00000 1.000
## PC70 PC71
## Standard deviation 0.00183 0.00139
## Proportion of Variance 0.00000 0.00000
## Cumulative Proportion 1.00000 1.00000pca.scoresplot2D(uv.propolis.string, uv.propolis.string.pca, pcas = c(1,2), "seasons", labels="F", pallette=2, ellipses="T")
uv.propolis.string.hc = clustering(uv.propolis.string, method = "hc", distance = "euclidean")
uv.propolis.string.hc##
## Call:
## hclust(d = dist.matrix, method = clustMethod)
##
## Cluster method : complete
## Distance : euclidean
## Number of objects: 195dendrogram.plot(uv.propolis.string, uv.propolis.string.hc)
dendrogram.plot.col(uv.propolis.string, uv.propolis.string.hc, "seasons")