EiraData
Pedro González
03/03/2022
df<-read.csv("Eira2.csv")Análisis de ANOVA
df$Productor<-factor(df$Productor)
df$Conc<-as.numeric(df$Conc)
df$Lugar<-factor(df$Lugar)
modelo<-aov(Conc~Lugar,data=df)
summary(modelo)## Df Sum Sq Mean Sq F value Pr(>F)
## Lugar 1 32080 32080 0.872 0.356
## Residuals 41 1508710 36798El valor de p=0.356 indica que no hay diferencias significativas en la concentración de ciproconazol entre los dos sitios de muestreo
library("tidyverse")## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.5 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.0.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()df2<-df %>% filter(Lugar=="BOQ")
df3<-df %>% filter(Lugar=="REN")
BoqData<-cut(df2$Conc,breaks=c(0,10,20,30,40,50,Inf),labels=c('0-10','10-20','20-30','30-40','40-50','1000-1300'),include.lowest =TRUE)
RenData<-cut(df3$Conc,breaks=c(0,10,20,30,40,50,Inf),labels=c('0-10','10-20','20-30','30-40','40-50','1000-1300'),include.lowest =TRUE)Gráficas de la distribución de niveles de ciproconazol en granos de café por lugar de muestreo
library(ggplot2)
ggplot(data = as_tibble(RenData), mapping = aes(x=value)) +
geom_bar(fill="bisque",color="gray",alpha=1.0) +
stat_count(geom="text", aes(label=sprintf("%d",..count..)), vjust=-0.5) +
labs(x='[Ciproconazol] ug/Kg',y='Número de Muestras') +
theme_minimal() +ylim(0, 25)+geom_text(x=3, y=25,size=6, label="Renacimiento")
ggplot(data = as_tibble(BoqData), mapping = aes(x=value)) +
geom_bar(fill="bisque",color="gray",alpha=1.0) +
stat_count(geom="text", aes(label=sprintf("%d",..count..)), vjust=-0.5) +
labs(x='[Ciproconazol] ug/Kg',y='Número de Muestras') +
theme_minimal() +ylim(0, 25)+geom_text(x=2, y=25,size=6, label="Boquete")
muestras<-read.csv("Muestras.csv")
patrones<-read.csv("Patrones.csv")Tabla de Datos de las curvas de calibración
knitr::kable(patrones, align = "lcccccccccc")| Conc | Acetamiprid | Acetoclor | Aflatoxina.B1 | AflatoxinaB2 | AfratoxinaG1 | Chloropiryfos | Clotiadinina | Cyproconazole | Epoxyconazole | Tiametoxan |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.50 | 870.9915 | 273.6039 | 384.8337 | 252.6643 | 0.000 | 147.8100 | 809.6255 | 7889.055 | 4436.050 | 1006.343 |
| 1.25 | 5243.8585 | 937.5786 | 1147.3751 | 675.9481 | 1234.315 | 117.3181 | 3485.7145 | 13812.877 | 8861.828 | 5489.108 |
| 2.50 | 5922.0192 | 2027.5003 | 1431.5289 | 1018.3362 | 1007.463 | 230.0323 | 4355.3209 | 20117.084 | 14589.111 | 8293.662 |
| 6.25 | 12422.5596 | 3683.9771 | 5982.4089 | 3764.5481 | 3923.536 | 446.3122 | 7275.4259 | 41361.157 | 30216.895 | 15411.588 |
| 10.00 | 18830.4849 | 6537.8212 | 11629.9570 | 7164.2837 | 8116.239 | 521.2114 | 10983.4070 | 56888.241 | 46417.955 | 23054.710 |
Curva de Calibración del ciproconazol
y<-patrones$Cyproconazole
x<-patrones$Conc
datos<-muestras$Cyproconazole
modelo<-lm(y~x)
s<-summary(modelo)
LOD<-s$coefficients[1,2]/s$coefficients[1]*3.3*8
b0=s$coefficients[1]
b1=s$coefficients[2]
yexp=modelo$fitted.values
Sxy=sqrt((sum(y*y)-b0*sum(y)-b1*sum(x*y))/(length(x)-2))
print(s)##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## 1 2 3 4 5
## -1597.4 466.6 337.7 2282.6 -1489.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6913.2 1266.3 5.459 0.012077 *
## x 5146.5 233.4 22.045 0.000204 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1854 on 3 degrees of freedom
## Multiple R-squared: 0.9939, Adjusted R-squared: 0.9918
## F-statistic: 486 on 1 and 3 DF, p-value: 0.0002043n=length(x)
SE=sqrt(sum((y-yexp)**2)/(n-2))
print("LOD:")## [1] "LOD:"print(SE/modelo$coefficients[2]*3.3*8)## x
## 9.51074t<-qt(0.975,3)
IC1<-yexp+t*Sxy*sqrt(1/n+(x-mean(x))**2/sum((x-mean(x))**2))
IC2<-yexp-t*Sxy*sqrt(1/n+(x-mean(x))**2/sum((x-mean(x))**2))
plot(x,y,main="Curva de calibración del ciproconazol",xlab="[Ciproconazol](ug/Kg)",ylab="Señal")
lines(x,yexp,lty=1)
lines(x,IC1,lty=2)
lines(x,IC2,lty=2)
Curva de Calibración del epoxiconazol
y<-patrones$Epoxyconazole
x<-patrones$Conc
modelo<-lm(y~x)
s<-summary(modelo)
LOD<-s$coefficients[1,2]/s$coefficients[1]*3.3*8
b0=s$coefficients[1]
b1=s$coefficients[2]
yexp=modelo$fitted.values
Sxy=sqrt((sum(y*y)-b0*sum(y)-b1*sum(x*y))/(length(x)-2))
print(s)##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## 1 2 3 4 5
## -814.74 349.87 641.89 -36.14 -140.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3076.68 435.45 7.065 0.00583 **
## x 4348.22 80.28 54.166 1.39e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 637.5 on 3 degrees of freedom
## Multiple R-squared: 0.999, Adjusted R-squared: 0.9986
## F-statistic: 2934 on 1 and 3 DF, p-value: 1.386e-05n=length(x)
SE=sqrt(sum((y-yexp)**2)/(n-2))
print("LOD:")## [1] "LOD:"print(SE/modelo$coefficients[2]*3.3*8)## x
## 3.870826t<-qt(0.975,3)
IC1<-yexp+t*Sxy*sqrt(1/n+(x-mean(x))**2/sum((x-mean(x))**2))
IC2<-yexp-t*Sxy*sqrt(1/n+(x-mean(x))**2/sum((x-mean(x))**2))
plot(x,y,main="Curva de calibración del epoxiconazol",xlab="[Epoxiconazol](ug/Kg)",ylab="Señal")
lines(x,yexp,lty=1)
lines(x,IC1,lty=2)
lines(x,IC2,lty=2)
Curva de Calibración del clotiadinina
y<-patrones$Clotiadinina
x<-patrones$Conc
modelo<-lm(y~x)
s<-summary(modelo)
LOD<-s$coefficients[1,2]/s$coefficients[1]*3.3*8
b0=s$coefficients[1]
b1=s$coefficients[2]
yexp=modelo$fitted.values
Sxy=sqrt((sum(y*y)-b0*sum(y)-b1*sum(x*y))/(length(x)-2))
print(s)##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## 1 2 3 4 5
## -1112.00 843.20 511.32 -173.02 -69.49
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1441.0 590.7 2.440 0.09253 .
## x 961.2 108.9 8.827 0.00306 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 864.8 on 3 degrees of freedom
## Multiple R-squared: 0.9629, Adjusted R-squared: 0.9506
## F-statistic: 77.91 on 1 and 3 DF, p-value: 0.003064n=length(x)
SE=sqrt(sum((y-yexp)**2)/(n-2))
print("LOD:")## [1] "LOD:"print(SE/modelo$coefficients[2]*3.3*8)## x
## 23.75322t<-qt(0.975,3)
IC1<-yexp+t*Sxy*sqrt(1/n+(x-mean(x))**2/sum((x-mean(x))**2))
IC2<-yexp-t*Sxy*sqrt(1/n+(x-mean(x))**2/sum((x-mean(x))**2))
plot(x,y,main="Curva de calibración del clotadinina",xlab="[Clotadinina](ug/Kg)",ylab="Señal")
lines(x,yexp,lty=1)
lines(x,IC1,lty=2)
lines(x,IC2,lty=2)
Curva de Calibración del tiametoxan
y<-patrones$Tiametoxan
x<-patrones$Conc
modelo<-lm(y~x)
s<-summary(modelo)
LOD<-s$coefficients[1,2]/s$coefficients[1]*3.3*8
b0=s$coefficients[1]
b1=s$coefficients[2]
yexp=modelo$fitted.values
Sxy=sqrt((sum(y*y)-b0*sum(y)-b1*sum(x*y))/(length(x)-2))
print(s)##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## 1 2 3 4 5
## -1847.1 1011.2 1108.2 103.6 -375.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1770.4 950.9 1.862 0.15952
## x 2166.0 175.3 12.357 0.00114 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1392 on 3 degrees of freedom
## Multiple R-squared: 0.9807, Adjusted R-squared: 0.9743
## F-statistic: 152.7 on 1 and 3 DF, p-value: 0.001142n=length(x)
SE=sqrt(sum((y-yexp)**2)/(n-2))
print("LOD:")## [1] "LOD:"print(SE/modelo$coefficients[2]*3.3*8)## x
## 16.96804t<-qt(0.975,3)
IC1<-yexp+t*Sxy*sqrt(1/n+(x-mean(x))**2/sum((x-mean(x))**2))
IC2<-yexp-t*Sxy*sqrt(1/n+(x-mean(x))**2/sum((x-mean(x))**2))
plot(x,y,main="Curva de calibración del tiametoxan",xlab="[Tiametoxan](ug/Kg)",ylab="Señal")
lines(x,yexp,lty=1)
lines(x,IC1,lty=2)
lines(x,IC2,lty=2)