vignettes/calibration.md
In zsteinmetz/envalysis: Miscellaneous Functions for Environmental Analyses
title: "Calibration workflow"
author: "Zacharias Steinmetz"
date: "2023-09-20"
output:
html_document:
keep_md: yes
fig_width: 8
vignette: >
%\VignetteIndexEntry{Calibration workflow}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
Packages
For the complete workflow, data.table and ggplot2 are required besides
envalysis.
library(envalysis)
library(data.table)
library(ggplot2)
Sample data
The sample data used stems from Steinmetz et al. (2019). It consists of two
tables: a sequence table and a sample table.
The sequence table contains gas-chromatography/mass spectrometry measurement
data of two phenolic compounds, these are tyrosol and vanillin. Besides the
samples, standard mixtures and extraction blanks (type) were acquired in three
separate analysis batches. Each measurement resulted in an integrated peak area.
Compound Type Batch Name Area Spec Conc
Tyrosol Extraction blank 1 Blank 1 0.000000 NA
Tyrosol Extraction blank 1 Blank 2 0.000000 NA
Tyrosol Sample 1 ZS-001 328.343597 NA
Tyrosol Sample 1 ZS-002 282.930939 NA
Tyrosol Standard 3 0 mg/L 0.000000 0.00000
Tyrosol Standard 3 1 mg/L 7.628456 0.97755
Tyrosol Standard 3 5 mg/L 35.566628 4.88775
Tyrosol Standard 3 20 mg/L 141.898056 19.55100
Tyrosol Standard 3 100 mg/L 715.496338 97.75500
Vanillin Sample 1 ZS-001 1876.933716 NA
Vanillin Sample 1 ZS-002 1578.626099 NA
The sample table describes the samples' origin from a 29-day degradation
experiment, in which the phenolic compounds were either degraded in the dark by
the native soil microbial community or photooxidized under UV irradiation after
sterilizing the soil. The samples were processed in threefold replication. Their
weight [g], the volume [mL] of extract solution, and the dilution factor were
recorded.
Name Day Lighting Sterilization Treatment Replicate Weight Extract Dilution
ZS-001 0 UV sterilized Photooxidation 1 2.5037 12.5 5
ZS-002 0 UV sterilized Photooxidation 2 2.5018 12.5 5
ZS-019 0 dark non-sterilized Biodegradation 1 2.5001 12.5 5
ZS-164 29 dark non-sterilized Biodegradation 2 2.4992 12.5 1
ZS-165 29 dark non-sterilized Biodegradation 3 2.5000 12.5 1
In envalysis, the sample data is stored in a two-item list called
phenolics. The list items are named seq and samples.
data("phenolics")
str(phenolics)
# > List of 2
# > $ seq :'data.frame': 160 obs. of 6 variables:
# > ..$ Compound : Factor w/ 2 levels "Tyrosol","Vanillin": 1 1 1 1 1 1 1 1 1 1 ...
# > ..$ Type : Factor w/ 3 levels "Extraction blank",..: 1 1 2 2 2 2 2 2 2 2 ...
# > ..$ Batch : int [1:160] 1 1 1 1 1 1 1 1 1 1 ...
# > ..$ Name : chr [1:160] "Blank 1" "Blank 2" "ZS-001" "ZS-002" ...
# > ..$ Area : num [1:160] 0 0 328 283 296 ...
# > ..$ Spec Conc: num [1:160] NA NA NA NA NA NA NA NA NA NA ...
# > $ samples:'data.frame': 42 obs. of 9 variables:
# > ..$ Name : chr [1:42] "ZS-001" "ZS-002" "ZS-003" "ZS-019" ...
# > ..$ Day : int [1:42] 0 0 0 0 0 0 1 1 1 1 ...
# > ..$ Lighting : Factor w/ 2 levels "dark","UV": 2 2 2 1 1 1 2 2 2 1 ...
# > ..$ Sterilization: Factor w/ 2 levels "non-sterilized",..: 2 2 2 1 1 1 2 2 2 1 ...
# > ..$ Treatment : Factor w/ 2 levels "Biodegradation",..: 2 2 2 1 1 1 2 2 2 1 ...
# > ..$ Replicate : int [1:42] 1 2 3 1 2 3 1 2 3 1 ...
# > ..$ Weight : num [1:42] 2.5 2.5 2.5 2.5 2.5 ...
# > ..$ Extract : num [1:42] 12.5 12.5 12.5 12.5 12.5 12.5 12.5 12.5 12.5 12.5 ...
# > ..$ Dilution : int [1:42] 5 5 5 5 5 5 2 2 2 2 ...
Simple calibation
Since the two phenolic compounds were analyzed in three different batches, six
individual calibration curves are required for quantification. For better
understanding, the calibration workflow is first shown for a subset of data,
namely the first batch of tyrosol measurements. The subset is stored in
tyrosol_1.
All standards in the tyrosol_1 subset are used for calibration. The
'calibration' object is stored as cal_1, which can be printed for additional
information including limits of detection and quantification, the adjusted
R^2^, blanks, and statistical checks of the underlying calibration model.
tyrosol_1 <- subset(phenolics$seq, Compound == "Tyrosol" & Batch == 1)
cal_1 <- calibration(Area ~ `Spec Conc`,
data = subset(tyrosol_1, Type == "Standard"))
print(cal_1)
# >
# > Call:
# > calibration(formula = Area ~ `Spec Conc`, data = subset(tyrosol_1,
# > Type == "Standard"))
# >
# > Coefficients:
# > (Intercept) `Spec Conc`
# > 3.762 7.508
# >
# > Adjusted R-squared: 0.9998
# > Sum relative error: 1.558
# >
# > Blanks:
# > [1] 0.07470126 0.06534690 0.06093782 0.00000000
# >
# > `Spec Conc` lwr upr
# > LOD 0.0144 0.00952 0.0293
# > LOQ 5.9300 3.92000 12.1000
# >
# > Check for normality of residuals:
# >
# > Shapiro-Wilk normality test
# >
# > data: residuals(calibration(formula = Area ~ `Spec Conc`, data = subset(tyrosol_1, )residuals( Type == "Standard")))
# > W = 0.87897, p-value = 0.1841
# >
# > Check for homoscedasticity of residuals:
# >
# > studentized Breusch-Pagan test
# >
# > data: Area ~ `Spec Conc`
# > BP = 0.40323, df = 1, p-value = 0.5254
plot(cal_1)
Based on cal_1, the tyrosol concentrations can be calculated for all samples
using inv_predict(). The argument below_lod = 0 specifies that
concentrations below limit of detection (LOD) should be set to zero.
tyrosol_1$`Calc Conc` <- inv_predict(cal_1, tyrosol_1$Area, below_lod = 0)
head(tyrosol_1)
# > Compound Type Batch Name Area Spec Conc Calc Conc
# > 1 Tyrosol Extraction blank 1 Blank 1 0.0000 NA 0.00000
# > 2 Tyrosol Extraction blank 1 Blank 2 0.0000 NA 0.00000
# > 3 Tyrosol Sample 1 ZS-001 328.3436 NA 43.23037
# > 4 Tyrosol Sample 1 ZS-002 282.9309 NA 37.18195
# > 5 Tyrosol Sample 1 ZS-003 296.2863 NA 38.96072
# > 6 Tyrosol Sample 1 ZS-019 243.0258 NA 31.86707
Working with data.tables
To process all compounds and analysis batches together, the phenolics data is
converted to data.tables.
dt <- lapply(phenolics, as.data.table)
To replicate the following steps, try to organize your data in the same way as
shown before. If you want to read in your data directly as data.table, use
their fread() function, for instance.
Batch calibration
Subsequently, calibration() and inv_predict() are applied by compound and
batch.
dt$seq[, `Calc Conc` := calibration(Area ~ `Spec Conc`, .SD[Type == "Standard"]) |>
inv_predict(Area, below_lod = 0),
by = .(Compound, Batch)]
head(dt$seq)
# > Compound Type Batch Name Area Spec Conc Calc Conc
# > 1: Tyrosol Extraction blank 1 Blank 1 0.0000 NA 0.00000
# > 2: Tyrosol Extraction blank 1 Blank 2 0.0000 NA 0.00000
# > 3: Tyrosol Sample 1 ZS-001 328.3436 NA 43.23037
# > 4: Tyrosol Sample 1 ZS-002 282.9309 NA 37.18195
# > 5: Tyrosol Sample 1 ZS-003 296.2863 NA 38.96072
# > 6: Tyrosol Sample 1 ZS-019 243.0258 NA 31.86707
Calibration parameters like LODs, LOQs, or adjusted R^2^ may be stored in a
separate list item for later use.
dt$cal <- dt$seq[Type == "Standard", calibration(Area ~ `Spec Conc`) |>
as.list(c("coef", "adj.r.squared", "lod", "loq")),
by = .(Compound, Batch)]
print(dt$cal)
# > Compound Batch (Intercept) `Spec Conc` adj.r.squared lod loq
# > 1: Tyrosol 1 3.76202803 7.508184 0.9997915 0.0143933 5.925273
# > 2: Tyrosol 2 3.07268449 7.601129 0.9992824 0.0092660 10.909912
# > 3: Tyrosol 3 0.05587782 7.309053 0.9999764 0.0000000 2.311322
# > 4: Vanillin 1 25.17794786 51.529670 0.9998175 0.0040133 5.623778
# > 5: Vanillin 2 24.44314369 51.774820 0.9992359 0.0003509 11.408191
# > 6: Vanillin 3 10.62899985 50.819997 0.9999641 0.0000000 2.886073
Blank subtraction
With the calculated concentrations at hand, the sample concentrations are
subtracted by the extraction blanks to correct for potential lab-borne
contamination.
dt$seq[, `Clean Conc` := `Calc Conc` - mean(
`Calc Conc`[Type == "Extraction blank"], na.rm = T),
by = .(Batch, Compound)]
Merging tables
The sequence table is merged with the sample table and the contents of
phenolic compounds are calculated from the extraction volume, sample weight, and
dilution factor.
dt$res <- merge(dt$seq, dt$samples, by = "Name")
dt$res[, Content := `Clean Conc` * (Extract / Weight) * Dilution]
head(dt$res)
# > Name Compound Type Batch Area Spec Conc Calc Conc Clean Conc Day
# > 1: ZS-001 Tyrosol Sample 1 328.3436 NA 43.23037 43.23037 0
# > 2: ZS-001 Vanillin Sample 1 1876.9337 NA 35.93572 35.93572 0
# > 3: ZS-002 Tyrosol Sample 1 282.9309 NA 37.18195 37.18195 0
# > 4: ZS-002 Vanillin Sample 1 1578.6261 NA 30.14667 30.14667 0
# > 5: ZS-003 Tyrosol Sample 1 296.2863 NA 38.96072 38.96072 0
# > 6: ZS-003 Vanillin Sample 1 1593.6272 NA 30.43779 30.43779 0
# > Lighting Sterilization Treatment Replicate Weight Extract Dilution
# > 1: UV sterilized Photooxidation 1 2.5037 12.5 5
# > 2: UV sterilized Photooxidation 1 2.5037 12.5 5
# > 3: UV sterilized Photooxidation 2 2.5018 12.5 5
# > 4: UV sterilized Photooxidation 2 2.5018 12.5 5
# > 5: UV sterilized Photooxidation 3 2.5048 12.5 5
# > 6: UV sterilized Photooxidation 3 2.5048 12.5 5
# > Content
# > 1: 1079.1621
# > 2: 897.0653
# > 3: 928.8800
# > 4: 753.1246
# > 5: 972.1515
# > 6: 759.4865
Plotting
For plotting the data using ggplot2, the contents are summarized by mean and
confidence interval (CI).
dt$sum <- dt$res[, .(Content = mean(Content, na.rm = T),
CI = CI(Content, na.rm = T)),
by = .(Compound, Treatment, Day)]
ggplot(dt$sum, aes(x = Day, y = Content)) +
geom_errorbar(aes(ymin = Content - CI, ymax = Content + CI, group = Treatment),
width = 1, position = position_dodge(1)) +
geom_point(aes(shape = Treatment, fill = Treatment),
position = position_dodge(1)) +
xlab("Day of incubation") +
ylab(expression("Phenolic content"~"["*mg~kg^-1*"]")) +
facet_wrap(~ Compound, ncol = 2, scales = "free") +
scale_shape_manual(values = c(21,24)) +
scale_fill_manual(values = c("black", "white")) +
theme_publish()
References
Steinmetz, Z., Kurtz, M.P., Zubrod, J.P., Meyer, A.H., Elsner, M., & Schaumann,
G.E. (2019) Biodegradation and photooxidation of phenolic compounds in soil—A
compound-specific stable isotope approach.
Chemosphere 230, 210-218. DOI: 10.1016/j.chemosphere.2019.05.030.
zsteinmetz/envalysis documentation built on Oct. 20, 2024, 4:57 a.m.
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title: "Calibration workflow" author: "Zacharias Steinmetz" date: "2023-09-20" output: html_document: keep_md: yes fig_width: 8 vignette: > %\VignetteIndexEntry{Calibration workflow} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}
Packages
For the complete workflow, data.table and ggplot2 are required besides envalysis.
library(envalysis)
library(data.table)
library(ggplot2)
Sample data
The sample data used stems from Steinmetz et al. (2019). It consists of two tables: a sequence table and a sample table.
The sequence table contains gas-chromatography/mass spectrometry measurement data of two phenolic compounds, these are tyrosol and vanillin. Besides the samples, standard mixtures and extraction blanks (type) were acquired in three separate analysis batches. Each measurement resulted in an integrated peak area.
Compound Type Batch Name Area Spec Conc
Tyrosol Extraction blank 1 Blank 1 0.000000 NA Tyrosol Extraction blank 1 Blank 2 0.000000 NA Tyrosol Sample 1 ZS-001 328.343597 NA Tyrosol Sample 1 ZS-002 282.930939 NA Tyrosol Standard 3 0 mg/L 0.000000 0.00000 Tyrosol Standard 3 1 mg/L 7.628456 0.97755 Tyrosol Standard 3 5 mg/L 35.566628 4.88775 Tyrosol Standard 3 20 mg/L 141.898056 19.55100 Tyrosol Standard 3 100 mg/L 715.496338 97.75500 Vanillin Sample 1 ZS-001 1876.933716 NA Vanillin Sample 1 ZS-002 1578.626099 NA
The sample table describes the samples' origin from a 29-day degradation experiment, in which the phenolic compounds were either degraded in the dark by the native soil microbial community or photooxidized under UV irradiation after sterilizing the soil. The samples were processed in threefold replication. Their weight [g], the volume [mL] of extract solution, and the dilution factor were recorded.
Name Day Lighting Sterilization Treatment Replicate Weight Extract Dilution
ZS-001 0 UV sterilized Photooxidation 1 2.5037 12.5 5 ZS-002 0 UV sterilized Photooxidation 2 2.5018 12.5 5 ZS-019 0 dark non-sterilized Biodegradation 1 2.5001 12.5 5 ZS-164 29 dark non-sterilized Biodegradation 2 2.4992 12.5 1 ZS-165 29 dark non-sterilized Biodegradation 3 2.5000 12.5 1
In envalysis, the sample data is stored in a two-item list called
phenolics. The list items are named seq and samples.
data("phenolics")
str(phenolics)
# > List of 2
# > $ seq :'data.frame': 160 obs. of 6 variables:
# > ..$ Compound : Factor w/ 2 levels "Tyrosol","Vanillin": 1 1 1 1 1 1 1 1 1 1 ...
# > ..$ Type : Factor w/ 3 levels "Extraction blank",..: 1 1 2 2 2 2 2 2 2 2 ...
# > ..$ Batch : int [1:160] 1 1 1 1 1 1 1 1 1 1 ...
# > ..$ Name : chr [1:160] "Blank 1" "Blank 2" "ZS-001" "ZS-002" ...
# > ..$ Area : num [1:160] 0 0 328 283 296 ...
# > ..$ Spec Conc: num [1:160] NA NA NA NA NA NA NA NA NA NA ...
# > $ samples:'data.frame': 42 obs. of 9 variables:
# > ..$ Name : chr [1:42] "ZS-001" "ZS-002" "ZS-003" "ZS-019" ...
# > ..$ Day : int [1:42] 0 0 0 0 0 0 1 1 1 1 ...
# > ..$ Lighting : Factor w/ 2 levels "dark","UV": 2 2 2 1 1 1 2 2 2 1 ...
# > ..$ Sterilization: Factor w/ 2 levels "non-sterilized",..: 2 2 2 1 1 1 2 2 2 1 ...
# > ..$ Treatment : Factor w/ 2 levels "Biodegradation",..: 2 2 2 1 1 1 2 2 2 1 ...
# > ..$ Replicate : int [1:42] 1 2 3 1 2 3 1 2 3 1 ...
# > ..$ Weight : num [1:42] 2.5 2.5 2.5 2.5 2.5 ...
# > ..$ Extract : num [1:42] 12.5 12.5 12.5 12.5 12.5 12.5 12.5 12.5 12.5 12.5 ...
# > ..$ Dilution : int [1:42] 5 5 5 5 5 5 2 2 2 2 ...
Simple calibation
Since the two phenolic compounds were analyzed in three different batches, six
individual calibration curves are required for quantification. For better
understanding, the calibration workflow is first shown for a subset of data,
namely the first batch of tyrosol measurements. The subset is stored in
tyrosol_1.
All standards in the tyrosol_1 subset are used for calibration. The
'calibration' object is stored as cal_1, which can be printed for additional
information including limits of detection and quantification, the adjusted
R^2^, blanks, and statistical checks of the underlying calibration model.
tyrosol_1 <- subset(phenolics$seq, Compound == "Tyrosol" & Batch == 1)
cal_1 <- calibration(Area ~ `Spec Conc`,
data = subset(tyrosol_1, Type == "Standard"))
print(cal_1)
# >
# > Call:
# > calibration(formula = Area ~ `Spec Conc`, data = subset(tyrosol_1,
# > Type == "Standard"))
# >
# > Coefficients:
# > (Intercept) `Spec Conc`
# > 3.762 7.508
# >
# > Adjusted R-squared: 0.9998
# > Sum relative error: 1.558
# >
# > Blanks:
# > [1] 0.07470126 0.06534690 0.06093782 0.00000000
# >
# > `Spec Conc` lwr upr
# > LOD 0.0144 0.00952 0.0293
# > LOQ 5.9300 3.92000 12.1000
# >
# > Check for normality of residuals:
# >
# > Shapiro-Wilk normality test
# >
# > data: residuals(calibration(formula = Area ~ `Spec Conc`, data = subset(tyrosol_1, )residuals( Type == "Standard")))
# > W = 0.87897, p-value = 0.1841
# >
# > Check for homoscedasticity of residuals:
# >
# > studentized Breusch-Pagan test
# >
# > data: Area ~ `Spec Conc`
# > BP = 0.40323, df = 1, p-value = 0.5254
plot(cal_1)
Based on cal_1, the tyrosol concentrations can be calculated for all samples
using inv_predict(). The argument below_lod = 0 specifies that
concentrations below limit of detection (LOD) should be set to zero.
tyrosol_1$`Calc Conc` <- inv_predict(cal_1, tyrosol_1$Area, below_lod = 0)
head(tyrosol_1)
# > Compound Type Batch Name Area Spec Conc Calc Conc
# > 1 Tyrosol Extraction blank 1 Blank 1 0.0000 NA 0.00000
# > 2 Tyrosol Extraction blank 1 Blank 2 0.0000 NA 0.00000
# > 3 Tyrosol Sample 1 ZS-001 328.3436 NA 43.23037
# > 4 Tyrosol Sample 1 ZS-002 282.9309 NA 37.18195
# > 5 Tyrosol Sample 1 ZS-003 296.2863 NA 38.96072
# > 6 Tyrosol Sample 1 ZS-019 243.0258 NA 31.86707
Working with data.tables
To process all compounds and analysis batches together, the phenolics data is
converted to data.tables.
dt <- lapply(phenolics, as.data.table)
To replicate the following steps, try to organize your data in the same way as
shown before. If you want to read in your data directly as data.table, use
their fread() function, for instance.
Batch calibration
Subsequently, calibration() and inv_predict() are applied by compound and
batch.
dt$seq[, `Calc Conc` := calibration(Area ~ `Spec Conc`, .SD[Type == "Standard"]) |>
inv_predict(Area, below_lod = 0),
by = .(Compound, Batch)]
head(dt$seq)
# > Compound Type Batch Name Area Spec Conc Calc Conc
# > 1: Tyrosol Extraction blank 1 Blank 1 0.0000 NA 0.00000
# > 2: Tyrosol Extraction blank 1 Blank 2 0.0000 NA 0.00000
# > 3: Tyrosol Sample 1 ZS-001 328.3436 NA 43.23037
# > 4: Tyrosol Sample 1 ZS-002 282.9309 NA 37.18195
# > 5: Tyrosol Sample 1 ZS-003 296.2863 NA 38.96072
# > 6: Tyrosol Sample 1 ZS-019 243.0258 NA 31.86707
Calibration parameters like LODs, LOQs, or adjusted R^2^ may be stored in a separate list item for later use.
dt$cal <- dt$seq[Type == "Standard", calibration(Area ~ `Spec Conc`) |>
as.list(c("coef", "adj.r.squared", "lod", "loq")),
by = .(Compound, Batch)]
print(dt$cal)
# > Compound Batch (Intercept) `Spec Conc` adj.r.squared lod loq
# > 1: Tyrosol 1 3.76202803 7.508184 0.9997915 0.0143933 5.925273
# > 2: Tyrosol 2 3.07268449 7.601129 0.9992824 0.0092660 10.909912
# > 3: Tyrosol 3 0.05587782 7.309053 0.9999764 0.0000000 2.311322
# > 4: Vanillin 1 25.17794786 51.529670 0.9998175 0.0040133 5.623778
# > 5: Vanillin 2 24.44314369 51.774820 0.9992359 0.0003509 11.408191
# > 6: Vanillin 3 10.62899985 50.819997 0.9999641 0.0000000 2.886073
Blank subtraction
With the calculated concentrations at hand, the sample concentrations are subtracted by the extraction blanks to correct for potential lab-borne contamination.
dt$seq[, `Clean Conc` := `Calc Conc` - mean(
`Calc Conc`[Type == "Extraction blank"], na.rm = T),
by = .(Batch, Compound)]
Merging tables
The sequence table is merged with the sample table and the contents of phenolic compounds are calculated from the extraction volume, sample weight, and dilution factor.
dt$res <- merge(dt$seq, dt$samples, by = "Name")
dt$res[, Content := `Clean Conc` * (Extract / Weight) * Dilution]
head(dt$res)
# > Name Compound Type Batch Area Spec Conc Calc Conc Clean Conc Day
# > 1: ZS-001 Tyrosol Sample 1 328.3436 NA 43.23037 43.23037 0
# > 2: ZS-001 Vanillin Sample 1 1876.9337 NA 35.93572 35.93572 0
# > 3: ZS-002 Tyrosol Sample 1 282.9309 NA 37.18195 37.18195 0
# > 4: ZS-002 Vanillin Sample 1 1578.6261 NA 30.14667 30.14667 0
# > 5: ZS-003 Tyrosol Sample 1 296.2863 NA 38.96072 38.96072 0
# > 6: ZS-003 Vanillin Sample 1 1593.6272 NA 30.43779 30.43779 0
# > Lighting Sterilization Treatment Replicate Weight Extract Dilution
# > 1: UV sterilized Photooxidation 1 2.5037 12.5 5
# > 2: UV sterilized Photooxidation 1 2.5037 12.5 5
# > 3: UV sterilized Photooxidation 2 2.5018 12.5 5
# > 4: UV sterilized Photooxidation 2 2.5018 12.5 5
# > 5: UV sterilized Photooxidation 3 2.5048 12.5 5
# > 6: UV sterilized Photooxidation 3 2.5048 12.5 5
# > Content
# > 1: 1079.1621
# > 2: 897.0653
# > 3: 928.8800
# > 4: 753.1246
# > 5: 972.1515
# > 6: 759.4865
Plotting
For plotting the data using ggplot2, the contents are summarized by mean and confidence interval (CI).
dt$sum <- dt$res[, .(Content = mean(Content, na.rm = T),
CI = CI(Content, na.rm = T)),
by = .(Compound, Treatment, Day)]
ggplot(dt$sum, aes(x = Day, y = Content)) +
geom_errorbar(aes(ymin = Content - CI, ymax = Content + CI, group = Treatment),
width = 1, position = position_dodge(1)) +
geom_point(aes(shape = Treatment, fill = Treatment),
position = position_dodge(1)) +
xlab("Day of incubation") +
ylab(expression("Phenolic content"~"["*mg~kg^-1*"]")) +
facet_wrap(~ Compound, ncol = 2, scales = "free") +
scale_shape_manual(values = c(21,24)) +
scale_fill_manual(values = c("black", "white")) +
theme_publish()
References
Steinmetz, Z., Kurtz, M.P., Zubrod, J.P., Meyer, A.H., Elsner, M., & Schaumann, G.E. (2019) Biodegradation and photooxidation of phenolic compounds in soil—A compound-specific stable isotope approach. Chemosphere 230, 210-218. DOI: 10.1016/j.chemosphere.2019.05.030.
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