Calibration workflow
In envalysis: Miscellaneous Functions for Environmental Analyses

knitr::opts_chunk$set(
  echo = TRUE,
  warning = FALSE, 
  message = FALSE,
  comment = "# >"
)

data.table::setDTthreads(2)

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.

knitr::kable(phenolics$seq[c(1:4,73:74,76,78,80,83:84),], "simple",
             row.names = F)

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.

knitr::kable(phenolics$samples[c(1:2,4,41:42),], "simple",
             row.names = F)

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)

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)
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)

Working with `data.table`s

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)

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)

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)

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.