Nothing
R/region_calc.R
In SOMnmR: Analysis of Soil Organic Matter using Nuclear Magnetic Resonance
#' Functional groups calculation
#'
#' This function loads, integrates and calculates the functional group distribution from the raw spectra.
#' Produces also the molecular mixing model fitting if NC data is provided.
#' Output is a list with the raw data, integrals and corrected spectra.
#' @param file The raw file
#' @param batch_nmr Vector with file names, default
#' @param NMRmeth Regions to be integrated, methods available include: "4region", "Bonanomi", "Smernik" and Molecular mixing model ("MMM").
#' @param FixNC TRUE or FALSE, for fixing or not the NC ratio on the sample fitting.
#' @param ecosys Standards to be used for the MMM, can be Terrestrial("Terr_Nelson" or "Terr_Baldock") or Aquatic ("Aqua_Nelson" or "Aqua_Baldock")
#' @param NMR_field Magnetic field of the NMR
#' @param NMR_rotation Rotation frequency of the sample probe in the NMR
#' @param mod_std File containing a modified NMR table
#' @param cndata The N:C data file created with mk_nc_data
#' @returns A data frame that contains the SSBs corrected C functional groups, or if the "MMM" method is selected, the result of the fitting of the "MMM".
#' @keywords fitting Molecular Mixing model Bonanomi
#' @import dplyr, InvervalSurgeon, data.table
#' @export
#' @examples
#' data("GarciaF200")
#' IntegralSSBc <- region_calc(GarciaF200, NMRmeth = "4region", NMR_field = 200, NMR_rotation = 6800)
region_calc <- function (batch_nmr = NULL, file = NULL, NMRmeth = NULL, FixNC,
NMR_field=NULL, NMR_rotation=NULL, ecosys=NULL,
cndata = NULL, mod_std = NULL) {
if (is.null(batch_nmr)) {
if (is.null(file)) {
stop("Please provide either a raw spectrum from the read_raw_spec function or a raw NMR file")
} else {
## read the raw spectra from a file list
batch.nmr <- read_raw_spec(file = file)
}
} else {
batch.nmr <- batch_nmr
}
if (is.null(NMRmeth)) {
stop("Please choose an preset region model composition by typing 'MMM' for Molecular mixing model, 'Bonanomi' or '4region'")
## Start processing for an Integration of 4 regions
} else if (NMRmeth == "4region") {
From <- NULL
To <- NULL
ID <- NULL
Component <- NULL
Component_ssb <- NULL
ssb_ofset <- NULL
sbb_index <- NULL
## Integral function
Integral <- int_nmr (raw.spec = batch.nmr, NMRmeth = NMRmeth, NMR_field = NMR_field, NMR_rotation = NMR_rotation)
## Variable of end result
NMR.end <- NULL
## Start loop to process all integrals
for (i in 1:length(Integral)) {
## Name of sample
name <- Integral[[i]]$name
## paste which sample is used now
message(paste("Integrating Sample: ", name, ", start date: ", Sys.time(), sep = ""))
## create progress bar for the standards combinations
pb <- txtProgressBar(min = 1, max = length(batch.nmr), style = 3)
setTxtProgressBar(pb, i)
## Removed messages from tidy
## Sum of the Central integrals, without Spinning Sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE) %>%
group_by(Component) %>%
summarise(Integral = sum(Integral), n = n())) -> sums_Integral
## Removed messages from tidy
## Sum of the Spinning Sidebands, discriminating each side
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(Component, Component_ssb, ssb_ofset) %>%
summarise(Integral = sum(Integral), n = n())) -> double_ssb
## Removed messages from tidy
## Chooses the max value between the sum of the SSB on each side, and multiplies by 2
suppressMessages(double_ssb %>%
group_by(Component_ssb, n) %>%
summarize(Integral = 2*max(Integral))) ->double_ssb
## Removed messages from tidy
## Renames the SSB from Component_ssb to Component
suppressMessages(double_ssb <- double_ssb %>%
group_by(Component_ssb) %>%
filter(n == max(n)) %>%
rename(Component = Component_ssb) %>%
ungroup())
## Removed messages from tidy
## takes the Integral table and selects the regions in which the main integral is overlaping with the spinning sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE, is.na(sbb_index) == FALSE) %>%
group_by(Component, sbb_index) %>%
summarise(n = n())) -> ossb
## Removed messages from tidy
## takes the Integral table and selects the regions in the spinning sidebands is not overlaping and multiplies it by -1
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(sbb_index) %>%
summarise(Integral = sum(Integral)*-1,n = n())) -> ossb2
## takes the Integral table and selects the regions in the spinning sidebands is not overlaping and multiplies it by -1
matching_rows <- NULL
for (j in 1:nrow(ossb)) {
index <- ossb$sbb_index[j]
result <- ossb2 %>%
filter(sbb_index == index)
matching_rows <- bind_rows(matching_rows, result)
}
joined_df <- merge(ossb, matching_rows, by = "sbb_index")
## Creates a table with sums_Integral: the sum of the main integrals, double_ssb: the ssb area multiplied by 2, and joined_df: the equivalent to the overalpping ssb to be substracted
table_merged <- rbind(sums_Integral[1:2], double_ssb[, c('Component', 'Integral')], joined_df[, c('Component', 'Integral')])
## Creates the final sum of integrals
table_merged %>%
group_by(Component) %>%
summarise(Integral = sum(Integral)) -> final_integral
## Normalizing the sum of integrals to 100
## Total value of the sum
norm <- sum(data.frame(final_integral$Integral))
## Normalized values
normalized.Int <- t(final_integral$Integral/norm)*100
## Giving back the names to the columns
colnames(normalized.Int) <- as.character(final_integral$Component)
##Final result
NMR.end[[i]] <- data.frame(cbind(name, normalized.Int))
}
## Start processing for an Integration of 4 regions
} else if (NMRmeth == "Smernik") {
From <- NULL
To <- NULL
ID <- NULL
Component <- NULL
Component_ssb <- NULL
ssb_ofset <- NULL
sbb_index <- NULL
## Integral function
Integral <- int_nmr (raw.spec = batch.nmr, NMRmeth = NMRmeth, NMR_field = NMR_field, NMR_rotation = NMR_rotation)
## Variable of end result
NMR.end <- NULL
## Start loop to process all integrals
for (i in 1:length(Integral)) {
## Name of sample
name <- Integral[[i]]$name
## paste which sample is used now
message(paste("Integrating Sample: ", name, ", start date: ", Sys.time(), sep = ""))
## create progress bar for the standards combinations
pb <- txtProgressBar(min = 1, max = length(batch.nmr), style = 3)
setTxtProgressBar(pb, i)
## Removed messages from tidy
## Sum of the Central integrals, without Spinning Sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE) %>%
group_by(Component) %>%
summarise(Integral = sum(Integral), n = n())) -> sums_Integral
## Removed messages from tidy
## Sum of the Spinning Sidebands, discriminating each side
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(Component, Component_ssb, ssb_ofset) %>%
summarise(Integral = sum(Integral), n = n())) -> double_ssb
## Removed messages from tidy
## Chooses the max value between the sum of the SSB on each side, and multiplies by 2
suppressMessages(double_ssb %>%
group_by(Component_ssb, n) %>%
summarize(Integral = 2*max(Integral))) ->double_ssb
## Removed messages from tidy
## Renames the SSB from Component_ssb to Component
suppressMessages(double_ssb <- double_ssb %>%
group_by(Component_ssb) %>%
filter(n == max(n)) %>%
rename(Component = Component_ssb) %>%
ungroup())
## Removed messages from tidy
## takes the Integral table and selects the regions in which the main integral is overlaping with the spinning sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE, is.na(sbb_index) == FALSE) %>%
group_by(Component, sbb_index) %>%
summarise(n = n())) -> ossb
## Removed messages from tidy
## takes the Integral table and selects the regions in the spinning sidebands is not overlaping and multiplies it by -1
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(sbb_index) %>%
summarise(Integral = sum(Integral)*-1,n = n())) -> ossb2
## takes the Integral table and selects the regions in the spinning sidebands is not overlaping and multiplies it by -1
matching_rows <- NULL
for (j in 1:nrow(ossb)) {
index <- ossb$sbb_index[j]
result <- ossb2 %>%
filter(sbb_index == index)
matching_rows <- bind_rows(matching_rows, result)
}
joined_df <- merge(ossb, matching_rows, by = "sbb_index")
## Creates a table with sums_Integral: the sum of the main integrals, double_ssb: the ssb area multiplied by 2, and joined_df: the equivalent to the overalpping ssb to be substracted
table_merged <- rbind(sums_Integral[1:2], double_ssb[, c('Component', 'Integral')], joined_df[, c('Component', 'Integral')])
## Creates the final sum of integrals
table_merged %>%
group_by(Component) %>%
summarise(Integral = sum(Integral)) -> final_integral
## Normalizing the sum of integrals to 100
## Total value of the sum
norm <- sum(data.frame(final_integral$Integral))
## Normalized values
normalized.Int <- t(final_integral$Integral/norm)*100
## Giving back the names to the columns
colnames(normalized.Int) <- as.character(final_integral$Component)
##Final result
NMR.end[[i]] <- data.frame(cbind(name, normalized.Int))
}
## Start of the processing for regions according to Bonanomi et al.
} else if (NMRmeth == "Bonanomi") {
From <- NULL
To <- NULL
ID <- NULL
Component <- NULL
Component_ssb <- NULL
ssb_ofset <- NULL
sbb_index <- NULL
## Integral function
Integral <- int_nmr (raw.spec = batch.nmr, NMRmeth = NMRmeth, NMR_field = NMR_field, NMR_rotation = NMR_rotation)
## Variable of end result
NMR.end <- NULL
## Start loop to process all integrals
for (i in 1:length(Integral)) {
## Name of sample
name <- Integral[[i]]$name
## paste which sample is used now
message(paste("Integrating Sample: ", name, ", start date: ", Sys.time(), sep = ""))
## create progress bar for the standards combinations
pb <- txtProgressBar(min = 1, max = length(batch.nmr), style = 3)
setTxtProgressBar(pb, i)
## Removed messages from tidy
## Sum of the Central integrals, without Spinning Sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE) %>%
group_by(Component) %>%
summarise(Integral = sum(Integral), n = n())) -> sums_Integral
## Removed messages from tidy
## Sum of the Spinning Sidebands, discriminating each side
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(Component, Component_ssb, ssb_ofset) %>%
summarise(Integral = sum(Integral), n = n())) -> double_ssb
## Removed messages from tidy
## Chooses the max value between the sum of the SSB on each side, and multiplies by 2
suppressMessages(double_ssb %>%
group_by(Component_ssb, n) %>%
summarize(Integral = 2*max(Integral))) ->double_ssb
## Removed messages from tidy
## Renames the SSB from Component_ssb to Component
suppressMessages(double_ssb <- double_ssb %>%
group_by(Component_ssb) %>%
filter(n == max(n)) %>%
rename(Component = Component_ssb) %>%
ungroup())
## Removed messages from tidy
## takes the Integral table and selects the regions in which the main integral is overlaping with the spinning sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE, is.na(sbb_index) == FALSE) %>%
group_by(Component, sbb_index) %>%
summarise(n = n())) -> ossb
## Removed messages from tidy
## takes the Integral table and selects the regions in the spinning sidebands is not overlaping and multiplies it by -1
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(sbb_index) %>%
summarise(Integral = sum(Integral)*-1,n = n())) -> ossb2
## takes the Integral table and selects the regions in the spinning sidebands is not overlaping and multiplies it by -1
matching_rows <- NULL
for (j in 1:nrow(ossb)) {
index <- ossb$sbb_index[j]
result <- ossb2 %>%
filter(sbb_index == index)
matching_rows <- bind_rows(matching_rows, result)
}
joined_df <- merge(ossb, matching_rows, by = "sbb_index")
## Creates a table with sums_Integral: the sum of the main integrals, double_ssb: the ssb area multiplied by 2, and joined_df: the equivalent to the overalpping ssb to be substracted
table_merged <- rbind(sums_Integral[1:2], double_ssb[, c('Component', 'Integral')], joined_df[, c('Component', 'Integral')])
## Creates the final sum of integrals
table_merged %>%
group_by(Component) %>%
summarise(Integral = sum(Integral)) -> final_integral
## Normalizing the sum of integrals to 100
## Total value of the sum
norm <- sum(data.frame(final_integral$Integral))
## Normalized values
normalized.Int <- t(final_integral$Integral/norm)*100
## Giving back the names to the columns
colnames(normalized.Int) <- as.character(final_integral$Component)
## Final result
NMR.end[[i]] <- data.frame(cbind(name, normalized.Int))
}
} else if (NMRmeth == "MMM") {
From <- NULL
To <- NULL
ID <- NULL
Component <- NULL
Component_ssb <- NULL
ssb_ofset <- NULL
sbb_index <- NULL
## loop to process all samples
Integral <- int_nmr (raw.spec = batch.nmr, NMRmeth = NMRmeth, NMR_field = NMR_field, NMR_rotation = NMR_rotation)
###sum of raw integrals
NMR.end <- NULL
raw.spec.end <- NULL
for (i in 1:length(Integral)) {
NCval <- c(as.numeric(cndata[[i]]$NC))
name <- Integral[[i]]$name
## paste which sample is used now
message(paste("Integrating Sample: ", name, ", start date: ", Sys.time(), sep = ""))
## create progress bar for the standards combinations
pb <- txtProgressBar(min = 1, max = length(batch.nmr), style = 3)
setTxtProgressBar(pb, i)
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE) %>%
group_by(Component) %>%
summarise(Integral = sum(Integral), n = n())) -> sums_Integral
###sum of double spinning sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(Component, Component_ssb, ssb_ofset) %>%
summarise(Integral = sum(Integral), n = n())) -> double_ssb
suppressMessages(double_ssb %>%
group_by(Component_ssb, n) %>%
summarize(Integral = 2*max(Integral))) ->double_ssb
suppressMessages(double_ssb <- double_ssb %>%
group_by(Component_ssb) %>%
filter(n == max(n)) %>%
rename(Component = Component_ssb) %>%
ungroup())
###sum of overlaping spinning sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE, is.na(sbb_index) == FALSE) %>%
group_by(Component, sbb_index) %>%
summarise(n = n())) -> ossb
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(sbb_index) %>%
summarise(Integral = sum(Integral)*-1,n = n())) -> ossb2
matching_rows <- NULL
for (j in 1:nrow(ossb)) {
index <- ossb$sbb_index[j]
result <- ossb2 %>%
filter(sbb_index == index)
matching_rows <- bind_rows(matching_rows, result)
}
joined_df <- merge(ossb, matching_rows, by = "sbb_index")
table_merged <- rbind(sums_Integral[1:2], double_ssb[, c('Component', 'Integral')], joined_df[, c('Component', 'Integral')])
table_merged %>%
group_by(Component) %>%
summarise(Integral = sum(Integral)) -> final_integral
###finished calculation of integrals
if (!is.null(ecosys)){
norm <- sum(data.frame(final_integral$Integral))
normalized.Int <- (final_integral$Integral/norm)*100
normalized.Int <- data.frame(final_integral$Component, normalized.Int)
normalized.Int <-setNames(normalized.Int,c("Component", "Integral"))
int_NMR <- NMR_table(NMRmeth = NMRmeth)
normalized.Int <- merge(int_NMR, normalized.Int, by = 'Component')
normalized.Int %>%
group_by(ID) %>%
summarise(From = From, To = To, Integral = sum(Integral)) %>%
arrange(From) -> normalized.Int
normalized.Int <- rbind(NCval, normalized.Int[,4])
raw.spec.end[[i]] <- list("name" = name, "data" = list("Integral" = data.frame(normalized.Int[1:8,])))
} else if (is.null(ecosys)) {
norm <- sum(data.frame(final_integral$Integral))
normalized.Int <- t(final_integral$Integral/norm)*100
colnames(normalized.Int) <- as.character(final_integral$Component)
#final_integral <- data.frame(final_integral$Component, normalized.Int)
#integral.end <- data.frame(file.name,integral.end)
NMR.end[[i]] <- data.frame(cbind(name, normalized.Int))
}
}
if (is.null(ecosys)) {
return(NMR.end)
}
else if (ecosys == "Terr_Nelson") {
stdmat <- std_nmr(ecosys = "Terr_Nelson")
if (FixNC == TRUE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = TRUE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
} else if (FixNC == FALSE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = FALSE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
}
}
else if (ecosys == "Terr_Baldock") {
stdmat <- std_nmr(ecosys = "Terr_Baldock")
if (FixNC == TRUE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = TRUE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
} else if (FixNC == FALSE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = FALSE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
}
}
else if (ecosys == "Aqua_Nelson") {
stdmat <- std_nmr(ecosys = "Aqua_Nelson")
if (FixNC == TRUE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = TRUE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
} else if (FixNC == FALSE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = FALSE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
}
}
else if (ecosys == "Aqua_Baldock") {
stdmat <- std_nmr(ecosys = "Aqua_Baldock")
if (FixNC == TRUE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = TRUE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
} else if (FixNC == FALSE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = FALSE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
}
}
}
return(NMR.end)
}
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#' Functional groups calculation
#'
#' This function loads, integrates and calculates the functional group distribution from the raw spectra.
#' Produces also the molecular mixing model fitting if NC data is provided.
#' Output is a list with the raw data, integrals and corrected spectra.
#' @param file The raw file
#' @param batch_nmr Vector with file names, default
#' @param NMRmeth Regions to be integrated, methods available include: "4region", "Bonanomi", "Smernik" and Molecular mixing model ("MMM").
#' @param FixNC TRUE or FALSE, for fixing or not the NC ratio on the sample fitting.
#' @param ecosys Standards to be used for the MMM, can be Terrestrial("Terr_Nelson" or "Terr_Baldock") or Aquatic ("Aqua_Nelson" or "Aqua_Baldock")
#' @param NMR_field Magnetic field of the NMR
#' @param NMR_rotation Rotation frequency of the sample probe in the NMR
#' @param mod_std File containing a modified NMR table
#' @param cndata The N:C data file created with mk_nc_data
#' @returns A data frame that contains the SSBs corrected C functional groups, or if the "MMM" method is selected, the result of the fitting of the "MMM".
#' @keywords fitting Molecular Mixing model Bonanomi
#' @import dplyr, InvervalSurgeon, data.table
#' @export
#' @examples
#' data("GarciaF200")
#' IntegralSSBc <- region_calc(GarciaF200, NMRmeth = "4region", NMR_field = 200, NMR_rotation = 6800)
region_calc <- function (batch_nmr = NULL, file = NULL, NMRmeth = NULL, FixNC,
NMR_field=NULL, NMR_rotation=NULL, ecosys=NULL,
cndata = NULL, mod_std = NULL) {
if (is.null(batch_nmr)) {
if (is.null(file)) {
stop("Please provide either a raw spectrum from the read_raw_spec function or a raw NMR file")
} else {
## read the raw spectra from a file list
batch.nmr <- read_raw_spec(file = file)
}
} else {
batch.nmr <- batch_nmr
}
if (is.null(NMRmeth)) {
stop("Please choose an preset region model composition by typing 'MMM' for Molecular mixing model, 'Bonanomi' or '4region'")
## Start processing for an Integration of 4 regions
} else if (NMRmeth == "4region") {
From <- NULL
To <- NULL
ID <- NULL
Component <- NULL
Component_ssb <- NULL
ssb_ofset <- NULL
sbb_index <- NULL
## Integral function
Integral <- int_nmr (raw.spec = batch.nmr, NMRmeth = NMRmeth, NMR_field = NMR_field, NMR_rotation = NMR_rotation)
## Variable of end result
NMR.end <- NULL
## Start loop to process all integrals
for (i in 1:length(Integral)) {
## Name of sample
name <- Integral[[i]]$name
## paste which sample is used now
message(paste("Integrating Sample: ", name, ", start date: ", Sys.time(), sep = ""))
## create progress bar for the standards combinations
pb <- txtProgressBar(min = 1, max = length(batch.nmr), style = 3)
setTxtProgressBar(pb, i)
## Removed messages from tidy
## Sum of the Central integrals, without Spinning Sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE) %>%
group_by(Component) %>%
summarise(Integral = sum(Integral), n = n())) -> sums_Integral
## Removed messages from tidy
## Sum of the Spinning Sidebands, discriminating each side
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(Component, Component_ssb, ssb_ofset) %>%
summarise(Integral = sum(Integral), n = n())) -> double_ssb
## Removed messages from tidy
## Chooses the max value between the sum of the SSB on each side, and multiplies by 2
suppressMessages(double_ssb %>%
group_by(Component_ssb, n) %>%
summarize(Integral = 2*max(Integral))) ->double_ssb
## Removed messages from tidy
## Renames the SSB from Component_ssb to Component
suppressMessages(double_ssb <- double_ssb %>%
group_by(Component_ssb) %>%
filter(n == max(n)) %>%
rename(Component = Component_ssb) %>%
ungroup())
## Removed messages from tidy
## takes the Integral table and selects the regions in which the main integral is overlaping with the spinning sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE, is.na(sbb_index) == FALSE) %>%
group_by(Component, sbb_index) %>%
summarise(n = n())) -> ossb
## Removed messages from tidy
## takes the Integral table and selects the regions in the spinning sidebands is not overlaping and multiplies it by -1
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(sbb_index) %>%
summarise(Integral = sum(Integral)*-1,n = n())) -> ossb2
## takes the Integral table and selects the regions in the spinning sidebands is not overlaping and multiplies it by -1
matching_rows <- NULL
for (j in 1:nrow(ossb)) {
index <- ossb$sbb_index[j]
result <- ossb2 %>%
filter(sbb_index == index)
matching_rows <- bind_rows(matching_rows, result)
}
joined_df <- merge(ossb, matching_rows, by = "sbb_index")
## Creates a table with sums_Integral: the sum of the main integrals, double_ssb: the ssb area multiplied by 2, and joined_df: the equivalent to the overalpping ssb to be substracted
table_merged <- rbind(sums_Integral[1:2], double_ssb[, c('Component', 'Integral')], joined_df[, c('Component', 'Integral')])
## Creates the final sum of integrals
table_merged %>%
group_by(Component) %>%
summarise(Integral = sum(Integral)) -> final_integral
## Normalizing the sum of integrals to 100
## Total value of the sum
norm <- sum(data.frame(final_integral$Integral))
## Normalized values
normalized.Int <- t(final_integral$Integral/norm)*100
## Giving back the names to the columns
colnames(normalized.Int) <- as.character(final_integral$Component)
##Final result
NMR.end[[i]] <- data.frame(cbind(name, normalized.Int))
}
## Start processing for an Integration of 4 regions
} else if (NMRmeth == "Smernik") {
From <- NULL
To <- NULL
ID <- NULL
Component <- NULL
Component_ssb <- NULL
ssb_ofset <- NULL
sbb_index <- NULL
## Integral function
Integral <- int_nmr (raw.spec = batch.nmr, NMRmeth = NMRmeth, NMR_field = NMR_field, NMR_rotation = NMR_rotation)
## Variable of end result
NMR.end <- NULL
## Start loop to process all integrals
for (i in 1:length(Integral)) {
## Name of sample
name <- Integral[[i]]$name
## paste which sample is used now
message(paste("Integrating Sample: ", name, ", start date: ", Sys.time(), sep = ""))
## create progress bar for the standards combinations
pb <- txtProgressBar(min = 1, max = length(batch.nmr), style = 3)
setTxtProgressBar(pb, i)
## Removed messages from tidy
## Sum of the Central integrals, without Spinning Sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE) %>%
group_by(Component) %>%
summarise(Integral = sum(Integral), n = n())) -> sums_Integral
## Removed messages from tidy
## Sum of the Spinning Sidebands, discriminating each side
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(Component, Component_ssb, ssb_ofset) %>%
summarise(Integral = sum(Integral), n = n())) -> double_ssb
## Removed messages from tidy
## Chooses the max value between the sum of the SSB on each side, and multiplies by 2
suppressMessages(double_ssb %>%
group_by(Component_ssb, n) %>%
summarize(Integral = 2*max(Integral))) ->double_ssb
## Removed messages from tidy
## Renames the SSB from Component_ssb to Component
suppressMessages(double_ssb <- double_ssb %>%
group_by(Component_ssb) %>%
filter(n == max(n)) %>%
rename(Component = Component_ssb) %>%
ungroup())
## Removed messages from tidy
## takes the Integral table and selects the regions in which the main integral is overlaping with the spinning sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE, is.na(sbb_index) == FALSE) %>%
group_by(Component, sbb_index) %>%
summarise(n = n())) -> ossb
## Removed messages from tidy
## takes the Integral table and selects the regions in the spinning sidebands is not overlaping and multiplies it by -1
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(sbb_index) %>%
summarise(Integral = sum(Integral)*-1,n = n())) -> ossb2
## takes the Integral table and selects the regions in the spinning sidebands is not overlaping and multiplies it by -1
matching_rows <- NULL
for (j in 1:nrow(ossb)) {
index <- ossb$sbb_index[j]
result <- ossb2 %>%
filter(sbb_index == index)
matching_rows <- bind_rows(matching_rows, result)
}
joined_df <- merge(ossb, matching_rows, by = "sbb_index")
## Creates a table with sums_Integral: the sum of the main integrals, double_ssb: the ssb area multiplied by 2, and joined_df: the equivalent to the overalpping ssb to be substracted
table_merged <- rbind(sums_Integral[1:2], double_ssb[, c('Component', 'Integral')], joined_df[, c('Component', 'Integral')])
## Creates the final sum of integrals
table_merged %>%
group_by(Component) %>%
summarise(Integral = sum(Integral)) -> final_integral
## Normalizing the sum of integrals to 100
## Total value of the sum
norm <- sum(data.frame(final_integral$Integral))
## Normalized values
normalized.Int <- t(final_integral$Integral/norm)*100
## Giving back the names to the columns
colnames(normalized.Int) <- as.character(final_integral$Component)
##Final result
NMR.end[[i]] <- data.frame(cbind(name, normalized.Int))
}
## Start of the processing for regions according to Bonanomi et al.
} else if (NMRmeth == "Bonanomi") {
From <- NULL
To <- NULL
ID <- NULL
Component <- NULL
Component_ssb <- NULL
ssb_ofset <- NULL
sbb_index <- NULL
## Integral function
Integral <- int_nmr (raw.spec = batch.nmr, NMRmeth = NMRmeth, NMR_field = NMR_field, NMR_rotation = NMR_rotation)
## Variable of end result
NMR.end <- NULL
## Start loop to process all integrals
for (i in 1:length(Integral)) {
## Name of sample
name <- Integral[[i]]$name
## paste which sample is used now
message(paste("Integrating Sample: ", name, ", start date: ", Sys.time(), sep = ""))
## create progress bar for the standards combinations
pb <- txtProgressBar(min = 1, max = length(batch.nmr), style = 3)
setTxtProgressBar(pb, i)
## Removed messages from tidy
## Sum of the Central integrals, without Spinning Sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE) %>%
group_by(Component) %>%
summarise(Integral = sum(Integral), n = n())) -> sums_Integral
## Removed messages from tidy
## Sum of the Spinning Sidebands, discriminating each side
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(Component, Component_ssb, ssb_ofset) %>%
summarise(Integral = sum(Integral), n = n())) -> double_ssb
## Removed messages from tidy
## Chooses the max value between the sum of the SSB on each side, and multiplies by 2
suppressMessages(double_ssb %>%
group_by(Component_ssb, n) %>%
summarize(Integral = 2*max(Integral))) ->double_ssb
## Removed messages from tidy
## Renames the SSB from Component_ssb to Component
suppressMessages(double_ssb <- double_ssb %>%
group_by(Component_ssb) %>%
filter(n == max(n)) %>%
rename(Component = Component_ssb) %>%
ungroup())
## Removed messages from tidy
## takes the Integral table and selects the regions in which the main integral is overlaping with the spinning sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE, is.na(sbb_index) == FALSE) %>%
group_by(Component, sbb_index) %>%
summarise(n = n())) -> ossb
## Removed messages from tidy
## takes the Integral table and selects the regions in the spinning sidebands is not overlaping and multiplies it by -1
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(sbb_index) %>%
summarise(Integral = sum(Integral)*-1,n = n())) -> ossb2
## takes the Integral table and selects the regions in the spinning sidebands is not overlaping and multiplies it by -1
matching_rows <- NULL
for (j in 1:nrow(ossb)) {
index <- ossb$sbb_index[j]
result <- ossb2 %>%
filter(sbb_index == index)
matching_rows <- bind_rows(matching_rows, result)
}
joined_df <- merge(ossb, matching_rows, by = "sbb_index")
## Creates a table with sums_Integral: the sum of the main integrals, double_ssb: the ssb area multiplied by 2, and joined_df: the equivalent to the overalpping ssb to be substracted
table_merged <- rbind(sums_Integral[1:2], double_ssb[, c('Component', 'Integral')], joined_df[, c('Component', 'Integral')])
## Creates the final sum of integrals
table_merged %>%
group_by(Component) %>%
summarise(Integral = sum(Integral)) -> final_integral
## Normalizing the sum of integrals to 100
## Total value of the sum
norm <- sum(data.frame(final_integral$Integral))
## Normalized values
normalized.Int <- t(final_integral$Integral/norm)*100
## Giving back the names to the columns
colnames(normalized.Int) <- as.character(final_integral$Component)
## Final result
NMR.end[[i]] <- data.frame(cbind(name, normalized.Int))
}
} else if (NMRmeth == "MMM") {
From <- NULL
To <- NULL
ID <- NULL
Component <- NULL
Component_ssb <- NULL
ssb_ofset <- NULL
sbb_index <- NULL
## loop to process all samples
Integral <- int_nmr (raw.spec = batch.nmr, NMRmeth = NMRmeth, NMR_field = NMR_field, NMR_rotation = NMR_rotation)
###sum of raw integrals
NMR.end <- NULL
raw.spec.end <- NULL
for (i in 1:length(Integral)) {
NCval <- c(as.numeric(cndata[[i]]$NC))
name <- Integral[[i]]$name
## paste which sample is used now
message(paste("Integrating Sample: ", name, ", start date: ", Sys.time(), sep = ""))
## create progress bar for the standards combinations
pb <- txtProgressBar(min = 1, max = length(batch.nmr), style = 3)
setTxtProgressBar(pb, i)
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE) %>%
group_by(Component) %>%
summarise(Integral = sum(Integral), n = n())) -> sums_Integral
###sum of double spinning sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(Component, Component_ssb, ssb_ofset) %>%
summarise(Integral = sum(Integral), n = n())) -> double_ssb
suppressMessages(double_ssb %>%
group_by(Component_ssb, n) %>%
summarize(Integral = 2*max(Integral))) ->double_ssb
suppressMessages(double_ssb <- double_ssb %>%
group_by(Component_ssb) %>%
filter(n == max(n)) %>%
rename(Component = Component_ssb) %>%
ungroup())
###sum of overlaping spinning sidebands
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == FALSE, is.na(sbb_index) == FALSE) %>%
group_by(Component, sbb_index) %>%
summarise(n = n())) -> ossb
suppressMessages(Integral[[i]]$data$Integral %>%
filter(is.na(Component) == TRUE) %>%
group_by(sbb_index) %>%
summarise(Integral = sum(Integral)*-1,n = n())) -> ossb2
matching_rows <- NULL
for (j in 1:nrow(ossb)) {
index <- ossb$sbb_index[j]
result <- ossb2 %>%
filter(sbb_index == index)
matching_rows <- bind_rows(matching_rows, result)
}
joined_df <- merge(ossb, matching_rows, by = "sbb_index")
table_merged <- rbind(sums_Integral[1:2], double_ssb[, c('Component', 'Integral')], joined_df[, c('Component', 'Integral')])
table_merged %>%
group_by(Component) %>%
summarise(Integral = sum(Integral)) -> final_integral
###finished calculation of integrals
if (!is.null(ecosys)){
norm <- sum(data.frame(final_integral$Integral))
normalized.Int <- (final_integral$Integral/norm)*100
normalized.Int <- data.frame(final_integral$Component, normalized.Int)
normalized.Int <-setNames(normalized.Int,c("Component", "Integral"))
int_NMR <- NMR_table(NMRmeth = NMRmeth)
normalized.Int <- merge(int_NMR, normalized.Int, by = 'Component')
normalized.Int %>%
group_by(ID) %>%
summarise(From = From, To = To, Integral = sum(Integral)) %>%
arrange(From) -> normalized.Int
normalized.Int <- rbind(NCval, normalized.Int[,4])
raw.spec.end[[i]] <- list("name" = name, "data" = list("Integral" = data.frame(normalized.Int[1:8,])))
} else if (is.null(ecosys)) {
norm <- sum(data.frame(final_integral$Integral))
normalized.Int <- t(final_integral$Integral/norm)*100
colnames(normalized.Int) <- as.character(final_integral$Component)
#final_integral <- data.frame(final_integral$Component, normalized.Int)
#integral.end <- data.frame(file.name,integral.end)
NMR.end[[i]] <- data.frame(cbind(name, normalized.Int))
}
}
if (is.null(ecosys)) {
return(NMR.end)
}
else if (ecosys == "Terr_Nelson") {
stdmat <- std_nmr(ecosys = "Terr_Nelson")
if (FixNC == TRUE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = TRUE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
} else if (FixNC == FALSE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = FALSE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
}
}
else if (ecosys == "Terr_Baldock") {
stdmat <- std_nmr(ecosys = "Terr_Baldock")
if (FixNC == TRUE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = TRUE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
} else if (FixNC == FALSE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = FALSE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
}
}
else if (ecosys == "Aqua_Nelson") {
stdmat <- std_nmr(ecosys = "Aqua_Nelson")
if (FixNC == TRUE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = TRUE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
} else if (FixNC == FALSE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = FALSE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
}
}
else if (ecosys == "Aqua_Baldock") {
stdmat <- std_nmr(ecosys = "Aqua_Baldock")
if (FixNC == TRUE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = TRUE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
} else if (FixNC == FALSE) {
NMR.end <- fit_LCF(all.samples = raw.spec.end, all.standards = stdmat, amoSTD = 6, best.fits = 30, NMRmeth = NMRmeth, FixNC = FALSE)
## Elementar ratios (relative to C)
Cmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]+NMR.end$Protein*stdmat[[1]][9,1]+NMR.end$Lignin*stdmat[[1]][9,3]+
NMR.end$Lipid*stdmat[[1]][9,4]+NMR.end$Carbonyl*stdmat[[1]][9,5]+NMR.end$Char*stdmat[[1]][9,6])
Nmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]+NMR.end$Protein*stdmat[[1]][10,1]+NMR.end$Lignin*stdmat[[1]][10,3]+
NMR.end$Lipid*stdmat[[1]][10,4]+NMR.end$Carbonyl*stdmat[[1]][10,5]+NMR.end$Char*stdmat[[1]][10,6])
Hmol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]+NMR.end$Protein*stdmat[[1]][11,1]+NMR.end$Lignin*stdmat[[1]][11,3]+
NMR.end$Lipid*stdmat[[1]][11,4]+NMR.end$Carbonyl*stdmat[[1]][11,5]+NMR.end$Char*stdmat[[1]][11,6])
Omol <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]+NMR.end$Protein*stdmat[[1]][12,1]+NMR.end$Lignin*stdmat[[1]][12,3]+
NMR.end$Lipid*stdmat[[1]][12,4]+NMR.end$Carbonyl*stdmat[[1]][12,5]+NMR.end$Char*stdmat[[1]][12,6])
Cwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][9,2]*12.0107+NMR.end$Protein*stdmat[[1]][9,1]*12.0107+NMR.end$Lignin*stdmat[[1]][9,3]*12.0107+
NMR.end$Lipid*stdmat[[1]][9,4]*12.0107+NMR.end$Carbonyl*stdmat[[1]][9,5]*12.0107+NMR.end$Char*stdmat[[1]][9,6]*12.0107)
Nwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][10,2]*14.0067+NMR.end$Protein*stdmat[[1]][10,1]*14.0067+NMR.end$Lignin*stdmat[[1]][10,3]*14.0067+
NMR.end$Lipid*stdmat[[1]][10,4]*14.0067+NMR.end$Carbonyl*stdmat[[1]][10,5]*14.0067+NMR.end$Char*stdmat[[1]][10,6]*14.0067)
Hwgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][11,2]*1.00794+NMR.end$Protein*stdmat[[1]][11,1]*1.00794+NMR.end$Lignin*stdmat[[1]][11,3]*1.00794+
NMR.end$Lipid*stdmat[[1]][11,4]*1.00794+NMR.end$Carbonyl*stdmat[[1]][11,5]*1.00794+NMR.end$Char*stdmat[[1]][11,6]*1.00794)
Owgt <- as.numeric(NMR.end$Carbohydrates*stdmat[[1]][12,2]*15.994+NMR.end$Protein*stdmat[[1]][12,1]*15.994+NMR.end$Lignin*stdmat[[1]][12,3]*15.994+
NMR.end$Lipid*stdmat[[1]][12,4]*15.994+NMR.end$Carbonyl*stdmat[[1]][12,5]*15.994+NMR.end$Char*stdmat[[1]][12,6]*15.994)
swgt <- c(Cwgt + Nwgt + Hwgt +Owgt)
NOSC <- as.numeric(4+((2*Omol+3*Nmol-1*Hmol-4*Cmol)/Cmol))
## Final result
NMR.end <- cbind(NMR.end, Cmol, Nmol, Hmol, Omol, Cwgt/swgt, Nwgt/swgt, Hwgt/swgt, Owgt/swgt, NOSC)
}
}
}
return(NMR.end)
}
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