Update uv_spectra.Rmd

vignettes/articles/uv_spectra.Rmd

@@ -62,14 +62,14 @@ matplot(pktab$ref_spectra[,idx_99],type='l', ylab="Abs")
 legend("top", "t = 0.99", bty = "n")
 ```
 
-We can then extract retention times for spectra above the desired spectral similarity threshold with the reference spectra.
+We can also extract retention times for spectra above the desired spectral similarity threshold with the reference spectra.
 
 ```{r}
 rts_99 = pk_tab$pk_meta["rt", idx_99]
 rts_99
 ```
 
-And identify peaks that match the desired criteria. Below the `r length(idx_99)` peaks that match the reference peak at the 99% level are marked with solid black lines and the `r length(idx_97)` peaks that match the reference at the 97% level are marked with dotted gray lines.
+Below we plot traces of our four chromatograms at 210 nm. The `r length(idx_99)` peaks that match the reference spectrum at the 99% level are marked with solid black lines and the `r length(idx_97)` peaks that match the reference spectrum at the 97% level are marked with dotted gray lines.
 
 ```{r}
 par(mfrow = c(1, 1))
@@ -81,6 +81,10 @@ abline(v = pk_tab$pk_meta["rt", idx_97], lty=2, col = "darkgray")
 
 If we are interested in the combined area of these peaks, we could also use these indices to sum them together.
 
+```{r}
+apply(pktab$tab[,idx_99], 1, sum)
+```
+
 `chromatographR` also includes a function (`cluster_spectra`) to group spectra using hierarchical clustering. We can set the seed with the `iseed` argument to ensure repeatability among instantiations of this vignette. For simplicity, we use the argument `max.only = TRUE` to return only the largest clusters.
 
 ```{r}