diff --git a/vignettes/RMassBankXCMS.Rnw b/vignettes/RMassBankXCMS.Rnw
index 678cb13f213126466ecbb24ff86ff27aa2233431..fdbd01beaed5268763007bdfebecaebfc6d641c0 100644
--- a/vignettes/RMassBankXCMS.Rnw
+++ b/vignettes/RMassBankXCMS.Rnw
@@ -39,7 +39,7 @@ options(width=74)
 \section{Introduction}
 
 As the RMassBank-workflow is described in the other manual, this document mainly explains how to utilize the
-XCMS-, MassBank-, andpeaklist-readMethods for step 1 of the workflow.
+XCMS-, MassBank-, and peaklist-readMethods for step 1 of the workflow.
 
 \section{Input files}
 
@@ -50,7 +50,7 @@ centroid\footnote{The term "centroid" here refers to any kind of data which are
 not in profile mode, i.e. don't have continuous m/z data. It does not refer to
 the (mathematical) centroid peak, i.e. the area-weighted mass peak.} or in
 profile mode.
-Data in the examples was acquired using an QTOF instrument.
+Data in the examples was acquired using a QTOF instrument.
 
 In the standard workflow, the file names are used to identify a
 compound: file names must be in the format \funcarg{xxxxxxxx\_1234\_xxx.mzXML},
@@ -71,7 +71,7 @@ library(RMassBankData)
 
 \subsection{Options}
 
-In the first part of the workflow, spectra are extracted from the files and processed. In the following example, we will process the Glulesquerellin spectra from the provided files.
+In the first part of the workflow, spectra are extracted from the files and processed. In the following example, we will process the Glucolesquerellin spectra from the provided files.
 
 For the workflow to work correctly, we use the default settings, and modify then to match the data acquisition method. The settings have to contain the same parameters as the mzR-method would for the workflow.
 
@@ -134,16 +134,30 @@ Args <- list(method="centWave",
 If you want to utilize XCMS for Step 1 of the workflow, you have to set the readMethod-parameter to "xcms" and - if you don't want to use standard values for findPeaks - pass on findPeaksArgs to the workflow.
 
 <<eval=TRUE>>=
-msmsList <- msmsRead(msmsList, files= msmsList@files, readMethod = "xcms", mode = "mH", Args = Args)
+msmsList <- msmsRead(msmsList, files= msmsList@files, 
+			readMethod = "xcms", mode = "mH", Args = Args)
 msmsList <- msmsWorkflow(msmsList, steps=2:8,
                                  mode="mH", readMethod="xcms")
 @
 
 You can of course run the rest of the workflow as usual, by - like here - setting steps to 1:8
 
+\subsection{Export the records}
+
+To export the records from the XCMS workflow, follow the same procedure as the standard RMassBank workflow, i.e.:
+
+<<>>=
+mb <- newMbWorkspace(msmsList)
+mb <- resetInfolists(mb)
+mb <- loadInfolist(mb,system.file("infolists/PlantDataset.csv",
+                                  package = "RMassBankData"))
+## Step
+mb <- mbWorkflow(mb, steps=1:8)
+@
+
 \subsection{peaklist-workflow}
 
-The peaklist-workflow works akin to the normal mzR-workflow with the only difference being, that the supplied data has to be in .csv format and contain 2 columns: "mz" and "int".
+The peaklist-workflow works akin to the normal mzR-workflow with the only difference being that the supplied data has to be in .csv format and contain 2 columns: "mz" and "int".
 You can look at an example file in the RMassBankData-package in spectra.Glucolesquerellin. Please note that the naming of the csv has to be similar to the mzdata-files, with the only difference being the filename extension.
 The readMethod name for this is "peaklist"
 
@@ -156,19 +170,7 @@ msmsPeaklist <- msmsWorkflow(msmsPeaklist, steps=1:8,
                                      mode="mH", readMethod="peaklist")
 @
 
-\subsection{Export the records}
-
-This section is just to debug the record creation with XCMS, and hence very terse.
-
 
-<<>>=
-mb <- newMbWorkspace(msmsList)
-mb <- resetInfolists(mb)
-mb <- loadInfolist(mb,system.file("infolists/PlantDataset.csv",
-                                  package = "RMassBankData"))
-## Step
-mb <- mbWorkflow(mb, steps=1:8)
-@