Building spectral library from a mixture of chemical standards

Introduction

This vignette describes how to generate a spectral library with chromatographic (retention time) and MS/MS information.LC-MS/MS files and metadata for library generation be downloaded at: https://zenodo.org/record/7506099#.Y7bJKXbMJaQ

The test dataset was provided by Janssen Pharmaceutica. The name, formula, SMILES and precursor mass/charge (PEPMASS) of the test compounds can be found in the metadata (Metadata_Library.txt). Data were acquired in positive ion mode (ESI-POS) using either data-dependent acquisition (DDA) or targeted MS/MS. Raw files in profile mode were converted into centroid mode mzML or mzXML files using MSConvertGUI (https://proteowizard.sourceforge.io/). Please also refer to following websites for more details about conversion of vendor raw data to mz(x)ML format: https://ccms-ucsd.github.io/GNPSDocumentation/fileconversion/.

Before starting

1. Create a folder and download the converted files (mzML or mzXML) and the metadata (Metadata_Library.txt). Make sure the metadata should contain at least the ID (a unique identifier) and PEPMASS (precursor mass) of compounds you would like to extract spectral records for in order to be included in the spectral library. FILENAME (in which experiment you expect the compound to occur) and RT (retention time) are recommended.
2. Create a new R script and set the working directory.
3. Load MergeION package

library(MergeION)

## Warning in fun(libname, pkgname): mzR has been built against a different Rcpp version (1.0.9)
## than is installed on your system (1.0.10). This might lead to errors
## when loading mzR. If you encounter such issues, please send a report,
## including the output of sessionInfo() to the Bioc support forum at 
## https://support.bioconductor.org/. For details see also
## https://github.com/sneumann/mzR/wiki/mzR-Rcpp-compiler-linker-issue.

Building the first spectral library

We can now start building a spectral library from 4 reference standard mixtures (one file per mixture). We know in total there are 36 compounds in these mixtures:

input_library = NULL #A brand-new spectral database will be created

#Check if the data files are in the working directory

list.files(pattern = ".mzXML")

## [1] "FD_terfenadine_190218_MAS011_02.mzXML"
## [2] "FD_terfenadine_190218_MAS011_03.mzXML"
## [3] "FD_terfenadine_190218_MAS011_04.mzXML"
## [4] "FD_terfenadine_190218_MAS011_05.mzXML"
## [5] "Mixture_36_POS.mzXML"                 
## [6] "Urine_05_Top10_POS.mzXML"             
## [7] "Urine_19_Top10_POS.mzXML"             
## [8] "Urine_37_Top10_POS.mzXML"

list.files(pattern = ".mzML")

## [1] "Compound_1_POS.mzML" "Compound_2_POS.mzML" "Compound_3_POS.mzML"

#Read the files

lcms_files = c("Mixture_36_POS.mzXML", 
               "Compound_1_POS.mzML",
               "Compound_2_POS.mzML",
               "Compound_3_POS.mzML")

#Read the metadata
metadata_file = "Metadata_Library.txt"

#Select the processing parameters as default
processing.algorithm = "Default"

# These parameters should reflect the mass accuracy of the instrument and the retention time deviation for the chromatographic system. 
params.search = list(mz_search = 0.01, ppm_search = 10, rt_search = 10, rt_gap = 30) # Units in Da, ppm, sec and sec

#  All MS/MS scans extracted are normalized to the highest peak, only top 200 most intense peaks are kept. An intensity baseline of 25000 was applied - here it reflects the noise level of Orbitrap instruments
params.ms.preprocessing = list(normalized = TRUE, baseline = 25000, relative = 0, max_peaks = 200, recalibration = 0)

# MS/MS scans of the same MS1 feature were extracted from different files,they are now combined to generate an fragment-rich consensus spectrum. Setting "consensus_method = consensus" kept fragments detected in all spectral records. A consensus window is applied to merge product ions with similar m/z values. 
params.consensus = list(consensus = TRUE, consensus_method = "consensus", consensus_window = 0.01)

# We can now run target feature search and consensus spectrum generation:

Library_POS  = library_generator(input_library, lcms_files, metadata_file,
polarity = "Positive", mslevel = c(1,2), add.adduct = FALSE,
processing.algorithm = processing.algorithm, params.search = params.search,     params.ms.preprocessing = params.ms.preprocessing, params.consensus = params.consensus)

## [1] 1
## [1] "Processing MS2 scans of data file ..."
## [1] 1
## [1] "Processing MS1 scans of data file ..."
## [1] 2
## [1] "Processing MS2 scans of data file ..."
## [1] 2
## [1] "Processing MS1 scans of data file ..."
## [1] 3
## [1] "Processing MS2 scans of data file ..."
## [1] 3
## [1] "Processing MS1 scans of data file ..."
## [1] 4
## [1] "Processing MS2 scans of data file ..."
## [1] 4
## [1] "Processing MS1 scans of data file ..."

## Generating consensus library...

This mensage should be ignored: Error in x\(.self\)finalize() : attempt to apply non-function

# This function generates a report from the created library
library_reporter(Library_POS)

## The entire library contains:32 MS1 scans and 46 MS2 scans of 25 compounds.
## 24 compounds possess both MS1 and MS2 scans.
## 25 compounds hold MS2 scans.
## 
## The consensus library contains:24 MS1 scans and 25 MS2 scans of 25 compounds.
## 24 compounds possess both MS1 and MS2 scans.
## 25 compounds hold MS2 scans.
## 
## The molecular network contains:25  nodes and   edges

# Writing the library
#The complete or consensus library can be exported as open source formats like msp or mgf
library_writer(Library_POS, con= "output_Library_POS.mgf", type="complete")

Merging with a public library

If you download other open source libraries from MassBank or GNPS, they can be merged with your local one. We have pre-compiled a combine MassBank and GNPS at: https://zenodo.org/record/7057435#.Y7atSnbMI2w. Please download the “GNPS_MASSBANK_PROCESSED_POS_CONSENSUS1.RData” file and put in the same working directory as the mgf/msp file:

load("GNPS_MASSBANK_PROCESSED_POS_CONSENSUS1.RData")

# Merging library with public ones:

Combined_Lib = library_combiner(library1c, Library_POS)

# Alternatively, you could merge the two files directly:

#Combined_Lib = library_combiner("GNPS_MASSBANK_PROCESSED_POS_CONSENSUS1.RData",  "output_Library_POS.mgf")

Let’s check now how many compounds in public library and in the updated one.

library_reporter(library1c) # 11642 compounds in library$complete

## The entire library contains:0 MS1 scans and 146247 MS2 scans of 11642 compounds.
## 0 compounds possess both MS1 and MS2 scans.
## 11642 compounds hold MS2 scans.
## 
## The consensus library contains:0 MS1 scans and 11642 MS2 scans of 11642 compounds.
## 0 compounds possess both MS1 and MS2 scans.
## 11642 compounds hold MS2 scans.
## 
## The molecular network contains:11642  nodes and   edges

library_reporter(Combined_Lib) # 11667 compounds in library$complete

## The entire library contains:32 MS1 scans and 146293 MS2 scans of 11667 compounds.
## 24 compounds possess both MS1 and MS2 scans.
## 11667 compounds hold MS2 scans.
## 
## The consensus library contains:24 MS1 scans and 11667 MS2 scans of 11667 compounds.
## 24 compounds possess both MS1 and MS2 scans.
## 11667 compounds hold MS2 scans.
## 
## The molecular network contains:  nodes and   edges

We have added 25 new compounds into the public library! That’s not exciting? Now we can convert the combined library to mgf format and export to your working directory:

library_writer(Combined_Lib, "Combined_Lib.mgf")

Building the library with RMassBank algorithm

We have included the RMassBank algorithm for library building (https://www.bioconductor.org/packages/release/bioc/html/RMassBank.html). You only need to change 1-2 parameters in library_generator function:

#See example below:
input_library = NULL #A brand-new spectral database will be created

#Change the processing algorithm to RMassBank
processing.algorithm = "RMassBank"

#Recalibration = 1 to show the output as the experimental mass along with annotated formula.
params.ms.preprocessing = list(normalized = TRUE, baseline = 25000, relative = 0, max_peaks = 200, recalibration = 1)

# Library generation
Library_POS_RMassBank  = library_generator(input_library, lcms_files, metadata_file,
          polarity = "Positive", mslevel = c(1,2), add.adduct = FALSE, 
          processing.algorithm = processing.algorithm, 
          params.search = params.search, 
          params.ms.preprocessing = params.ms.preprocessing, 
          params.consensus = params.consensus)

## Loading required package: Rcpp

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## msmsWorkflow: Step 1. Acquire all MSMS spectra from files

## msmsWorkflow: Step 2. First analysis pre recalibration

## msmsWorkflow: Step 3. Aggregate all spectra

## msmsWorkflow: Step 4. Recalibrate m/z values in raw spectra

## msmsWorkflow: Step 5. Reanalyze recalibrated spectra

## msmsWorkflow: Step 6. Aggregate recalibrated results

## msmsWorkflow: Done.

## Generating consensus library...

Let’s check how many compounds in RMassBank processed library, it should be very similar to the library by Default algorithm:

library_reporter(Library_POS_RMassBank) 

## The entire library contains:21 MS1 scans and 21 MS2 scans of 21 compounds.
## 21 compounds possess both MS1 and MS2 scans.
## 21 compounds hold MS2 scans.
## 
## The consensus library contains:21 MS1 scans and 21 MS2 scans of 21 compounds.
## 21 compounds possess both MS1 and MS2 scans.
## 21 compounds hold MS2 scans.
## 
## The molecular network contains:21  nodes and   edges

library_reporter(Library_POS)

## The entire library contains:32 MS1 scans and 46 MS2 scans of 25 compounds.
## 24 compounds possess both MS1 and MS2 scans.
## 25 compounds hold MS2 scans.
## 
## The consensus library contains:24 MS1 scans and 25 MS2 scans of 25 compounds.
## 24 compounds possess both MS1 and MS2 scans.
## 25 compounds hold MS2 scans.
## 
## The molecular network contains:25  nodes and   edges

You could now display the elemental formula of product ions annotated by RMassBank:

# Metadata of the compound:
print(Library_POS_RMassBank$complete$metadata[5,]) 

##    PEPMASS   RT  IONMODE ADDUCT CHARGE ID                       SMILES  FORMULA
## 7 176.0706 4.28 Positive    M+H      1  4 C1=CC=C2C(=C1)C(=CN2)CC(=O)O C10H9NO2
##               FILENAME INSTRUMENT DATACOLLECTOR SAMPLEID  ORGANISM SUBMITUSER
## 7 Mixture_36_POS.mzXML Q Exactive        Thermo     MIX2 Standards      Adrem
##              COMPOUND CAS Name MSLEVEL      TIC  PEPMASS_DEV SCAN_NUMBER
## 7 3-Indoleacetic acid N/A  N/A       2 579418.3 -0.005792615        1464
##   PARAM_SUBMIT_USER PARAM_SAMPLE_TYPE PARAM_COMMENTS PARAM_ALGORITHM
## 7                                                          RMassBank
##   PARAM_CREATION_TIME SCANS
## 7 2023-06-11 13:37:15     5

# Its MS/MS spectra with elemental formula annotation by RMassBank:
print(Library_POS_RMassBank$complete$sp[[5]])

##                                 
## 1  87.04414   0.1686453   C4H7O2
## 2 103.05424   1.7280715     C8H7
## 3 116.07066   0.2765680 C5H10NO2
## 4 120.08075   0.3170023   C8H10N
## 5 130.06508 100.0000000    C9H8N
## 6 148.07588   0.2503935  C9H10NO

The end