Structural Characterization of Acidic Compounds in Pyrolysis Liquids

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Structural Characterization of Acidic Compounds in Pyrolysis Liquids Using Collision-Induced Dissociation and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Jan Zuber, Philipp Henry Rathsack, and Matthias Otto Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b02873 • Publication Date (Web): 03 Oct 2018 Downloaded from http://pubs.acs.org on October 7, 2018

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Analytical Chemistry

Structural Characterization of Acidic Compounds in Pyrolysis Liquids Using Collision-Induced Dissociation and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Jan Zuber,

∗,†

†,‡

Philipp Rathsack,

and Matthias Otto

†

†Institute of Analytical Chemistry, TU Bergakademie Freiberg, Leipziger Strasse 29, 09599

Freiberg, Germany ‡German Centre for Energy Resources, Reiche Zeche, Fuchsmuehlenweg 9, 09599 Freiberg,

Germany E-mail: [email protected]

Phone: +49 (0)3731 39 4193

1

Abstract

2

In this study, a novel approach to characterize and identify acidic oil compounds uti-

3

lizing the fragmentational behaviour of their corresponding precursor ions is presented.

4

Precursor ions of seven analyzed pyrolysis oils that were generated from pyrolysis educts

5

of dierent origin and degree of coalication were produced by electrospray ionization

6

in negative ion mode (ESI(−)). Following a fragmentation of all ions in the ion cloud by

7

collision-induced dissociation (CID), the precursor and product ions were subsequently

1

ACS Paragon Plus Environment


8

detected by ultra-high resolving Fourier transform ion cyclotron resonance mass spec-

9

trometry (FT-ICR-MS). The ESI(−)-CID data sets were evaluated by applying either

10

a targeted classication or untargeted clustering approach. In case of the targeted clas-

11

sication, 10 % of the ionized precursor ions of the analyzed pyrolysis liquid samples

12

could be classied into one of eleven compound classes utilizing theoretical fragmenta-

13

tion pathways of these classes. In contrast, theoretical fragmentation pathways were

14

not necessary for the untargeted clustering approach, making it the more transmittable

15

method. Results from both approaches were veried by analyzing standard compounds

16

of known structure. The analysis and data evaluation methods presented in this work

17

can be used to characterize complex organic mixtures, such as pyrolysis oils, and their

18

compounds in-depth on a structural level. Intensity

m/z

m/z

m/z

m/z

Collision energy

Untargeted clustering

Targeted classication

DBE

O2, DBE = 1, EE Saturated monocarboxylic acids

180

20

160

18

16

140

- H2O

14

120

O1, DBE = 2, EE Product ion 1

- CO2

Hierarchical cluster

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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- C3H6 - H2

- CO

12

100 10

80 8

60 6

40

Product ion 2

Product ion 3

Product ion 4

CcHh, DBE = 2, EE

CcHh, DBE = 0, EE

O2, DBE = 2, EE

4

20

0 100

2

0

200

300

400

500

m/z [Da]

2


600

700

800

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19


Introduction

20

Pyrolysis liquids that are produced from coal 1 or other organic materials, for instance,

21

biomass, 24 are an interesting alternative to overcome the industrial dependence on nat-

22

ural oil and gas. Furthermore, the pyrolysis process presents a possibility to recycle carbon

23

wastes, such as scrap tyres 5,6 or plastic. 7 In general, during the pyrolysis process, macro-

24

molecular compounds of the introduced organic material are thermally decomposed into

25

smaller molecules in an oxygen-free atmosphere by cracking, elimination, isomerisation and

26

rearrangement reactions. 2,4,8 Due to the great variety of chemical reactions, ultra-complex

27

product mixtures result, 4,9 which can only be analyzed comprehensively by ultra-high resolv-

28

ing analysis techniques, such as Fourier transform ion cyclotron resonance mass spectrometry

29

(FT-ICR-MS). In general, FT-ICR-MS resolves the ionized analytes according to their indi-

30

vidual cyclotron frequencies, which are inversely related to the mass-to-charge-ratios (m/z )

31

of the ions. 1012 Depending on the applied ionization technique and ionization mode, com-

32

pounds of all polarities can be analyzed. For example, electrospray ionization (ESI) provides

33

a selective ionization for mainly polar species of petroleum related liquids. 1325

34

Nonetheless, an ESI FT-ICR-MS analysis of a complex organic mixture usually only gives

35

an overview over the dominating compound and heteroatomic classes in a multi-component

36

sample. The derivation of structural information on a single compound level is only possible

37

if tandem mass spectrometry is applied. 26 Usually, in a tandem MS expriment, a compound

38

can only be structurally characterized if its corresponding ion is isolated from the overall

39

amount of ions. Furthermore, time-consuming multiple tandem MS steps (MSn ) are often

40

necessary to identify the general structure of a molecule. If a single tandem MS event is

41

sucient, the MS/MS analysis can also be realized by hyphenating the mass spectrome-

42

ter to a chromatographic separation technique, such as liquid chromatography (LC) 2733 or

43

gas chromatography (GC). 1,29,3438 The resulting data sets of this analytical combination

44

can result in very large data sets that may be hard to process and evaluate, especially if a

45

high-resolving mass spectrometer, such as a FT-ICR-MS, is used. 39 Hence, a simple and fast 3


Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

46

analysis and data evaluation method to characterize the structure of single compounds or at

47

least structural subunits and functional groups, would be of great interest to overcome the

48

previously mentioned problems.

49

The aim of this study is the presentation of the development process of such a preparation,

50

analysis and data evaluation method and its application to seven pyrolysis liquids that were

51

produced from dierent organic materials with a varying degree of coalication. Based on

52

theoretical fragmentation pathways, structurally similar compounds in the analyzed sam-

53

ples could be assigned to one of eleven compound classes (saturated monocarboxylic acids,

54

unsaturated monocarboxylic acids, aromatic acids/ketones/aldehydes, oxocarboxylic acids,

55

saturated dicarboxylic acids, hydroxyphenylaldehydes and -ketones, dihydroxyphenylalde-

56

hydes, sulnic acids, sulfonic acids, sulfobenzoic acids and aromatic disulfonic acids), using

57

a targeted classication approach. Furthermore, an untargeted clustering approach was

58

applied that enabled an identication of compounds without any information about their

59

prior fragmentational behaviour. Tandem MS experiments for this paper were performed

60

by transferring the ionized analytes to a collision cell, where a collision-induced dissociation

61

(CID) of all ions was conducted. In general, in a CID process, precursor ions are fragmented

62

into product ions and neutral losses (e.g. H2O or CO2) by a collision event with neutral gas

63

molecules (e.g. N2 or Ar). Thus, the exact structures of the precursor ions can be recon-

64

structed by comprehending the fragmentation mechanism with the help of the detected CID

65

mass spectra. 4044 In a previous paper, we were able to successfully apply ESI(−) CID-FT-

66

ICR-MS to characterize the structural building blocks of a pyrolysis liquid produced from

67

a german brown coal. 45 The chemometric approaches presented in this scientic work are,

68

thus, a progression of the methods used in the previous work.

4


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69


Experimental Section

70

Sample preparation

71

The pyrolysis liquid samples were produced as reported in prior works 24,25,39,45,46 using seven

72

dierent feedstocks. An overview of the produced pyrolysis liquids that were analyzed for

73

this paper can be found in Table 1. Further information on the proximate and ultimate

74

analyses of the seven pyrolysis educts are given in section S1 of the Supporting Information

75

to this paper. Table 1: Pyrolysis liquids analyzed for this study.

Feedstock

Origin

Wheat straw pellets Brown coal Brown coal Brown coal Brown coal Hard coal Anthracite

Germany (location unknown) Hambach, Germany Schleenhain, Germany Schöningen, Germany V°esová, Czech Republic Poland (location unknown) Ibbenbühren, Germany

Pyrolysis Abbreviation temperature [◦ C]

800 800 800 500 500 800 800

WSTP BCHA BCSL BCSO BCCZ HCPO ANIB

76

The sample standard solutions as well as the solutions used for the ESI(−) CID-FT-ICR-MS

77

analyses of the pyrolysis oils were prepared according to previously published routines. 45

78

Chemometric results of the seven analyzed pyrolysis oils were veried by analyzing nine

79

analytical standard compounds, namely cerotic acid, montanic acid, melissic acid (satu-

80

rated monocarboxylic acids), α-linolenic acid, linoleic acid, oleic acid (unsaturated monocar-

81

boxylic acids), docosanedioic acid (saturated dicarboxylic acids), 4-octylbenzoic acid (aro-

82

matic acids/ketones/aldehydes) and 4-dodecylbenzenesulfonic acid (sulfonic acids), using the

83

same ESI(−)-CID preparation, analysis and data evaluation methods, which were utilized

84

for the analysis of the oil samples. The analytical standard compounds were analyzed us-

85

ing standard concentrations of 1 - 25 µmol/L (1 µmol/L: saturated monocarboxylic acids;

86

2 µmol/L: unsaturated monocarboxylic acids, saturated dicarboxylic acids and sulfonic acids;

87

25 µmol/L: aromatic acids/ketones/aldehydes). 5



88

Mass spectrometry (FT-ICR-MS)

89

MS experiments were conducted on a 15 T solariX FT-ICR-MS from Bruker Daltonics,

90

equipped with an ESI source, which was operated in negative ion mode, using a scan range

91

from 46.06 to 1000.00 Da, an ion accumulation time of 100 ms and a resolving power of R

92

= 800,000 (m/z = 400 Da). Resulting data sets had a size of 8 M. Furthermore, basic ESI

93

settings were chosen as follows: capillary voltage +2,800 V, end-plate-oset -500 V, nebu-

94

lizer gas pressure 1.0 bar, dry gas ow 4.0 L/min, dry gas temperature 300 ◦ C and Q1 mass

95

70 Da. Sample solutions were introduced with 10 µL/min using the source-integrated syringe

96

pump. During tandem MS analysis of a pyrolysis liquid sample, the collision energy (CE )

97

in the collision cell was varied from 0 - 100 eV in 5 eV steps, while accumulating 64 single

98

mass spectra each. In general, CE is a MS instrument parameter and represents a potential

99

that is applied to accelerate the ions in the collision cell. Every CID-MS experiment was

100

performed in triplicate at each CE.

101

Calibration of the FT-ICR-MS experiments was conducted in a two step process. In a rst

102

step, a spiked sample was measured and internally calibrated, using the ESI-L Low Concen-

103

tration Tuning Mix (Agilent) and its known masses. From these mass spectra, molecular

104

formulae were calculated using Bruker Daltonics software DataAnalysis 5.0 (SR1) and the

105

resulting molecular formula lists were used to generate an internal calibration list for all

106

analyzed pyrolysis liquid samples. This calibration list was applied to calibrate the mass

107

spectra of the unspiked samples, which resulted in a mean standard deviation between the

108

observed and calculated m/z of less than 0.1 ppm. For further information on the applied

109

calibration lists, see section S2 in the Supporting Information to this paper.

110

Molecular formula assignment was only applied to peaks with a signal-to-noise-ratio (s/n )

111

≥ 10. Furthermore, the deviation from the theoretical mass should not exceed 0.5 ppm and

112

should show a composition within the element numbers set (Cc Hh Nn Oo Ss : c = unlimited,

113

h = unlimited, 0 ≤ n ≤ 3, o = unlimited, 0 ≤ s ≤ 5). Resulting molecular formula lists

114

were exported and imported into MATLAB 2017a (Mathworks) and processed further using 6


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115

in-house scripts for molecular formula ltering and blank correction. Filtering of the molec-

116

ular formula lists was conducted applying the rules established by Herzsprung 47,48 (double

117

bond equivalent (DBE ) ≥ 0, 0.3 ≤ H/C ≤ 2.5, O/C ≤ 1.0, N/C ≤ 1.0, S/C ≤ 1.0). Fur-

118

thermore, also the peak lists (all signals with s/n ≥ 5) for each experiment were exported

119

from DataAnalysis and imported into MATLAB 2017a. Each molecular formula and peak

120

list was blank corrected in a further step by excluding all peaks and molecular formulae that

121

were assigned to data sets obtained by analyzing the pure solvent at each CE. The targeted

122

classication and untargeted clustering of the precursor ions based on their fragmentation

123

patterns were performed using these preprocessed molecular formula and peak lists.

7



124

125

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Data Analysis Targeted classication O2, DBE ≥ 2, EE Unsaturated monocarboxylic acids - H2O

Product ion 1

- CO2 Product ion 2

- H2

- C6H6

Product ion 3

Product ion 4

- C5H6O2 Product ion 5

- C8H8O2 Product ion 6

O2, DBE ≥ 0, CcHh, DBE ≥ 0, CcHh, DBE ≥ 0, EE EE EE ≥ 4 ≥ 5 ≥3 double bonds double bonds double bonds

O1, DBE ≥ 3, CcHh, DBE ≥ 1, O2, DBE ≥ 3, EE EE EE = 3 =0-6 ≥3 double bonds double bonds double bonds

Figure 1: Theoretical fragmentation tree for the compound class of unsaturated monocarboxylic acids. Precursor ions, product ions or fragmentation pathways that are illustrated in red could be veried by the analysis of standard compounds. Precursor ions or product ions, which are illustrated in blue, as well as pathways that are illustrated in black are parts of the fragmentation tree that are only described in literature 33,49,50 and which could not be veried by our analyses. 126

The targeted classication approach was based on fragmentation pathways for eleven com-

127

pound classes that were reported by other research groups. For this purpose, only papers

128

were reviewed where a soft ionization (e.g. ESI or fast atom bombardment (FAB)) and

129

CID as tandem MS method were applied. With the help of this literature research, ex-

130

pectable fragmentation trees for saturated monocarboxylic acids, 33,5153 unsaturated mono-

131

carboxylic acids, 33,49,50 aromatic acids/ketones/aldehydes, 31 oxocarboxylic acids, 54 saturated

132

dicarboxylic acids, 55 hydroxyphenylaldehydes and -ketones, 56,57 dihydroxyphenylaldehydes, 57

133

sulnic acids, 58 sulfonic acids, 5861 sulfobenzoic acids 59 and aromatic disulfonic acids 59 could

134

be constructed. For instance, Figure 1 summarizes the theoretical pathways for the com-

135

pound class of unsaturated monocarboxylic acids. Fragmentation pathways, which we could

136

verify by analyzing the standard compounds α-linolenic acid, linoleic acid and oleic acid,

137

are illustrated in red, whereas black pathways correspond to pathways only reported in the 8


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138

literature. The theoretical fragmentation trees of the other ten classiable compound classes

139

are given in section S3 of the Supporting Information.

140

Both, the targeted classication and untargeted clustering of the precursor ions, were per-

141

formed using a newly developed in-house MATLAB program (ClassicationTool ), which is

142

given as Supporting Material to this paper. In case of the targeted classication, in a rst

143

step, all detected ions in each CID experiment (CE variation) with their individual m/z and

144

molecular formula were summarized in a comprehensive matrix and sorted according to their

145

m/z. Furthermore, a separate matrix was created that contained the relative intensity of

146

each peak and, thus, m/z in every mass spectrum at each CE. For classication purposes, all

147

molecular ions were evaluated to nd matching precursor ion - product ion pairs. Based on

148

observable neutral losses that induced changes of the m/z, molecular formula, DBE and elec-

149

tron conguration (even number of electrons (EE) or odd number of electrons (OE)) of the

150

precursor ions, possibly resulting product ions were searched in the comprehensive matrix.

151

If a potential product ion of a precursor ion was found, the CE 's of the relative intensity

152

maximum of both ions were compared. This procedure is based on our assumption that

153

the intensity maximum of a precursor ion should occur at lower CE 's than the maximum of

154

a product ion, due to the degradation of the precursor ion and the ongoing production of

155

the product ion with increasing CE. If an appropriate intensity-based relationship between

156

the precursor and the product ion was observed, both ions were placed in the result matrix.

157

In contrast, if the relative intensity maximum of the product ion was observed at equal or

158

lower CE 's than the maximum of the potential precursor ion, both ions were excluded from

159

the result matrix. In case of a fragmentation, which involved multiple neutral losses, for

160

example a H2O and CO loss (saturated monocarboxylic acids), the CE at which the relative

161

intensity maximum of the product ion after both losses was observed had to be lower than

162

the intensity maximum CE of the intermediate product ion, which was exclusively formed

163

by the rst (e.g. H2O) loss. In the process of relating precursor and product ions multiple

164

assignments of both ion types occurred. These ions were agged as inconclusive assigned 9



165

ions, placed in a separate part of the result matrix and excluded from further statistical

166

evaluation. A precursor ion was considered as correctly classied if at least one unique cor-

167

responding product ion could be found. For further information on the detailed structure of

168

the result matrix, the reader is referred to section S4 of the Supporting Information.

169


170

Beside the targeted classication approach, the fragmentational behaviour of the precur-

171

sor ions was also utilized for an untargeted clustering. By applying this approach, ionized

172

molecules can be structurally characterized without the knowledge of their fragmentation

173

pathways. Thus, this method allows the comparison of data evaluation results based on

174

analyses obtained by dierent ionization techniques, ionization modes or tandem MS meth-

175

ods. A owchart of the developed untargeted clustering approach that was used for the data

176

processing of the ESI(−)-CID data sets of the seven analyzed pyrolysis oils is illustrated in

177

Figure 2. Possible neutral losses

Precursor

Precursor

S U B T R A C T I O N

m/z C H N O S 1 0 1 0 0 0 2 0

2 0 0

0

15 1

Binary matrix

Scoring matrix Neutral loss

Neutral loss

Loss H

0

1 0 0 0

H2

0

0

0

1 0

1

0.0 6.6 0.00.00.0 8.2 0.0 3.4

1 1 0 0 1

0

0.0 0.0 6.2 8.10.00.0 5.30.0

3 0 0

0

16 0

0 0 1

0

O

18 0

2 0 1

0

H2O

41 2

3 1 0

0 C2H3N1

44 1

0 0 2

0

CO2

0

0 0 0 1

44 3

8 0 0

0 C3H8

0

0

CH3

1 0

1 0 0 1 0

1 0 0 0 0

0

Precursor ion

Entirety of all m/z m/z C H N O S DBE e-

Precursor ion

1 1

0 0

1 1 0 0 1

1 0 0 0 0 1

0

0 1

0.0 7.1 0.0 5.5 0.0 0.0 4.2 0.0 7.3 0.0 0.00.0 6.18.0 0.00.0 0.0 0.0 6.4 8.10.00.0 5.4 0.0 4.9 0.0 0.00.00.0 3.10.0 7.7

0 0

1

0.0 0.00.00.0 5.10.00.0 7.0

1 1 0 0 1

0

0.0 0.0 6.3 8.10.00.0 5.4 0.0

Dendrogram

Diagram of m/z vs. cluster number 100 90

RESULT MATRICES

GENERATION OF SUBTRACTION MATRICES

C L U S T E R I N G

TRANSFORMATION INTO RESULT MATRICES

80 70

Hierarchichal cluster

INITIAL MATRICES

60 50 40 30 20 10

SCORING

140

120

100

80

60

40

20

0 100

200

300

Features

400

500

m/z [Da]

Distance

VISUALISED RESULTS OF THE CLUSTER ANALYSIS

Score

Score

or

- Check possible neutral losses matrix - m/z neutral loss vs. m/z precursor ion - Pure C- or N-clusters - Intensity product ion vs. precursor ion - Rel. intensity precursor - Heteroatom to C ratio

SUBTRACTION MATRICES

160

FILTERING

rs

m/z C H N O S DBE e

-

Pr ec u

Possible neutral losses of each precursor ion

Neutral losses

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 10 of 31

ENTIRETY OF FILTERED SUBTRACTION MATRICES

Figure 2: Flowchart of the untargeted clustering of precursor ions based on their fragmentational behaviour. The applied scoring mechanism was inspired by previous works from Rasche, Böcker 6266 and Kind. 67

10


600

700

800

900

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178

Using the newly developed MATLAB program, in a rst step of the untargeted clustering

179

approach, all pre-processed peak lists and molecular formula lists at each CE were sum-

180

marized to median peak and molecular formula lists, containing only peaks and molecular

181

formulae observable in all three analyses. In a following step, comprehensive peak list and

182

molecular formula list matrices for all median lists were created, as well as matrices only

183

comprising the lists at CE = 0 eV, which contained all potential precursor ions. Furthermore,

184

a list of possible neutral losses for tandem MS experiments was used. This list was imported

185

as a separate matrix and was created based on previous research by Rasche and Böcker

186

on computing fragmentation trees. 6266 The list of possible neutral losses can be found in

187

section S5 of the Supporting Information to this paper. During the process of untargeted

188

clustering, only those neutral losses were assumed as possible that were a linear combination

189

of maximum three single losses.

190

In a second step, subtraction matrices for each precursor ion were constructed. This was

191

possible by determining the position of the precursor ion in the matrices, comprising all ions

192

detected throughout the ESI(−)-CID analysis of a sample. Beginning at this position, the

193

m/z and molecular formula dierence to all ions with a lower m/z in the comprehensive

194

matrix were calculated. Hence, an individual subtraction matrix for each precursor ion re-

195

sulted. Dierence entries with a negative C, H, N, O or S number were excluded from these

196

matrices. The evaluation of the subtraction matrices could be subsequently performed in two

197

ways using the MATLAB program. Utilizing the rst approach, all product ions and their

198

corresponding neutral losses were checked against the list of possible neutral losses, excluding

199

all product ions that could not be related to a neutral loss from the list or a linear combi-

200

nation of these losses. These ltered subtraction matrices were subsequently transformed to

201

the result matrix. The number of rows in this matrix corresponded to the overall number

202

of precursor ions, the number of columns to the number of possible neutral losses and their

203

linear combinations. If a possible neutral loss could be logically related to a precursor ion,

204

a 1 was placed at the corresponding position in the result matrix. In contrast, if a neutral 11



205

loss could not be logically related to a precursor ion, a 0 was placed in the matrix. Thus, a

206

binary result matrix was constructed by this procedure.

207

For the second approach, every entry in the subtraction matrices was evaluated based on a

208

scoring mechanism, inspired by the work of Rasche, Böcker and Kind. 6267 For every prod-

209

uct ion a score was calculated by applying strict rules. An exact description of this scoring

210

mechanism is given in section S6 of the Supporting Information. The result matrix of this

211

approach was constructed similar to the binary matrix. The number of rows in this matrix

212

also corresponded to the overall number of precursor ions, whereas, the number of columns

213

corresponded to the overall number of all found neutral losses. If a possible neutral loss could

214

be related in any way to a precursor ion, the corresponding score for this fragmentation event

215

was placed at the corresponding position in the scoring result matrix, hence, distinguishing

216

in-silico between likely and unlikely fragmentation pathways.

217

In a further step of the untargeted clustering of the precursor ions, the binary or scoring

218

result matrix was used for a hierarchical or a k-means cluster analysis. To determine the

219

number of necessary clusters for both analyses, the number of occupied heteroatomic classes

220

multiplied by two was utilized. For instance, if only precursor ions assigned to heteroatomic

221

classes O1, O2, O3 and O4 could be observed, a cluster number of eight would result due to

222

the four occupied classes. This factor was optimised during the development of this approach

223

using the results of the ESI(−)-CID analyses of the standard compounds. Utilizing a factor

224

of two, a correct clustering of standard compounds, belonging to the same compound class,

225

into the same cluster could be observed.

226

Finally, the results of the cluster analyses were bundled by the MATLAB program to a clus-

227

ter result matrix where each precursor ion with its m/z, molecular formula, DBE, electron

228

conguration, hierarchical cluster number and k-means cluster number was listed. The result

229

matrices for all analyzed pyrolysis oils are given as Supporting Material to this paper. In this

230

work, only results of the hierarchical clustering are presented to demonstrate the potential

231

of the untargeted clustering approach. 12


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232


Results and Discussion

233

Targeted classication

234

The targeted classication approach was applied to study the dierences of seven pyrolysis

235

liquids in terms of compounds, which could be allocated to the eleven compound classes (satu-

236

rated monocarboxylic acids, unsaturated monocarboxylic acids, aromatic acids/ketones/aldehydes,

237

oxocarboxylic acids, saturated dicarboxylic acids, hydroxyphenylaldehydes and -ketones, di-

238

hydroxyphenylaldehydes, sulnic acids, sulfonic acids, sulfobenzoic acids and aromatic disul-

239

fonic acids), using the class specic fragmentational behaviour. In general, more than 10 %

240

of the precursor ions of the pyrolysis oils BCSL (10.6 %, on average), BCSO (10.4 %) and

241

ANIB (10.8 %) could be assigned to one of the eleven compound classes. In contrast, only

242

4.6 % of the precursor ions of WSTP could be classied. In absolute numbers, most precur-

243

sor ions could be classied for samples BCHA (239, on average), BCSL (246), BCSO (236)

244

and HCPO (226), whereas only 75 and 86 precursor ions of oil samples WSTP and ANIB

245

could be classied. An overview over the relative number of classied precursor ions for each

246

analyzed pyrolysis liquid is given in section S7 of the Supporting Information to this paper.

247

Figure 3 illustrates the total number of precursor ions of the seven pyrolysis oils assigned to

248

the eleven compound classes. Most precursor ions were assigned to the compound classes

249

of saturated and unsaturated monocarboxylic acids, as well as oxocarboxylic acids, for all

250

analyzed samples. Especially samples BCHA, BCSL, BCSO and, in parts, BCCZ exhibit

251

comparable numbers of assigned precursor ions to most compound classes. Most likely, this

252

observation can be explained by the comparable degree of coalication of the pyrolysis educts

253

of all four oil samples. Nonetheless, also dierences between these pyrolysis oils can be ob-

254

served using the targeted classication approach. For instance, a higher number of precursor

255

ions of sample BCSO were assigned to sulfur-containing compound classes, such as sulnic

256

acids or sulfobenzoic acids, compared to all other pyrolysis liquids produced from a brown

257

coal. Thus, the origin of the pyrolysis educt, the conditions during the coalication and 13



258

a slightly dierent degree of coalication are probably the essential factors leading to the

259

dierences discovered.

260

Furthermore, a higher number of precursor ions of sample HCPO were allocated to the

261

compound class of unsaturated monocarboxylic acids, compared to all other analyzed py-

262

rolysis liquids. Due to partially dierent pyrolysis temperatures during the production of

263

the pyrolysis liquids as well as increasing dehydration and condensation reactions during the

264

coalication process, a higher number of unsaturated and aromatic compounds is present in

265

sample HCPO, in comparison to the other six pyrolysis oils. Pyrolysis liquid ANIB should

266

also contain a high number of unsaturated and (polycyclic) aromatic compounds, which

267

we could not comprehend by the number of assigned precursor ions to the corresponding

268

compound classes. Probably, oil sample ANIB contains mostly non-acidic high-aromatic

269

compounds with a low amount of heteroatoms that are dicult to ionize by means of ESI.

270

Hence, the compound classes mainly comprised in this pyrolysis liquid are not veriable

271

by the developed ESI(−)-CID based targeted classication approach. For further informa-

272

tion on the exact number of assigned precursor ions to the eleven compound classes for all

273

analyzed pyrolysis oils, the reader is referred to section S8 of the Supporting Information. Total number of classied precursor ions

120 WSTP BCHA

100

BCSL BCSO BCCZ

80

HCPO ANIB

60

40

20

s ac id

id ac

ni

c

c oi nz

di

be om

at

ic

lfo

su lfo

ni Su

s

s

s id

ac id

ac

c

c ni l Su

ld yla

en

lfo Su

Ar

es

s

yd eh

to -k e d

an

ph

D

ih

yd

ld

ro

eh

xy

yd

ph

es

ca di t. Sa

yla en

ne

id

id

ac c yli

ox rb

rb

ke

ca xo

O

at

H

yd

ro

Ar

xy

om

U

s

s

s c yli ox

ne to

ac

id

ac id s/

ac

de

hy

de

s/

ar al ic

ns

at

t.

.m

m

on

on oc

oc

ar

bo

bo

xy

xy

lic

lic

ac id

s

s

0

Sa

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 31

Compound class

Figure 3: Number of precursor ions assigned to the eleven compound classes using the targeted classication approach. 14


Page 15 of 31

274

Semiquantiative statements on the relative amount of ionizable and classiable compounds

275

between all analyzed samples were possible by comparing the relative abundance of each

276

compound class for all analyzed pyrolysis oils. The relative abundance for one compound

277

class was calculated by summing up the relative intensities of all precursor ions assigned to

278

this compound class. Figure 4 illustrates the relative abundance of the eleven classiable

279

compound classes for each pyrolysis liquid. Comparative statements between all samples

280

analyzed should only be based on a compound class specic comparison and not, in general,

281

for all compound classes, due to the varying response factors of each compound class. Thus,

282

the ionization eciencies of dierent compound classes might vary signicantly, which makes

283

a comparison of the relative abundances of dierent compound classes almost impossible.

284

According to Figure 4, the highest relative amount of ionizable and classiable saturated

285

monocarboxylic acids can be observed for samples BCHA, BCSL, BCSO and ANIB. In

286

comparison, samples BCCZ and HCPO exhibit the highest relative amount of unsaturated

287

monocarboxylic and sulfonic acids. Furthermore, higher relative amounts of saturated di-

288

carboxylic acids were found for pyrolysis liquid samples BCHA, BCSL, BCSO and HCPO

289

in comparison to all other analyzed samples. 100

WSTP BCHA

Relative abundance [%]

BCSL BCSO BCCZ

1

HCPO ANIB

0.01

en

s id

id

id Ar

om

at

ic

di

su lfo

ni

c

c oi nz be

ac

ac

ac c ni lfo

Su

Su lfo

c ni Su l

s

s

s id

s

ac

de

es yla

ld e

hy

on

nd

en

ro

xy

ph

sa ih yd

H

yd

ro

Ar

xy

om

ph

D

ld e

hy

de

ca di t. Sa

yla

al ic

-k et

ac

rb

rb

ox

ox

yli

yli

c

c

ac

id

id

s

s

s id ac s/

ne to ke

ca xo

O

de

hy

de

s/

ar on oc

.m

at ns

at

Sa

t.

m on oc

ar

bo

bo

xy

xy

lic

lic

ac

ac

id

id

s

s

0.0001

U

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Compound class

Figure 4: Relative abundance of the eleven compound classes of the targeted classication approach. 15



290


291

The untargeted clustering approach enables the evaluation of tandem MS data sets of com-

292

plex organic mixtures without the necessity for knowing the fragmentation pathways of all

293

compounds present. Hence, this approach gives the opportunity to structurally characterize

294

a higher number of compounds, compared to the targeted classication approach.

295

By applying a hierarchical clustering to the binary and scoring result matrices, various ho-

296

mologous series could be identied in the pyrolysis oils. These homologous series can be

297

visualized by plotting the m/z of each precursor ion in dependence on its assigned clus-

298

ter number. Ideally, compounds belonging to the same homologous series were assigned to

299

the same cluster. Furthermore, each compound of the homologous series exhibited a sim-

300

ilar m/z dierence of 14.015650 Da, corresponding to a CH2 unit, to its homologues with

301

lower or higher alkyl chain length. Thus, the m/z -cluster number-plots can be read similar

302

to spectro chromatograms, usually obtained by LC- or GC-MS analyses. As an example,

303

the m/z -cluster number-plots obtained by the untargeted clustering of the precursor ions of

304

pyrolysis liquid BCSO using the binary and scoring result matrix are illustrated in Figure 5. 200

A: Binary result matrix

DBE

B: Scoring result matrix

DBE

20

200

180

18

180

18

160

16

160

16

140

14

140

14

120

12

120

12

100

10

100

10

80

8

80

8

60

6

60

6

40

4

40

4

20

2

20

2

0 200

0

400

600

800

1000



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 31

0 200

m/z [Da]

20

0

400

600

800

1000

m/z [Da]

Figure 5: m/z -cluster number-plots of sample BCSO obtained by using the binary (A) and scoring (B) result matrices for a hierarchical clustering. In total, the precursor ions of sample BCSO could be grouped into 198 hierarchical clusters. 16


Page 17 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


305

The assignment of analyzed standard compounds to clusters, containing precursor ions of

306

compounds of unknown structures, helped to identify these unknown compounds. For in-

307

stance, saturated monocarboxylic acids with an alkyl chain length from C24 to C50 could

308

be identied in the seven analyzed pyrolysis oils by this procedure. For the identication

309

of saturated monocarboxylic acids, only corresponding precursor ions that were allocated to

310

the same cluster as the ions of the standard compounds cerotic acid (C26H52O2), montanic

311

acid (C28H56O2) and melissic acid (C30H60O2) were considered correctly clustered. Utilizing

312

the binary result matrix for the hierarchical clustering, saturated monocarboxylic acids with

313

an alkyl chain length of C24 - C46 were identied. In contrast, if the scoring result matrix

314

was used for the clustering process, saturated monocarboxylic acids from C32 to C50 could be

315

identied. Thus, the usage of the scoring result matrix leads to a joint clustering of acids of

316

higher alkyl chain length in comparison to the usage of the binary result matrix. Nonethe-

317

less, the same saturated monocarboxylic acids were identied in an alkyl chain length region

318

from C32 to C46 for each pyrolysis oil, independent from the application of the binary or the

319

scoring result matrix for hierarchical clustering.

320

Figure 6 summarizes the identied saturated monocarboxylic acids for each analyzed pyroly-

321

sis liquid, combining the results from the hierarchical clustering using the binary and scoring

322

result matrix. As illustrated, the least amount of identied saturated monocarboxylic acids

323

can be observed for sample BCCZ. Applying the targeted classication approach, the lowest

324

number of classied saturated monocarboxylic acid precursor ions could be also observed

325

for this pyrolysis oil. Hence, the results from the untargeted clustering conrm these previ-

326

ous results. Furthermore, comparable amounts of saturated monocarboxylic acids with an

327

alkyl chain length from C24 to C44 could be identied for samples BCHA, BCSL and BCSO.

328

Most likely, the similar degree of coalication of the pyrolysis educts of these three oils is

329

responsible for this trend. Due to the highest number of identied compounds for these py-

330

rolysis liquids compared to all other samples, an extraction of compounds belonging to the

331

compound class of saturated monocarboxylic acids seems to be most economical if samples 17



332

BCHA, BCSL or BCSO are utilized, based on the results of the untargeted clustering.

333

The results of the untargeted clustering approach in terms of identied saturated mono-

334

carboxylic acids should visualize the general potential of this chemometric method. An

335

enumeration of all identied compounds, contained in the analyzed pyrolysis oils, is not in

336

the scope of this paper. For further information on the results of the untargeted clustering,

337

the reader is referred to the MATLAB result matrices, comprising all identied compounds

338

and assigned compound clusters, which are given as Supporting Material to this paper. An

339

overview of the identied monounsaturated monocarboxylic acids for all seven analyzed py-

340

rolysis oils, using the untargeted clustering approach, is furthermore presented in section S9

341

in the Supporting Information.

WSTP BCHA BCSL

Sample

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 31

BCSO BCCZ HCPO ANIB C25

C30

C35

C40

C45

C50

Alkyl chain length

Figure 6: Identied saturated monocarboxylic acids, utilizing the untargeted clustering approach. The fragmentational behaviour of the precursor ions was evaluated using the binary and scoring result matrices along with a hierarchical clustering.

18


Page 19 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

342


Conclusion

343

In this study, ESI(−) CID-FT-ICR-MS analyses results of seven dierent pyrolysis oils, pro-

344

duced from pyrolysis educts of dierent origin and degree of coalication, were evaluated

345

using a targeted classication and untargeted clustering to structurally characterize acidic

346

oil compounds. The targeted classication approach enables an evaluation of the tandem MS

347

experiments in terms of product ions that can be related to their precursor ions by compound

348

class specic neutral losses. Thus, this chemometric method utilizes fragmentation pathways

349

of eleven compound classes reported in studies from other research groups. In contrast, the

350

untargeted classication approach is based on an evaluation, if an observed product ion can

351

be related to a precursor ion. The advantage of this method over the targeted classication

352

approach is its potential to structurally characterize a higher number of compounds and

353

compound classes, due to the unnecessity for theoretical fragmentation pathways. Hence,

354

using the untargeted classication approach, results obtained from analyses utilizing dier-

355

ent ionization techniques, ionization modes or ion activation methods can be compared. The

356

current version of the developed targeted classication approach is exclusively designed for

357

ESI(−)-CID analyses of complex organic mixtures, which inevitably limits its application to

358

data sets received by dierent ionization or tandem MS methods.

359

By applying both approaches, dierences between the analyzed pyrolysis liquids were dis-

360

covered. For instance, the results of the targeted classication indicate to a comparable

361

amount of saturated and unsaturated monocarboxylic acids, as well as aromatic aldehydes,

362

ketones and acids and saturated dicarboxylic acids in the brown coal pyrolysates BCHA,

363

BCSL and BCSO. In contrast, hard coal pyrolysate HCPO seems to contain a higher num-

364

ber of unsaturated monocarboxylic acids and sulfonic acids than all other analyzed pyrolysis

365

oils. Using the untargeted clustering approach, similar trends for these pyrolysis oils were

366

found. In general, the results from both chemometric methods could be used, for example, to

367

construct compound class specic extraction strategies to achieve a more expedient pyrolysis

368

oil production and a more ecient application of the pyrolysis liquids in terms of material 19



369

utilization.

370

Further advancements of the analyses and data evaluation methods presented should involve

371

an implementation to analyze data sets obtained from LC-MS/MS or GC-MS/MS experi-

372

ments. Hence, also a chromatographic separation of isomers would be possible, leading to

373

further structural information on the compounds in complex organic mixtures.

20


Page 20 of 31

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374


Acknowledgement

375

This work has been funded by the European Social Fund (ESF) and the Development

376

Bank of Saxony (SAB). Furthermore, we would like to thank Denise Klinger and Michaela

377

Ngyuen from the Institute of Energy Process Engineering and Chemical Engineering at TU

378

Bergakademie Freiberg for kindly providing the pyrolysis liquid samples.

379

380

381

Supporting Information Available The following les are available free of charge as Supporting Material to this paper: Supporting_Information_Zuber_et_al_2018_Fragmentation_Classication_Clustering.pdf:

382

Additional information on the feedstocks used to produce the pyrolysis oils analyzed

383

for this paper, as well as calibration lists used for the ESI(−)-CID analyses, theoretical

384

fragmentation trees utilized for the targeted classication, a visualization of the result

385

matrix of the targeted classication approach, a list of possible neutral losses used for

386

the untargeted clustering, an in-depth description of the scoring mechanism utilized

387

for this approach and an overview of the identied monounsaturated monocarboxylic

388

acids in the analyzed oils, using the untargeted clustering approach, are presented.

389

ClassicationTool.m: Executable MATLAB code of the newly developed program for

390

the targeted classication and untargeted clustering.

391

Result_matrices_classication_and_clustering.mat: MATLAB result matrices of the

392

targeted classication and untargeted clustering (binary and scoring matrix) of the

393

precursor ions of the seven analyzed and evaluated oil samples.

21



394

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Page 31 of 31

599

Graphical TOC Entry Intensity

m/z

m/z

m/z

m/z

Collision energy

600 Untargeted clustering

Targeted classication

DBE

O2, DBE = 1, EE Saturated monocarboxylic acids

180

20

160

18

16

140

- H2O

14

120

O1, DBE = 2, EE Product ion 1

- CO2


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


- C3H6 - H2

- CO

12

100 10

80 8

60 6

40

Product ion 2

Product ion 3

Product ion 4

CcHh, DBE = 2, EE

CcHh, DBE = 0, EE

O2, DBE = 2, EE

4

20

0 100

2

0

200

300

400

500

600

m/z [Da]

31


700

800

Structural Characterization of Acidic Compounds in Pyrolysis Liquids

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