Multidimensional gas chromatography in food analysis · An overview 28 of recent data analysis,...

Nolvachai et al. MDGC in Food Analysis Page 1

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Multidimensional gas chromatography in food analysis 4

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Yada Nolvachai, Chadin Kulsing and Philip J. Marriott* 8

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1 Australian Centre for Research on Separation Science, School of Chemistry, Monash 10

University, Wellington Road, Clayton, VIC 3800, Melbourne, Australia. 11

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Submitted to: 14

TrAC Trends in Analytical Chemistry 15

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(Running Head: MDGC in Food Analysis) 18

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* Corresponding author: Tel.:+61 3 99059630; fax +61 3 99058501; 21

Email: [email protected] 22

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Abstract 25

Multidimensional gas chromatography (MDGC) offers excellent separation efficiency that 26

serves advanced characterisation of volatiles and semi-volatiles in food samples. An overview 27

of recent data analysis, capabilities, and technologies in MDGC commencing from 28

conventional heart-cut (MDGC), to its more recent comprehensive two-dimensional gas 29

chromatography (GCGC) variant are included. Selected applications are emphasised that 30

include improved untargeted and target compound identification, fingerprinting and group type 31

analysis, applied to food. Benefits to improved compound identification in MDGC by 32

employing different hyphenation approaches are also emphasised. We conclude by highlighting 33

recent advances and future trends, including three- and higher-dimensional GC with MS 34

separation. 35

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Keywords 37

GCGC; GC–GC; higher dimensional separation; LC–GC; complex sample analysis 38

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1. Introduction 40

There is a broad requirement for analysis of food samples, ranging from pure natural products 41

(e.g., honey, sugars, herbs, spices and milk), to technologically-modified products (e.g., roasted 42

products and cheese peptides), analysis of undesired transformation products (e.g., oxidisation, 43

isomerisation, and hydrolysis), or xenobiotic products (e.g., colourants, phytosanitary products, 44

mineral oil, plastic derivatives and printing inks, pesticides and deliberately adulterated 45

materials) [1]. In general, food consists of a large number of chemical classes, e.g., amino acids, 46

organic acids, nucleic acids, proteins, lipids, essential oils, flavour and aroma compounds, 47

polyphenols, and polysaccharides, having a broad molar mass range, variable polarity, and a 48

wide range of chemical abundance. A challenge remains in the identification and quantification 49

of such constituents in a complex food matrix. Over the years, considerable effort has been 50

expended in development of analytical methods for improved food analysis, and generally may 51

extend from sample preparation, through more advanced instrumental procedures, sample 52

profiling, data analysis, identification of potential candidate markers for specific purposes, and 53

biological interpretation [2]. 54

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Recent analytical technologies allow improved comprehensive molecular profiling of 56

thousands of compounds in foods in a single analysis. With highly sensitive and selective MS 57

approaches, food components can be detected at trace levels (e.g., fmol) [2]. However, 58

notwithstanding the expense of analysis cost and time, additional chromatographic separation 59

prior to MS detection is often required for analysis of a large number of compounds. In addition, 60

data analysis can be obtained with greater confidence when combined with chemical separation, 61

especially when analysing isomers or compounds with similar mass spectrum fingerprints. 62

Several reviews detail different chromatographic and MS based approaches in food and food 63

metabolomics analysis [3-5]. Among these techniques, MDGC offers high resolution chemical 64

analysis arising from enhanced peak capacity, some of which have been highlighted in a recent 65

review on comprehensive chromatography for food analysis [1]. 66

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Different experimental set ups and approaches to implementing MDGC methodologies have 68

also been developed and reviewed [6-8], which can be considered equally applicable for food 69

analysis. This review further focuses on recent technologies, data analysis capabilities offered 70

by MDGC, and additional applications with some useful systems and hyphenations, which 71

improve reliability in analysis of different types of food samples. 72

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2. Multidimensional gas chromatography (MDGC) 74

Following the first demonstration of heart-cut (H/C) multidimensional gas chromatography 75

(MDGC) reported by Simmons and Snyder [9] in 1958 for analysis of a stabilised platformate 76

sample, an early application of this technique in the area of food flavour was reported in 1977 77

[10-12]. Some years later, the introduction of comprehensive two-dimensional gas 78

chromatography (GCGC) was reported in 1991 by Liu and Phillips [13] with application of a 79

thermal modulator for analysis of standard mixtures and a coal liquids sample on a 1D / 2D 80

column set of complementary polarities. An early report applying GCGC to analysis of 81

essential oils in 2000 [14], was followed by further food related analysis by GCGC. In general, 82

MDGC applies high resolution approaches for the separation of complex samples in order to 83

provide improved individual molecular-based information of volatile analytes [6, 15]. It 84

conventionally employs two sequentially connected columns with different (ideally orthogonal) 85

selectivity toward sample components, via a device offering an effective heart-cut (H/C) or 86

modulation process [16]. Compared with one dimensional GC (1DGC), MDGC provides 87

improved resolution and analyte peak capacity, and reduced interfering chemical background. 88

Improved detection limit is also expected, i.e., in systems where a cryogenic H/C collection 89

method is used, as a result of the cryogenic trapping/refocusing effect. Since high resolution is 90

suited to complex sample analysis, sample preparation steps (e.g., liquid/solid phase extraction) 91

may also be reduced or eliminated, avoiding the problem of analyte loss during sample 92

preparation, which is important especially when sample amount is limited. 93

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3. Capability for compound identification in food samples with MDGC 95

Confirmation of compound identities with MDGC is based on integration of information 96

obtained from different analytical data. With more information available, compound 97

identification can be obtained with greater confidence as briefly summarised in Fig. 1. After 98

performing MDGC analysis, experimental data are processed and resulting parameter values 99

(e.g., GC retention indices, MS molecular or fragment m/z values, Fourier transform infrared 100

(FTIR) characteristic vibrational spectra, or olfactometry odour descriptors) can be obtained for 101

compound identification. Comparison with reference database values, either from the literature 102

or acquired in-house, is usually performed. Most often, MDGC will be hyphenated with flame 103

ionisation detection (FID) or MS, although other detectors (e.g. electron-capture detection 104

(ECD), nitrogen phosphorus detection (NPD), and flame photometric detection (FPD)) may 105

offer specific compound analysis as required [17]. It is known that MS analysis provides useful 106


information such as molecular or fragment ion m/z of analytes, but specificity towards an 107

individual molecule is lacking; e.g., isomers often have similar mass spectra. Comparison with 108

MS library data only results in a suggested set of compounds with corresponding match score 109

or probability. Conventionally, a peak could be assigned as a compound with the best match 110

score (e.g., >70–80% probability). However, this approach can be insufficient in the area of 111

food analysis, especially when analysing compounds with similar fingerprints or isomers such 112

as those in saffron [18]. On the other hand, molecular spectroscopic based information, such as 113

functional groups, proton environment, electronic transitions or 3D structures, which might be 114

important for absolute identification, are not normally provided from the GC experiment. The 115

important method of FTIR is an exception, and hyphenated GC–FTIR has been available for 116

many years, although today is not often used (Section 4.2.3). Preparative GC, with collection 117

of an individual, resolved analyte, offers further – off-line – identification, and whilst tedious, 118

adds molecular structural specificity to compound identity. 119

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INSERT FIGURE 1 HERE 121

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Hyphenation with other detectors can be applied in order to confirm compound identities. For 123

example, MDGC can be hyphenated with olfactometry for odorant analysis, FPD for sulfur 124

compound analysis, NPD for analysis of nitrogen-containing compounds, and ECD for 125

halogen-containing compounds [17, 19, 20]. For absolute identification, preparative MDGC 126

can be performed (e.g., proof of concept studies for caffeine analysis [21] or menthol in 127

peppermint [22]) prior to molecular spectroscopic information such as that obtained from 128

nuclear magnetic resonance (NMR) and FTIR. Although of low sensitivity and poor detection 129

limit e.g., when using a light-pipe flow channel to interface GC with FTIR, on-line hyphenation 130

with FTIR has been reported, and is reviewed in detail in Section 4.2.3. 131

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3.1. 1D and 2D retention indices in MDGC 133

MDGC offers additional capability to screen out false compounds, and hyphenated with MS is 134

the best technology for this. Although the compounds may have high MS match scores, the 135

application of 1D and 2D retention indices on the respective phases (1I and 2I), which can be 136

assigned for each peak relative to peak positions of suitable reference compound series (e.g., 137

alkanes or saturated fatty acid methyl esters, FAME), can provide additional supporting 138

information to reject incorrectly matched peaks. 1I can be calculated directly from retention 139

time (tR) of a target analyte normalised to the reference compound series scale according to the 140


van den Dool and Kratz relationship in temperature (T) programmed separation. 2I in MDGC 141

with T programmed separation of 2D may also be interpreted by the van den Dool and Kratz 142

formula, but for isothermal 2D separation (which applies to the fast 2D separation in GCGC), 143

2I can be calculated according to Kovatś index [18]. Alternatively, I values of a peak can also 144

be approximated according to the plot of reference compound tR values vs carbon number [23]. 145

Note that comprehensive sets of retention indices are only available for a few GC phase types 146

so for analysts who wish to have the additional information provided by index values, only a 147

few phases can be used. 148

149

Calculation of I values for peaks of interest require injection of reference compounds in MDGC 150

that span the peak, under the same experimental conditions. Providing that the detector is 151

sensitive to hydrocarbons, the reference positions in MDGC can be experimentally obtained. 152

Alkane positions in 1D can be obtained directly by injection of alkanes into MDGC with 153

detection at the end of the 1D column (e.g., by diverting the flow through the restrictor in H/C 154

MDGC, e.g., Fig. 2A (left), or 1tR in GCGC approximated from the time of the modulated 155

peak maximum response). However, generation of reference compound positions in 2D can be 156

difficult, but they can be predicted based on 1DGC isothermal data using the same experimental 157

column with corrected flow calculation as shown for prediction of saturated FAME positions 158

in GCGC [24, 25]. 159

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162

A more straightforward approach involves experimental injection of reference compounds onto 163

the applied system in MDGC. All of the alkanes are H/C then trapped at the inlet of the 2D 164

column, and in the described example the oven is cooled prior to alkane release to conduct the 165

2D separation under the same experimental conditions as for the H/C regions of the sample. 166

Thus, H/C alkanes and sample regions are analysed under the exact same conditions, and 2I 167

values may be calculated. For example, construction of alkane retention plots on both 1D and 168

2D columns allowed calculation of 1I and 2I for five peaks attributed to Shiraz wine odours, 169

which were then confirmed to be acetic acid, octen-3-ol, ethyl octanoate, hexanoic acid and -170

damascenone [23], as illustrated here for the peaks a–e, respectively, in Fig. 2A. Providing that 171

the 2D separation is performed under the same experimental conditions (e.g., same flow rate 172

and T programme), the alkane positions are applicable for calculation of 2I for all the H/C 173


fractions in the analysis as indicated by the use of the same alkane positions for both H/C I and 174

H/C II in Fig. 2A (right). This study confirmed peak identities with high confidence by 175

combination of experimental data that included (1) nominal mass from quadrupole (Q) MS, (2) 176

odour description from olfactometry, (3) 1I and (4) 2I data from H/C MDGC. Columns 1D and 177

2D had difference phases, for which reference retention index values were available. 178

179

In GCGC with T programmed separation, calculation of 2I cannot be performed directly based 180

on comparison of target analyte 2tR with that of reference literature, since 2tR of compounds vary 181

according to the prevailing T at the elution point. The variation of alkane retention time with T 182

must be investigated in order to calculate 2I values; the resulting relationship is termed an 183

‘isovolatility’ curve. Experimental approaches for construction of isovolatility curves involve 184

a sequence of multiple injections of the reference compound mixture into the 2D column [26, 185

27]. A recent approach in food analysis applied multiple direct injections of an alkane mixture 186

in GCGC with several stepwise isothermal oven T programmes [18]. The use of a cryogenic 187

modulator allows trapping and release of relevant alkanes, e.g., C8 and above at 0 C 188

modulation T (but without trapping of solvent such as hexane) on the 2D non-polar column as 189

shown in the 2D plot in Fig. 2B (left). 190

191

The solvent can be eluted prior to the release of the reference alkanes at every isothermal step. 192

Sufficient time is also required to elute alkanes of interest at each step. Isovolatility curves (peak 193

positions at different T) of the reference compounds can then be reconstructed to form the dotted 194

lines on the 2D contour plots of samples for 2I calculation, as illustrated for saffron analysis in 195

Fig. 2B (right). 196

197

4. Application of MDGC in food analysis 198

Characterisation of chemical constituents in food (qualitative or quantitative) by MDGC 199

approaches is essential for the improved assessment of food safety and quality. This enables 200

enhanced analysis and differentiation of food products with more complete descriptive and 201

informative parameters for evaluation of food compared to conventional analytical approaches, 202

according to bulk factors, e.g., smell, texture, flavour or colour. Food samples in this review 203

are classified, based on the degree of complexity of the chemical components to be measured, 204

into four groups (S1–S4) following the criteria given by the previous review [1]. This arbitrary 205

classification considers S1 as being of low-complexity which includes mixtures of up to 50 206


volatile compounds (e.g., vegetable oil fatty acids). This group of samples should be suitably 207

analysed by 1DGC employing a conventional GC column (30 m 0.25 mm ID) with 208

appropriate selectivity. Sample type S2 includes medium-complexity samples, consisting of 209

50–200 compounds such as essential oils. Use of a high-efficiency conventional GC column 210

may be adequate for analysis of S2, such as a 200 m long speciality cyanopropyl phase column 211

for more complex fatty acids. High-complexity samples (S3) are defined as a mix of 200–400 212

volatiles (i.e., fish oil fatty acids), whereas very high-complexity samples (S4) are those 213

containing >400 different compounds (e.g., as required for metabolomics, or industrial roasted 214

food products such as coffee). 1DGC analysis is ineffective for analysis of S3 and S4, unless 215

only a smaller number of analytes are of interest, for which reliable detection is available. 216

However, analysis of all the group types have been applied to MDGC and GC×GC. For 217

example, although application of MDGC may seem to be extravagant for separations of low 218

complexity, e.g., S1–S2 samples, this technique offers additional identification tools via the use 219

of 1I and 2I as well as cryogenic focusing effects (with improved detection limits) and more 220

detailed compound fingerprints. It should be noted that classification of samples here are not 221

strictly applied for all the studied cases in the future. Rather, the samples mentioned in this 222

review are mainly classified on the basis of their apparent chromatograms as mentioned earlier 223

[1]. Selected MDGC applications for analysis of S1–S4, ranging from simpler fatty acid 224

samples, to flavonoids in chocolate (S2), odour compounds in wine (S3), and coffee samples 225

(S4), are exemplified in Fig. 3 with recent applications summarised in Table 1. 226

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Table 1. Selected recent applications of MDGC in food analysis. 230

Application Column Set* Compound

confirmation Ref

2017

FAME profile of wild

and farmed fish and

bivalves

GC×GC–QMS 1D: SLB-5ms (30×0.25×0.25) 2D: SUPELCOWAX 10

(1.5×0.10×0.10)

Authentic standard

AOCS lipid library

Retention index

[32]

2016

Fingerprinting of extra

virgin olive oil volatiles GC×GC–QMS 1D: SolGel-WAX (30×0.25×0.25) 2D: Mega-1701 (1×0.1×0.10)

NIST/Wiley/in-

house library

Retention index

[33]

Separation of piperitone

enantiomer in aged

Bordeaux red wines.

MDGC–accTOFMS 1D: BP20 (30×0.22×0.2)

Authentic standard

NIST library

[34]


2D: MEGA-DEX DAC-Beta

(25×0.25×0.25) Retention index

Literature

FAME in non-

hydrogenated vegetable

oils using online

reduction

GC×GC–FID 1D: SP2380/SP2560/SLB-IL111

(100×0.25×0.20) 2D: SLB-IL111 (2×0.1×0.05)

Authentic standard [28]

2015

Integrated GC×GC and

MDGC for analysis of

Shiraz wine and coffee

powder volatiles.

MDGC/GC×GC–QMS/O/FID

For Shiraz wine 1D: DB-FFAP (30×0.25×0.25) 2DS: BPX5 (0.9×0.10×0.10) 2DL: DB-5ms (30×0.25×0.25

For Coffee sample 1D: SLB-IL59 (30×0.25×0.20) 2DS: VF-200ms (1.0×0.25×0.25) 2DL: VF-200ms (30×0.10×0.10)

Authentic standard

NIST library

Retention index

[31]

Odour-active

compounds in

pasteurised orange juice

MDGC–O/QqQMS 1D: HP-FFAP (30×0.25×0.25) 2D: DB-5ms (30×0.25×0.25)

GC×GC–FID 1D: HP-FFAP (30×0.25×0.25) 2D: Rxi-5Sil MS (1×0.18×0.18)

GC×GC–QTOFMS 1D: SolGel-WAX (30×0.25×0.25) 2D: Rxi-5Sil MS (0.8×0.1×0.1)

Authentic standard

NIST library

Retention index

[35]

Two-dimension

retention indices for

analysis of saffron

GC×GC–accTOFMS 1D: SUPELCOWAX 10

(30×0.25×0.25) 2D: Rxi-5Sil MS (1.0×0.1×0.1)

NIST library

Retention index

[18]

Profiling of volatiles in

sea salt from North-East

Atlantic ocean

GC×GC–TOFMS 1D: BPX5 (30×0.25×0.25) 2D: BP20 (1×0.1×0.1)

NIST library

Retention index

[36]

Odour-active volatiles in

banana Terra spirit MDGC–O/QqQMS 1D: HP-FFAP (30×0.25×0.25) 2D: DB-5ms (30×0.25×0.25)

GC×GC–FID 1D: HP-FFAP (30×0.25×0.25) 2D: Rxi-5Sil MS (1.0×0.18×0.18)

Authentic standard

NIST library

Retention index

[37]

Compound causing off-

flavoured cork taint from

cork stopper

MDGC–O/FID/MS/MS 1D: ZB-Wax (20×0.25×0.5) 2D: DB-5 (11×0.32×1.0)

MDGC–ECD 1D: DB-XLB (20×0.25×0.25) 2D: TG-1301MS (15×0.25×0.25)

GC×GC–QMS 1D: ZB-Wax (30×0.25×0.5) 2D: BPX5 (2.0×0.15×0.25)

Authentic standard

NIST library

Retention index

MS/MS (on some

compounds)

ECD (on some

compounds)

[38]

Determination of

process-induced PAH MDGC–QMS/O 1D: Rtx-5ms (60×0.32×1)

Authentic standard

(PAH)

[39]


and aroma active

compounds in meat

cooked with different

processes

2D: DB-WAXETR (30×0.32×1)

GC×GC–TOFMS 1D: BPX5 (30×0.25×0.25) 2D: BPX50 (1.0×0.1×0.1)

NIST library

Retention index

Profiling of minor

components (<5%) in

vegetable oil

GC×GC–FID/QMS 1D: Rxi-5Sil MS/Rxi-17Sil MS

(8×0.25×0.25) 2D: Rxi-17Sil MS/Rxi-5Sil MS

(1.5×0.15×0.15)

Authentic standard

NIST library

Retention index

[40]

2014

Essential oil in spices

treated with γ-irradiation

and modified

atmospheric packaging

GC×GC–QTOFMS 1D: HP-5ms (30×0.25×0.25) 2D: BPX50 (1.2×0.1×0.1)

Authentic standard

NIST library

[41]

Free phytosterol in

vegetable oil as a marker

for adulteration

GC×GC–TOFMS 1D: DB-5ms (30×0.25×0.25) 2D: Rxi-17Sil MS (2.0×0.15×0.15)

Authentic standard

NIST library

[42]

Investigation of

enantiomer ratio of

chiral volatile organic

compounds in honeys

MDGC–FID 1D: DB-FFAP (30×0.25×0.25) 2D: CP-Chirasil-Dex CB/ MEGA-

DEX DMT-Beta (20×0.25×N/A)


Methoxypyrazine in

ladybug-tainted German

wine

MDGC–QMS 1D: HP-5ms (30×0.25×0.25) 2D: DB-WAX (30×0.25×0.25)

Authentic standard

Selected ion

monitoring

[44]

Lactone responsible for

overripe orange aroma in

Bordeaux dessert wines

MDGC–O/QMS (EI) or TOFMS

(CI) 1D: HP-5 (30×0.32×0.5) 2D: BP20 (50×0.22×0.25) or BP1

(50×0.22×1)

Authentic standard

NIST library

Retention index

CI with methanol

[45]

Profiling of Camellia

sinensis tea leaves

harvested in spring and

monsoon

MDGC–QMS 1D: Rtx-Wax (30×0.25×0.25) 2D: Rxi-5ms (30×0.25×0.25)

NIST/Adams

essential oil library

Retention index

Literature data

[46]

2013

Volatiles from

Australian-grown

strawberry

GC×GC–TOFMS 1D: BPX5 (30×0.25×0.25) 2D: BP20 (1.0×0.1×0.1)

Authentic standard

NIST/ Adams

essential oil library

Retention index

[47,

48]

FAME in safflower oil,

linseed oil, butter and

fish oil

GC×GC–FID or QTOFMS 1D: SLB-IL111 (30×0.25×0.2) 2D: SLB-IL59 (1×0.1×0.08)

Authentic standard

Accurate mass

[49]

Effect of shelf life

storage of aroma

compounds in dried

ground fennel seeds

GC×GC–FID 1D: HP-INNOWax (15×0.25×0.25) 2D: DB-1 (1.1×0.1×0.1)

GC×GC–TOFMS 1D: DB-FFAP (15×0.25×0.25) 2D: DB-1 (1.1×0.1×0.1)

Authentic standard

NIST/Wiley

library

Retention index

[50]


Organophosphorus

pesticides in Chinese

grain, fruit, vegetables

GC×GC–FPD 1D: DB-5 (30×0.32×0.25) 2D: DB-1701 (1×0.1×0.4)

Authentic standard

FPD (Phosphorous

mode)

[51]

FAME in fish oil and

butter using integrated

MDGC and GC×GC

system

MDGC/GC×GC–FID 1D: DB-5ms (30×0.25×0.25) 2DS: HP-INNOWax (2×0.18×0.18) 2DL: DB-FFAP (30×0.25×0.25) or

SLB-IL76/100/111 (30×0.25×0.2)


Irradiated cooked ham

using direct sample

introduction

MDGC–ITMS 1D: ZB-WAX (30×0.25×0.25) 2D: Chirasil-β-Dex (30×0.25×0.25)

Authentic standard

NIST library

[52]

Benzophenone

derivatives in foods from

Switzerland

MDGC–MS 1D: VF-5ms (15×0.25×0.25) 2D: VF-17ms (5.0×0.15×0.15)


*Column dimensions are expressed in parentheses as (length (m)×internal diameter (mm)×film 231

thickness (μm)). 232

233

Conventionally, GCGC or comprehensive MDGC approaches are applicable for either target 234

or untargeted analysis, group type analysis and fingerprinting; whilst, selective H/C MDGC 235

with a long 2D column (10–30 m) can be specifically applied for improved target compound 236

analysis. However, these are not strictly applied for all food analyses. Application of GCGC 237

and H/C MDGC can be combined, allowing further options towards specific types of analysis. 238

239

4.1. Untargeted compound identification 240

This analysis aims to identify all or part of the constituents in food including either natural or 241

technologically-modified with further quantification, if required [1]. Since untargeted analysis 242

requires (or should require) the highest possible separation of compounds, either GCGC or 243

comprehensive hybrid MDGC clearly provides benefits for such analyses, resulting in a whole 244

profile of compounds within a sample. A critical review for application of GCGC for analysis 245

of S1–S4 classes has been reported [1]. Recent applications are given in Table 1. For example, 246

analysis of a complex matrix of saffron (S4) with >600 peaks detected by GCGCaccTOFMS 247

(accurate mass time-of-flight MS) [18], tentatively identified 213 volatiles in the saffron sample 248

with MS match and reverse match score values being ≥750. Within this set of volatile 249

compounds, 114 compounds were further confirmed (but still must be considered tentative, in 250

the absence of authentic standards) according to 1I and 2I values within ±20 index unit. 251

252

Note that 2I calculation was performed based on experimental results for isovolatility curves of 253

alkane references (Fig. 2B). An example demonstrated identification of a peak of interest where 254


the accTOFMS result of this peak initially proposed six isomers; each of which gave an MS 255

match factor ≥750, but with 1I revealing only two possible isomers with retention indices within 256

±20 compared with literature data [18]. 2I was further applied to identify this peak as 1,4-257

diethyl-2-methyl-benzene. Of course, best confirmation of identity is to co-inject an authentic 258

standard of this proposed compound, and check that its 1D and 2D retentions coincide, and that 259

the mass spectrum agrees. The overall process is shown in Fig. 4. In the cases where I values 260

are not available from the literature (e.g., for compounds whose I values have not been 261

established, or columns for which I values are unavailable) prediction of retention index data 262

in either 1D or 2D is also possible, e.g., according to the concept of the linear solvation energy 263

relationship (LSER) as illustrated in the prediction of retention indices for several compounds 264

in food, such as separation of FAME on a wide range of columns [24, 54]. 265

266


268

4.2. Target compound identification 269

Target compound analysis requires good separation of the target peak(s) from interferences, 270

unless detection protocols allow unique response just to the target analyte(s). Whilst it is 271

possible to obtain target compound analysis in GCGC, additional 2D separation power is often 272

required due to complex matrix interferences, especially for target compound(s) present in S3 273

and S4 matrices. In this case, single or multiple H/C with the focus on particular 1D separation 274

regions of target compounds can be performed prior to separation with a long 2D column, which 275

offers enhanced separation with less interference and improved detection limit. Since a whole 276

compound profile of the sample (comprehensive analysis) is not required in target analysis, 277

total analysis time can be reduced by ignoring H/C analysis of regions that do not have the 278

target analyte(s). However with an expansive separation power, GCGC can be applied for 279

reduction of analysis time. Target compound analysis in food with GCGC has been reviewed 280

[1]. Recent applications of MDGC for target compound analysis in food are also summarised 281

in Table 1. Advanced on-line hyphenation for improved target compounds analysis with 282

MDGC is further highlighted below. 283

284

4.2.1. Hyphenation with MS/MS 285

Hyphenation with MS/MS allows better selectivity and sensitivity of target compounds, which 286

can improve (decrease) limit of detection and analysis reliability. Pesticide analysis is a good 287


example for application of MDGC–MS/MS due to the requirement of especially sensitive and 288

selective MS analysis with high chromatographic resolution, e.g., as offered by MDGC [17]. 289

With enhanced chromatographic resolution provided by MDGC, target pesticide isolation can 290

be performed by selecting the correct set of precursor and product ions (MS/MS transitions) 291

resulting in improved sensitivity towards the target analytes. H/C MDGC–MS/MS was reported 292

for analysis of food related samples, e.g., for identification of off-flavour compounds in natural 293

cork stoppers which were present in wine [38] such as 3,5-dimethyl-2-methoxypyrazine, 3-294

isopropyl-2-methoxypyrazine, 3-isobutyl-2-methoxypyrazine, 2-methylisoborneol and 295

geosmin. These compounds are responsible for the smells of “musty, moldy, nutty, earthy”, 296

“green, pea, earthy”, “green, bell pepper”, “earthy, musty, camphoraceous” and “earthy, moldy, 297

beetroot”, respectively, as confirmed by MDGC coupled with olfactometry (MDGCO). 298

299

Apart from H/C analysis, multiple reaction monitoring (MRM) analysis with GCGC–MS/MS 300

allowed sensitive and selective detection of five pesticides at 1 ppb spiked in spearmint oil 301

(fenchlorphos, terbufos, fenthion, bupiramate, resmethrin I/II) [55]. This approach is especially 302

useful when there are interference signals in MS/MS analysis where clean signals of the target 303

compounds can be well separated from interferences in a 2D separation space as shown in the 304

GCGC–QqQMS result operated in MRM mode for the five MS/MS transitions in Fig. 5. 305

306


308

4.2.2. Hyphenation with olfactometry 309

Due to the complexity of food samples, where aroma-active compounds are to be distinguished 310

from other components by using olfactometry, high resolution techniques such as MDGC are 311

required. This is in order to minimise background interference prior to odour detection and to 312

narrow down a range of possible odorants in olfactometry analysis, especially for parallel MS 313

detection. Poorly resolved compounds will make attribution of the sensed odour to a specific 314

compound recorded by MS difficult. Different MDGCO based methodologies have been 315

developed and reviewed for analysis of complex food samples in order to distinguish individual 316

odour compounds, and then assess their contribution to global aroma and odour characteristic 317

of a sample [56]. Recent applications are summarised in Table 1. Advanced development and 318

applications will be further discussed in Section 5.2. 319

320


4.2.3 Hyphenation with FTIR 321

Due to its superior detection limit and available standard operation protocols, with widely 322

established databases of electron ionisation (EI) MS spectra, GC–MS is far more popular than 323

GC–FTIR. However, the molecular vibrational information from FTIR makes this technique 324

attractive as a universal, selective and/or specific detection method, which provides 325

complementary information involving functionalities and some structural arrangement detail of 326

compounds. GCFTIR has been applied to the analysis of compounds in food or food related 327

samples such as FAME [57-60]. FTIR is especially useful for differentiation of geometric 328

(cis/trans or E/Z) isomers [61], which is essential in food analysis. For example, trans-329

conjugated unsaturated fatty acids are believed to contribute to the prevention of health related 330

diseases such as heart attacks and cancer. An example can be given for analysis of FAME. 331

Whilst the presence of FAME can be indicated e.g., by monitoring the carbonyl absorption at 332

1754 cm–1, further molecular based information can also be obtained. Bending out-of-plane of 333

–HC=CH– bonds for trans and cis isomers show wavenumbers of e.g., 969 cm–1. The values 334

can be slightly shifted depending on the instrumental setup. Although the vibrational bands 335

observed in this bending mode are similar for these isomers, the absorptivity for the –HC=CH– 336

bending (out-of-plane) of the trans isomer is much higher resulting in a more intense peak at 337

969 cm–1. Other GCFTIR applications include analysis of natural saccharides [62] and 338

mycotoxins which are formed by fungal activity in food products under specific environmental 339

conditions of moisture, temperature and host, some of which are highly toxic to humans, 340

animals and plants [63], as well as identification of amino acids and related pyrolysis products 341

[64]. 342

343


345

The obtained spectra can be searched against available database spectra, e.g., of vapour-phase 346

reference compounds [66]. When neither libraries nor standard compounds are available, 347

identification of unknown peaks in GCFTIR/MS results is still possible. From the molecular 348

mass of the unknown compound and its fragmentation, a set of expected compound structures 349

can be generated. The obtained FTIR spectra can be used to further select plausible structures 350

of the target peak. FTIR fingerprints are specific to molecular modes of vibrations, and can be 351

directly predicted based on quantum chemistry, e.g., by using GAUSSIAN [67]; prediction is 352


especially useful when confirmation of functional groups or isomer structures is required [68, 353

69]. 354

355

MS is a favoured technique to be hyphenated with FTIR, providing complementary information 356

of both molecular/fragment m/z ratios and possible compound functionalities of the studied 357

compounds [66]. Combined with GC retention indices, a more informative conclusion as well 358

as more confidence in the ultimate identification can be obtained. A typical hyphenation can be 359

achieved by performing conventional GC or MDGC followed by post-column splitting at a 360

suitable ratio (e.g., 10% v/v of the effluent to MS and the rest to FTIR [70]), prior to the 361

synchronised detection with both MS and FTIR. One obvious advantage of the hyphenation of 362

MS and FTIR is to avoid the ambiguity regarding which mass spectrum corresponds to which 363

infrared spectrum, provided the respective compound is located in the MS or FTIR at the 364

appropriate time; MDGC should assist to assuring this. 365

366

The example MDGCFTIR/MS system was reported as shown in Fig. 6A for analysis of 367

cascarilla bark, which can be applied to flavour the liqueurs Campari and Vermouth. The 368

example 1D and 2DGC results are shown in Fig. 6B–6E. Application of MDGC clearly 369

improved reliability in compound identification where a wide range of compound classes can 370

be confidently confirmed according to clean signals of well separated peaks in FTIR and MS 371

[65]. In addition, the use of a rotary valve (d in Fig. 6A) on select specific traps allows 372

continuous cryogenic trapping of several H/C zones in a single analysis which can effectively 373

reduce analysis time in multiple H/C analyses. Beside the benefit of additional make-up flow 374

of He for re-injection and 2D separation, the use of three switching valves allows an on-line 375

option to select the desirable stationary phase (g or h in Fig. 6A) for different quality 2D 376

separations. Further application of this approach for analysis of food is still a challenge. 377

378

4.3. Fingerprinting 379

Effective fingerprinting requires use of chemometrics approaches to define similarities and 380

differences between food samples according to the MDGC results (fingerprints) after data pre-381

processing (e.g., baseline correction, noise reduction, and retention time alignment) [71], as 382

previously reviewed [72, 73]. GCGC analysis of volatiles in roasted hazelnut samples obtained 383

from different origins reveals these samples as S4 with >400 peaks detected, Fig. 7A. 384

According to the similarity with a set of samples of interest, one or more peaks can be combined 385


into a chromatographic feature and classification of different hazelnut samples can be obtained 386

based on their fingerprints as shown in Fig. 7B. 387

388


390

4.4. Group type analysis 391

Underivatised low volatility compounds such as sterols and lipids can be analysed with high T 392

GC, e.g., with the oven T programme ramped up to >350 C. Quantitative comparison between 393

different compound types can be performed according to peak area analysis of the GCGC 394

result as illustrated in Fig. 8A for a solvent extract of green Arabica coffee bean. The GCGC 395

result clearly indicates substantial co-elution between different compound types in 1DGC 396

analysis. As a result, the group type analysis result obtained from 1DGC showed significant 397

variation compared to literature values (Fig. 8B), especially for the analysis of minor 398

compounds such as hydrocarbons, diterpenes, sterols, diterpene esters and diacylglycerols 399

(DAG), whilst more accurate values (closer to those reported in the literature) were obtained 400

from GCGC analysis. Furthermore, cryogenic modulation permits refocusing of these 401

compounds at the beginning of the 2D separation which increases their detectability, with about 402

200 peaks detected in the coffee bean extract [74]. The developed GCGC technique is 403

especially useful for improved analysis of diterpenes and sterols, which are important chemical 404

markers to distinguish coffee species, supporting authentication of coffee blends [75, 76]. In 405

addition, diterpene contents were correlated with coffee cupping tests as potential quality 406

markers [77], while DAGs and triacylglycerols (TAGs) have been found to be markers for 407

coffee beans treated by different degrees of roasting from medium to very dark [78]. This 408

established approach broadens compound ranges (hydrocarbons, fatty acids, tocopherols, 409

sterols, diterpene alcohols, esters, DAGs and TAGs), which can be analysed by GCGC (and 410

expectedly MDGC), allowing improved compound identification and group type analysis in 411

any food sample as an alternative to the application of liquid chromatography (LC) and direct 412

analysis with high resolution MS. 413

414


416

5. Advanced and future trends 417

5.1. Higher dimensional separation 418


5.1.1. On-line hyphenation with liquid chromatography 419

It is worth mentioning that online LCGCGC systems, which were developed and applied for 420

food analysis as early as 1993, can be achieved by applying an advanced valve switching based 421

system [79]. The example system with the corresponding results for corned beef extract spiked 422

(20 μg kg–1) with methylated stilbene standards is shown in Fig. 9A. The benefit of the 423

LCGCGC system is clearly illustrated by well separated peaks of the target compounds 424

detected at the analytical 2D GC column – refer to the signal from detector (iii). Improved 425

management of derivatised samples is also possible, e.g., by addition of isolated valves for 426

derivatising agents together with programmed backflush of the excess reagents prior to the 427

separation of the derivatised samples [81]. On-line derivatisation LCGC–MS system was 428

found to be sufficient (without requirement for higher dimensional separation techniques such 429

as LCGC–MS for analysis of trimethylsilylated cis- and trans-diethylstilbestrol, hexestrol and 430

dienestrol spiked (6 μg kg–1) in garlic sausage and Danish salami. Selectivity provided by MS 431

in this case can be applied to reduce the complexity in chromatographic analysis. 432

433


435

Recent advances for online higher dimensional separation involves application of sequential 436

GC ovens for preparative scale analysis with GC–GC–GC or LC–GC–GC (Fig. 9B). The 437

system was applied for analysis of target compounds in essential oils with reported recovery of 438

87%, and high purity compounds were obtained with 91% purity [80]. Since some 439

compounds can be prepared as pure components with 1D or 2D separation, analysis time can 440

be reduced by addition of a high temperature valve allowing collection at the outlet of any 441

employed column [82]. It is thus a challenge to apply these technologies in the area of food 442

analysis in the future. Although this approach is applicable for even more than 4 separation 443

stages, the limiting factor appears to be the lack of novel types of stationary phase with 444

significant selectivity difference. 445

446

5.1.2. H/C in 2D space 447

Application of one or more H/C events during GC×GC can be performed, as illustrated by a 448

hybrid instrument design (Fig. 10A), which allows the H/C analysis of specific zones or even 449

individual peaks from the 2D space, during a relatively slow modulation GC×GC experiment, 450

e.g., using a modulation period (PM) of 20 s [15]. A Deans switch (DS) can be employed after 451


the modulation system (with an example shown in Fig. 10A) to perform H/C of modulated 452

peaks during the modulation events resulting in H/C of a single peak of interest in a 2D space. 453

Refer to the processes without and with H/C in Fig. 10B (ii) and (iii), respectively. In this 454

approach, the DS flow switching had to operate very fast (faster than the applied PM), and 455

required several on/off events for the operation of cutting the compound to a downstream 456

column. This technique was applied to isolate target compound regions from the matrix of 457

coffee extract, as illustrated by the experimental result shown in Fig. 10C. In case of co-eluting 458

peaks occurring in GCGC, the H/C peaks can be further separated on the 3D column (see also 459

Fig. 10A). Application of this technique in the area of food analysis in the future will depend 460

on requirements of the analysis, and suitability of the hybrid approach to provide the desired 461

analytical result, especially for target compound analysis in a matrix of S4. 462

463


465

5.2. Multifunctional MDGC systems 466

Hyphenation with olfactometry (O) has a limitation of slow acquisition rate (i.e., determined 467

by natural sniffing rate of the assessor) which may hinder detection of peaks with narrow (a 468

few seconds) peak widths in GCGCO analysis. H/C MDGCO should be performed to allow 469

use of longer 2D column, resulting in broader peak widths (facilitating O detection) and at the 470

same time improving the resolution. Thus, MDGC systems that can operate in either H/C 471

MDGC or GCGC mode as well as with hyphenation to an informative detector such as MS, 472

are useful in olfactometry based analysis. A general multifunctional system hyphenated with 473

both MS and an olfactory port for odour sensing can be proposed [23], as is shown in Fig. 3 474

(top right). The system has been applied for analysis of odour compounds in a wine sample (see 475

S3 in Fig. 3) and coffee extract (see S4 in Fig. 3), where GCGC was applied for improved 476

chemical profiling and untargeted compound analysis (compared with 1DGC), and 477

subsequently MDGC was then selectively applied for further target compound analysis [31]. 478

Furthermore, the system is also capable of on-line enrichment where the wine sample was 479

injected by SPME with 6 replicates into the ‘Trap’ (a cryotrap unit) corresponding to 6-fold 480

cumulative sampling, see H/C results for analysis of S4: 1 SPME (C) vs 6 SPME (D), with the 481

H/C region shown in B (see S4 in Fig. 3), in order to improve the signal of the target capsicum 482

odorant (2-methoxy-3-isobutylpyrazine) the mass spectrum of which is shown in E (see S4 in 483

Fig. 3). The corresponding GCGC result for the coffee extract was also obtained as shown in 484


A (see S4 in Fig. 3). The system was also proposed to be suitable for hyphenation with 485

olfactometry or other detectors with slow acquisition rates. 486

487

Apart from O based analysis, the multifunctional system was also demonstrated for both 488

GCGC and H/C MDGC analysis of FAME in milk and fish oil [29], see also the example 489

results for S2 in Fig. 3. The system with capability for hyphenation with MS, Fig. 3 (top right), 490

can be applied in the future for analysis of any volatile compounds in complex food samples 491

such as characterisation of milk metabolites from different species or that obtained from 492

different processing approaches which may be important, e.g., to understand the role of milk in 493

food and health and to track the post manufacturing process [2]. 494

495

6. Concluding remarks 496

High-resolution GC techniques play an important role in food analysis. Approaches that provide 497

greater separation power than 1DGC, such as a range of MDGC methodologies, should be 498

increasingly attractive to provide various desirable goals for analysis of volatile and semi-499

volatile compounds, particularly for identification with high confidence. Whilst MDGC 500

technologies will not be available in all laboratories, deciding to use a MDGC system and 501

approach clearly depend on compromise between user aims (e.g., analysis quality and level of 502

compound detail required) and expense of time, system complexity and analysis cost. Clearly, 503

adoption of more advanced methods in a routine laboratory will require a significant investment 504

in time, cost, education and commitment. However, the full capabilities of these techniques are 505

expected to be more widely applied in the future following the growing need to characterise 506

food samples more completely, the introduction of new types of food, to monitor foods for 507

toxicity or adulteration, as well as the increasing demand for chemical information to meet more 508

stringent new regulations, or discovery of benefits of chemical species. Increased application 509

of MDGC systems with their accompanying interfaces and H/C devices, together with MS or 510

MS/MS technologies, can be expected within the next decades, and will mimic the trend that 511

has been evident in recent years. In addition application of accurate mass analysis capability is 512

also desirable for improved food analysis such as GC×GC–QTOFMS. Although MS/MS 513

methods and accurate mass MS have not been the focus of this review, the molecular specificity 514

available from MS/MS and accurate mass measurement methods have been attracting much 515

interest in the food analysis area. In addition to the QTOF mass analyser, recent very high 516


resolution MS technology such as the Orbitrap MS with GC will increasingly focus on food 517

applications, and in this respect may also be applied to multidimensional separations. 518

519

7. Acknowledgements 520

We acknowledge funding from the ARC Discovery and Linkage program grants DP130100217 521

and LP130100048. PM acknowledges the Australian Research Council for a Discovery 522

Outstanding Researcher Award, DP130100217. 523

524

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of silica-catalyzed amino acid thermal condensation and products of their thermal 721

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volatile fraction of roasted hazelnuts (Corylus avellana L.) from different origins, J. 735

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774

775


LIST OF FIGURE CAPTIONS 776

777

Fig. 1. Flow chart illustrating data processing and compound identification in food with 778

MDGC, supported by mass spectrometry and / or other detectors. 779

780

Fig. 2. Experimental approaches for calculation of (A) 1D and 2D retention indices in H/C 781

MDGC adapted with permission from [23]. Copyright (2012) American Chemical Society; and 782

(B) 1D and 2D retention indices in GCGC adapted with permission from [18]. Copyright 783

(2015) American Chemical Society. Compounds a–e in (A) are acetic acid, octen-3-ol, ethyl 784

octanoate, hexanoic acid and -damascenone, respectively. 785

786

Fig. 3. Experimental results for H/C MDGC and GCGC for analysis of compounds in different 787

food samples classified according to level of complexity, S1–S4, with a proposed universal 788

MDGC configuration being applicable for these analyses shown at the top right. S1 (vegetable 789

oil fatty acids) and S2 (milk fatty acids and flavonoids in dark chocolate) are adapted from [28], 790

Copyright (2016) with permission from Elsevier, and [29, 30], Copyright Wiley-VCH Verlag 791

GmbH & Co. KGaA. Reproduced with permission, and Copyright (2010) with permission from 792

Elsevier, respectively. Both S3 (wine) and S4 (coffee) are adapted from [31]. Copyright (2015) 793

with permission from Elsevier. Instrumental design is adapted with permission from [23]. 794

Copyright (2012) American Chemical Society. 795

796

Fig. 4. Flowchart and diagram showing the process of compound confirmation in 797

GC×GCaccTOFMS, progressively applying MS library matches and retention index filters to 798

refine the data, adapted with permission from [18]. Copyright (2015) American Chemical 799

Society. 800

801

Fig. 5. GCGC–QqQMS result operated in MRM mode for the analysis of five target pesticides 802

spiked in spearmint oil, at the 1 ppb level. Adapted from [55]. Copyright (2013) with permission 803

from Elsevier. 804

805

Fig. 6. (A) MDGCFTIR/MS configuration: a, injector port; b, 1D Rtx-1701 precolumn; c, 806

make-up He for re-injection; d, trap-selection rotary valve; e, cryogenic traps; f, make-up flow 807

for the analytical columns; g, Rtx-5 column; h, Stabilwax column: V1 cryogenic trap-switching 808


valve: V2, post-trap vent-column-switching valve, and V3, analytical column selection valve 809

applied for analysis of cascarilla bark with (B) the 1DGC result and further 2D analysis of the 810

H/C region of 23–26.2 min (tsamp and 2tR,max = 3.2 and 30 min, 2tR,max/tsamp = 9.4) with (C) FID 811

(obtained by using the same experimental condition), (D) FTIR and (E) MS. Adapted from [65] 812

with permission from The Royal Society of Chemistry. 813

814

Fig. 7. (A) Cumulative chromatogram for nine samples of roasted hazelnuts and the regions of 815

detected peaks (with 411 different features) used for chromatographic fingerprinting indicated 816

within white polygons in the 2D space. (B) Fingerprinting results expressed as % match with 817

the consensus template. Results refer to chromatographic (dashed line) and comprehensive 818

template match fingerprinting with MS approach (solid line). Adapted from [71]. Copyright 819

(2010) with permission from Elsevier. 820

821

Fig. 8. (A) GCGC results for green Arabica coffee extract in ethyl acetate, and (B) group type 822

analysis results obtained from 1DGC, GCGC results with the literature values. The data were 823

normalised based on the total peak areas in each analysis. 1DGC analysis employed a mid-polar 824

Rtx-65TG column (11 m 0.25 mm I.D. 0.1 µm df). The GCGC column set consisted of a 825

1D mid-polar Rtx-65TG column (11 m 0.25 mm I.D. 0.1 µm df) and a 2D non-polar MEGA-826

5 Fast column (1.0 m 0.1 mm I.D. 0.1 µm df). Adapted from [74]. 827

828

Fig. 9. (A) Schematic diagram of LC–GC–GC instrumentation with the corresponding 829

chromatograms illustrating the three stages of separation from LC column (i), 1D GC column 830

(ii) and 2D GC column (iii) for methylated stilbene standards: trans-diethylstilbestrol (a), cis-831

diethylstilbestrol (b), hexestrol (c) and dienestrol (d), and for an extract from corned beef spiked 832

with these standards at 20 μg kg–1 from [79]. Copyright Wiley-VCH Verlag GmbH & Co. 833

KGaA. Adapted with permission. (B) Scheme of the LC–MDGC–prep system reprinted from 834

[80]. Copyright (2015) with permission from Elsevier. 835

836

Fig. 10. (A) Diagram illustrating experimental setup for hybrid GCGC–MDGC, with H/C of 837

peaks separated into 2D space, (B) process of H/C of an example peak B in a 2D space, and (C) 838

3D plots of coffee volatile extract indicating the H/C mechanism applied to 3 individual peaks 839

corresponding to a single compound B in the separation space, adapted with permission from 840

[15]. Copyright (2012) American Chemical Society. 841


Figures: 842

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Figure 2 849

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Figure 3 855

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Figure 4 860

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Figure 5 865

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Figure 10 887

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