20 March 2013 Volume 9 Issue 5 Cover Story

Automated Analysis

Determining glyphosate residues in water and food

2 Automated Derivatization, Cleanup and LC–MS–MS Determination of Glyphosate and AMPA

Norbert Helle and Franziska Chmelka, TeLA GmbH,

Bremerhaven, Germany. A reliable and sensitive determination of glyphosate residues in

water and food is discussed.

Cover Story

Features13 Verifying Data Quality of an Untargeted Approach to Analyse Terroir in Wine Grape Juice Mark Dreyer1, Paul Tarr2 and Michael Athanas1,1Thermo Fisher Scientific,

San Jose California, USA, 2California Institute of Technology, Pasadena,

California, USA.

A careful approach to data handling is important for the understanding of

how certain procedures alter the metabolic profile of samples.

Regulars

9 News

Using breath as an indicator of stress levels, a biomarker for river

blindness is discovered, Agilent supports cancer research and Bruker expands

its reach in India.

11 Incognito A Testing Time... Incognito espouses the benefits of a rigorous testing procedure in the workplace.

18 CHROMacademy

Update on what’s new on the professional site for chromatographers.

19 Training Courses and Events

21 Staff

20 March 2013 Volume 9 Issue 5

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Automated Derivatization, Cleanup and LC–MS–MS Determination of Glyphosate and AMPA

There is widespread interest in the reliable and sensitive determination of glyphosate residues in water and food.

Commonly used direct large volume injection (LVI) methods without sample cleanup are severely infl uenced by

matrix, making accurate determination in food very diffi cult. Derivatization techniques can be used successfully

for water samples, but food samples still require solid-phase extraction (SPE) cleanup. The method presented here

combines derivatization with 9-fl uorenylmethyl chloroformate (FMOC-Cl) with cleanup on a high-pressure SPE system

and liquid chromatography–tandem mass spectrometry (LC–MS–MS) determination in one automated procedure.

Results for water, wheat, tea and honey are presented with limits of detection (LOD) in water below 0.01 μg/L.

Norbert Helle and Franziska Chmelka, TeLA GmbH, Bremerhaven, Germany.

Glyphosate-based broad spectrum

herbicides are among the most widely used

crop protection products in the world.

Herbicides containing glyphosate are used

by agricultural businesses to clear fi elds of

weeds and other competing plants before

sowing to ensure maximum crop yield.

Glyphosate does not target particular

plants, but does destroy virtually any

plant except those bred to be resistant to

it. Integrated in a formulation that aids

distribution from root to leaf, glyphosate

[N-(phosphonomethyl)glycine] radically

targets a plant’s metabolism by bonding

manganese, an essential element for all

organisms. This, in turn, disrupts synthesis

of the enzyme 5-enol-pyruvyl-shikimate-

3-phosphate (EPSP) synthase. The plant in

question is subsequently deprived of essential

metabolites and dies.

The glyphosate business is booming

internationally thanks to transgenic crops

such as corn and soy. Typically, the genetic

make-up of these plants is altered to make

them resistant to glyphosate. Simply put, if

more transgenic plants are cultivated then

more glyphosate will be used.

Additionally, people use glyphosate for

gardening at home in signifi cant quantities

because it eliminates a lot of tedious and

back-breaking work such as weeding out

fl owerbeds. Products that contain glyphosate

are readily available in garden centres,

hardware stores and on-line.

The success of glyphosate has been

attributed to its assumed environmental

compatibility and limited half-life in the

environment. According to the US-EPA,

glyphosate readily and completely

biodegrades, even under low temperature

conditions with an average half-life in soil

of approximately 60 days and in water of

just a few days, part of which can probably

be attributed to adsorption on surfaces.1

However, a recent report has indicated

that glyphosate may affect the agricultural

ecosystem. The contention is that the absence

of wild weeds and herbs after glyphosate use

2

Helle and Chmelka2 News9 Incognito11 Dreyer et al1399 11CHROMacademy18 Training & Events19 Staff21

The Column www.chromatographyonline.com

reduces biodiversity in the food chain and is suspected of making plants more susceptible to diseases and of reducing the availability of nutrients for plants. This, in turn, could trigger an increased reliance on pesticides and fertilizers. According to the same report, more herbicide will ultimately be used in the medium term because more weeds are developing resistance to glyphosate.2 For these reasons, environmentalists are demanding that the widespread herbicidal use of glyphosate be addressed with renewed urgency, pointing to plans for cultivating glyphosate-resistant, genetically modifi ed plants in Europe.

Conversely, Germany’s infl uential Federal Institute for Risk Assessment (BfR) has concluded that there is no risk to human reproduction as alleged by environmentalists. According to the BfR: “Not only the EU but also the WHO and the US-EPA have come to the conclusion that glyphosate is detrimental neither to the reproduction nor the development of mammals including humans.”3

Whether there is cause for alarm or not, the increased awareness has led to growing demand for reliable and sensitive determination of glyphosate. Monitoring

OHOH

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OH

OH

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HO

NH

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OO

OH

HO

P

HO

OHP

O

O

O O

N

Glyphosate

AMPA

FMOC-CI

FMOC-CI

Glyphosate–FMOCMonoisotopic mass = 391

AMPA–FMOCMonoisotopic mass = 333

NHO

O

P +

+

Figure 1: Structural representations of glyphosate, AMPA and their FMOC-derivatives.

Helle and Chmelka

33

Helle and Chmelka2 Helle and Chmelka2 News9 Incognito11 Dreyer et al13 Dreyer et al131399 News99 11 Incognito11CHROMacademy18 CHROMacademy1818 Training & Events19 Training & Events1919 Staff21 Staff21


them for higher throughput, lower limits of

detection (LOD) and improved accuracy while

using standard systems. In particular, we

wanted to improve method sensitivity using

a concentration step. Because of their high

polarity, glyphosate and its main metabolite,

aminomethylphosphonic acid (AMPA) are

diffi cult to analyse directly using HPLC.

However, the compounds can be derivatized

using 9-fl uorenylmethyl chloroformate

(FMOC-Cl) as described in method DIN/

ISO 214587,4 which has been used for this

would apply both to agricultural products,

for food safety reasons, and to environmental

samples such as soil and water.

Analysis Method

Glyphosate and its most important

metabolite, aminomethylphosphonic acid

(AMPA), are generally determined using

high performance liquid chromatography

(HPLC) coupled with multidimensional

mass selective detection (MS–MS). When

we developed our method, we wanted to

achieve liquid chromatography–tandem

mass spectrometry (LC–MS–MS) trace-level

determination of glyphosate and AMPA in

water using direct injection. The focus was

not on copying existing methods, but rather

on developing them further and automating

Overlay chromatograms of Glyphosate and AMPA in wheat matrix at 0.01 and 0.1 mg/kg respectively after derivatization and cleanup

x10

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8.5

8.75-ESI MRM Frag=80.0V CID@** (390.1000 -> 168.0000) GLY-AMPA-532.d Smooth

1 22 33 44

Counts vs. Acquisition Time (min)

2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5

0.5

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-ESI MRM Frag=80.0V CID@** (332.2000 -> 109.9000) GLY-AMPA-532.d Smooth

1 22 33 4

Counts vs. Acquisition Time (min)

2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5

Figure 3: Chromatograms resulting from the extraction of spiked wheat samples. Glyphosate concentrations 0.01 mg/kg (red trace) and 0.1 mg/kg (green trace), respectively. AMPA concentrations 0.01 mg/kg (green trace) and 0.1 mg/kg (red trace), respectively. Mass transitions monitored for glyphosate: 390 --> 168 and 390 --> 150; for AMPA 332 --> 136 and 332 --> 110.

Glyphosate FMOC - 4 Levels, 4 Levels Used, 5 Points, 5 Points Used, 0 QCs

Concentration (ng/ml)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

y = 7881.396964 * x + 196.319238R^2 = 0.99913072

AMPA FMOC - 3 Levels, 3 Levels Used, 4 Points, 4 Points Used, 0 QCs

Concentration (ng/ ml)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Re

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0.5

1

1.5

2

2.5

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4

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Re

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0

0.5

1

1.5

2

2.5

3

3.5

y = 9553.422022 * x - 287.590988R^2 = 0.99976155

Glyphosate-FMOCcalibration curve(peak areas)

AMPA-FMOCcalibration curve(peak areas)

Figure 2: Calibration curves for Glyphosate-FMOC (top) and AMPA-FMOC (bottom) in water. R2 for both curves were >0.999 from 0.1 –5 ng/mL.

Helle and Chmelka

4



analysis based on HPLC with fl uorescence

detection for many years. Derivatization in

our case was performed using a combined

multipurpose autosampler and liquid

handling robot (MultiPurpose Sampler,

Gerstel). While derivatization combined

with direct injection to the analysis system

is used successfully for water samples, food

samples require additional SPE cleanup.

Commonly used direct large volume injection

(LVI) methods without sample cleanup

are severely infl uenced by matrix, making

accurate and reliable determination in food

very diffi cult. The method presented here

combines derivatization using FMOC-Cl with

cleanup on an on-line high pressure SPExos

SPE system (Gerstel) positioned between the

autosampler and the 1290 UHPLC system

(Agilent Technologies).

The SPE system used has small cartridges

with only 50 mg of sorbent, which allows

elution to be carried out with small volumes

of solvent.

Automated sample preparation takes

20 min and is performed simultaneously

with the ultrahigh-performance liquid

x10

2

0.5

0.75

1.25

1.5

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7

6

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8

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8.751 2 2 3 3 4

Counts vs. Acquisition Time

2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5

-ESI MRM Frag=80.0V CID@** (390.1000 -> 168.0000) GLY-AMPA-531.d

Glyphosate

Figure 4: Overlay chromatograms showing glyphosate in tea spiked at 0.01 mg/kg and 0.1 mg/kg respectively, dilution 1:125. Mass transitions monitored for glyphosate: 390 --> 168 and 390 --> 150.

Helle and Chmelka

5



chromatography (UHPLC) analysis run of the

preceding sample. This means that overall

analysis time for each sample in a batch of

samples is 20 min. The timing of the sample

preparation can be controlled to ensure that

samples are injected at exactly the point in

time when the HPLC run of the previous

sample has fi nished. A 6460 TripleQuad mass

spectrometer (Agilent Technologies) was used

as MS–MS detector. The FMOC derivatives

of glyphosate and AMPA were detected in

negative ion mode [electrospray ionization

(ESI)]. The mass transitions monitored were

390 Da to 168 Da and 390 Da to 150 Da for

glyphosate; for AMPA, 332 Da to 136 Da and

332 Da to 110 Da were monitored.

Sample Preparation:

Extraction: Blended wheat was extracted

with acidifi ed water, neutralized and

diluted with water. Honey was dissolved in

water at approximately 60 °C. Soil samples

were extracted using water. Blended tea

leaves were extracted with acidifi ed water,

neutralized and diluted with water.

Derivatization: Following extraction, extracts

were placed in the autosampler, which

added derivatization reagent to each extract

and fi nally injected a 1000 µL aliquot of


4.2

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x10

2

1 2 2 3 3 4

-ESI MRM Frag=80.0V CID@** (332.2000 -> 135.8000) GLY-AMPA-716.d

Glyphosate

AMPA

Figure 5: Chromatogram resulting from the extraction of a soil sample from Tenerife. Glyphosate concentration found: Approximately 740 µg/kg; AMPA concentration approximately 17 µg/kg. The two traces shown for each compound refl ect the two transitions monitored for each compound.

Helle and Chmelka

6


Chromatography Online: A Trusted Resource for Separation ScientistsLCGC’s website, www.chromatographyonline.com, spans across the world providing original and fresh material across all key segments including food analysis, bioanalysis, environmental analysis, pharmaceutical analysis and petroleum and biofuels research.

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ES212545_LCTC032013_V6.pgs 03.12.2013 23:24 ADV blackyellowmagentacyan


ª�Rinse the cartridge with 2 mL of water.

ª�Introduce 1000 μL of sample.

ª�Wash the cartridge adsorbent bed with

1100 μL of water.

ª�Switch the SPE system from MPS mode,

that is, sample preparation and extraction

mode, to injection mode for transfer of

the eluate to the HPLC system.

ª�Elution and derivatization of the next

sample.

All steps were fully automated. The sample

preparation was completed in 20 min.

The HPLC–MS–MS analysis cycle time was

around 20 min.

the extract into the on-line SPE system for

cleanup.

Solid-Phase Extraction and On-line

Sample Introduction: Listed below are the

individual sample preparation steps that

are performed synchronizing automated

derivatization and SPE cleanup with the LC–

MS–MS analysis. During LC–MS–MS analysis

of a sample, the next sample is prepared so

that it is ready for injection when the LC–

MS–MS system becomes ready:

ª�Load the on-line SPE cartridge.

ª�Condition the cartridge with 1 mL of

methanol.

4

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6

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3

2 3 3 41 2

-ESI MRM Frag=80.0V CID@** (332.2000 -> 135.8000) GLY-1069.d

Glyphosate


2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17 17.5 18 18.5 19 19.5 20 20.5 21 21.5 22 22.5 23 23.5

Figure 6: Chromatogram resulting from the extraction of a one-year-old honey sample from an area where glyphosate is being used. Glyphosate concentration found: Approximately 4 mg/kg, 80 times the allowable concentration; AMPA concentration: Not detected. The two traces shown for the compound refl ect the two transitions monitored for reliable detection. According to EU regulation requirements, the glyphosate concentration in honey must not exceed 0.05 mg/kg.

Helle and Chmelka

7


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Results and Discussion

The analysis method enables the

determination of glyphosate and AMPA

in wheat, water, tea leaves and honey at

suffi ciently low limits of detection and

limits of quantitation to meet EU guideline

requirements as shown in Table 1. In

Figure 2, calibration curves for glyphosate

and AMPA in water are seen. For both

compounds, R2-values of > 0.999 are

reached.

Conclusion

The presented automated method for

derivatization, on-line SPE and

HPLC–MS–MS determination of glyphosate

and its most important metabolite

aminomethylphosphonic acid (AMPA) was

successfully implemented for a variety of

samples, including: spiked water samples, tea

leaves, wheat, honey and soil. Determination

of a dilution series produced excellent

linearity in the order of 0.999 and low limits

of quantitation (LOQ) of 10 ng/L for both

glyphosate and AMPA in water. Recovery of

glyphosate and AMPA in water, wheat and

tea leaves was found to be in the range of

90% to 105%.

The automated method resulted in LOQs

below 1 μg/kg for both glyphosate and

AMPA in wheat, tea and honey, easily

meeting EU and US requirements. The

same LOQs were reached for soil samples.

Variation coeffi cients reached using the

automated system were lower than those

typically achieved using manual procedures

since the timing of each individual step

is accurately and uniformly controlled for

all samples so that any decomposition

of derivatized analytes would not impact

accuracy.

We would like to emphasize that the AMPA

concentrations found in non-spiked real

samples of soil and honey were surprisingly

low relative to the glyphosate concentrations.

This indicates that glyphosate could be more

stable than is widely assumed. The same was

found to be the case in real-world samples of

wheat that we have analysed.

In future research, we will pursue the

automation of further methods that currently

require manual liquid sample preparation and

SPE cleanup.

References

1. US EPA “Technical Fact Sheet on: GLYPHOSATE”

(http://www.epa.gov/ogwdw/pdfs/factsheets/

soc/tech/glyphosa.pdf)

2. Greenpeace Report, Herbicide tolerance and GM

crops, www.greenpeace.de/fi leadmin/gpd/user_

upload/themen/gentechnik/Herbicide_tolerance_

and_GM_crops_lo_res.pdf

3. Frequently Asked Questions on the Health Risk

Assessment of Glyphosate, BfR FAQs, November

11, 2011; http://www.bfr.bund.de/cm/349/

frequently-asked-questions-on-the-health-risk-

assessment-of-glyphosate.pdf

4. International Organization for Standardization

(ISO) Technical Committee ISO/TC 147, Water

quality, subcommittee SC2, Physical, chemical

and biochemical methods: Determination of

Glyphosate and AMPA using high performance

liquid chromatography (HPLC) with fl uorescence

detection.

Norbert Helle, PhD, is the owner and

general manager of TeLA GmbH, a contract

laboratory in the fi eld of food safety

analysis. Norbert Helle has more than 15

years experience working in food safety and

environmental analysis for various German

Federal and State agencies, mainly in the fi eld

of HPLC–MS. He chairs two working groups

under the German § 64 Foodstuffs and

Commodities Act charged with developing

food safety analysis methods for phycotoxins

and biogenic amines.

Franziska Chmelka is the Head of

Research and Development at TeLA GmbH,

including method development on HPLC–

MS and HPLC–MS–MS instruments for the

determination of residues of pesticides,

biotoxins and drugs. She oversees work on

various German Federal research projects

on preparative and analytical HPLC–MS and

HPLC–MS–MS work in the area of food

safety.

E-mail:[email protected] Website: www.tela-bremerhaven.de

Sample LOQ LOD

Wheat < 1.0 μg/kg < 0.3 μg/kg

Water < 10 ng/L < 3.0 ng/L

Tea leaves < 1.0 μg/kg < 0.3 μg/kg

Honey < 1.0 μg/kg < 0.3 μg/kg

Note: Maximum Residue Level (MRL) in the EU for glyphosate in wheat: 10 mg/kg (US: 30 mg/kg). The LOQ achieved here is substantially lower.

EU regulations require glyphosate levels in water to be below 100 ng/L (US: 700 μg/L*). The LOQ achieved using the presented method is substantially lower.

Maximum Residue Level (MRL) in the EU for glyphosate in tea leaves: 2 mg/kg (US: 1 mg/kg). The LOQ achieved here is substantially lower.

The MRL in honey according to EU regulations is 0.05 mg/kg. As can be seen, the LOQ achieved using the presented method is substantially lower. * http://water.epa.gov/drink/contaminants/index.cfm

US EPA: Drinking Water Standards Maximum Contaminant Level (MCL): 0.7 mg/L

USDA: MRL Wheat: 30 ppm (EU: 10 ppm)

Table 1: The limit of quantifi cation (LOQ) and limit of detection (LOD) of glyphosate and AMPA in wheat, water, tea leaves and honey samples.

Helle and Chmelka

8


Agilent supports cancer research centerDr Ronald A. DePinho — president of the University of Texas MD Anderson Cancer

Center (Texas, USA) — has received an Agilent Thought Leader Award. Agilent

Technologies (California, USA) will provide financial support for personnel as well as

instrumentation for the center.

The work of DePinho has resulted in the development of improved cancer detection

methods and cancer drug development. His research group focuses broadly on

basic-to-translational research programmes for brain, colorectal, pancreas and prostate

cancers, as well as ageing and neuro-degeneration.

The award will support DePinho and his team in their research into reprogramming

the metabolome at the initial stages of cancer. Specifically, the instrumentation

will “enhance” the investigation of metabolic adaptions during the development of

pancreatic cancer, according to Dr Guilio Draetta, director of the Institute for Applied

Cancer Science.

Pancreatic cancer is a major concern of DePinho’s group because survival rates of

patients have not significantly improved over the past two decades. Depinho said:

“This technology will allow us to rapidly identify new targets that drive the formation,

progression and maintenance of pancreatic cancer. Discoveries from this research

will also lead to the development of effective early detection biomarkers and novel

therapeutic interventions.”

Patrick Kaltenbach, Agilent VP and general manager of the Liquid Phase division,

said: “We are proud to support Dr DePinho’s exciting translational research

programme, which will make use of metabolomics and integrated biology workflows

and solutions in biomarker discovery.”

Draetta added: “Armed with novel biomarkers, earlier diagnosis and treatment will

be possible, leading to improved patient outcomes. Additional opportunities for novel

therapeutic intervention will also emerge from this work.”

For more information please visit: www.agilent.com

Bruker expands Indian operationsBruker (Massachusetts, USA) has built on its 30-year history in India by opening

two new centres of excellence in Mumbai and Bengaluru. During this time,

Bruker has established itself in many of India’s major cities including New Delhi,

Mumbai, Bengaluru, Chennai, Kolkata, Lucknow and Hyderabad.

The new centres of excellence will offer customer support for scientifi c and

analytical instrumentation for the growing Indian market. The locations of

the centres were selected based on their proximity to major academic,

government and industrial research laboratories.

Dr Frank Laukien, Bruker’s President and CEO, commented: “With this

signifi cant investment

in our two new centres

of excellence, Bruker

has taken a major step

forward in India to provide

highest-level support

for the outstanding and

rapidly growing Indian

research, industrial and

clinical communities.”

For more information,

please visit: www.bruker.

comPh

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9



Biomarker for river blindness discoveredScientists at The Scripps Institute (California, USA) have used liquid chromatography–mass spectrometry (LC–MS) to identify a new biomarker for the diagnosis of river blindness.1 The discovery is an important step towards the development of a portable diagnostic test for use in the fi eld.

River blindness affects 37 million people worldwide and is focused specifi cally in sub-Saharan Africa, Central and South America and Yemen. The disease is caused by nematode (Onchocerca volvulus) larvae transmitted by blackfl y (Simulium sp.). In an attempt to kill the larvae, the body mounts an infl ammatory immune response, indirectly damaging the eye and other body tissues.

The team analysed the urinary metabolome of infected and uninfected patients, identifying one particular protein that became elevated in the urine of infected patients. High performance liquid chromatography (HPLC) was performed to concentrate the protein and LC–MS was able to identify the chemical structure as N-acetyltyramine-O,ß-glucoronide.

The analysis was not as straightforward as initially hoped. The small, then unknown, molecule was not immediately identifi able. Daniel Globisch, a postdoctoral fellow in the Janda laboratory, studied the available literature referencing the metabolome of the nematode and pinpointed the metabolite

source. The molecule was a breakdown product of a neurotransmitter produced by young larvae, excreted into the urine at higher

levels during an active infection.“It’s a spectacular fi nd in terms of biomarkers as it does not occur naturally in humans,” said Globisch. Lead

investigator Professor Janda advanced on this stating that for the fi ndings to be of value in Third World countries it would need to be developed into a portable instrument, “basically distilling our fi nding to a test that can be carted around in a backpack”.

According to the authors, the theory behind the investigation should be applicable to the diagnosis of

other worm infection-induced diseases.

Reference1. D. Globisch, A. Moreno, M.S Hixon, A.A.K. Nunes, J.R. Denery,

S. Specht, A. Hoerauf and K.D. Janda, PNAS, DOI: 10.1073/

pnas.1221969110 (2013).

GC–MS detects stress levelsBreath may be an indicator of stress levels according to a study published in the Journal of Breath Research.1

The breath samples of 22 individuals were analysed by performing thermal desporption–gas chromatography–mass spectrometry (TD–GC–MS) to pinpoint six stress sensitive compounds.

Stress is the by-product of the evolutionary “fight-or-flight” response. In a situation percieved to be challenging, or in some cases threatening, the body’s biological processes change in preparation. The most obvious change induced by fear or anticipation is an increase in heart rate and blood pressure.

The study hypothesized that this would result in a change in the chemical profile of breath samples. These samples were collected from subjects following psychological interventions — designed to either induce stress or maintain a relaxed state of mind. One intervention used was a mental arithmetic test, while another was listening to classical music.

The four compounds identified to be elevated following induced stress were indole, 2-hydroxy-1-phenylethanone, benzaldehyde and 2-ethylhexan-1-ol. 2-methylpentadecane was tentatively identified and another componenent remained undetermined.

It is plausible that this non-invasive test could be used in clinical settings to determine the stress levels of patients.

Reference 1. M.A. Turner, S. Bandelow, L. Edwards, P. Patel, H.J. Martin, I.D. Wilson

and C.L.P. Thomas, Journal of Breath Research, DOI: 10.1088/1752-

7155/7/1/017102 (2013).

Ph

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: St

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10


11


So there I was in the laboratory

discussing an analytical application with

a young trainee when his supervisor

approached and commented that

the neophyte was undergoing some

“analyst validation” and was a little

nervous because they were “doing their

assessment”. I’m not a fan of the term

“analyst validation” but I smiled as the

background radio played “Learning to

Fly” by Tom Petty and Heartbreakers

— not a particular favourite of mine (a

little too “soft rock”), but very fitting

nonetheless.

I get more and more requests across

my desk for staff to attend “webcast

training”. Now to some this may seem

pedantic, I return every note with a

considered yea or nay, but always with

the word “training” scored out and

replaced with “development”. To even

describe some of these webcasts as

“development” is questionable.

My question which ties these two

situations together is this: in this

information-rich age, what is the best

way to translate all of this information

into business advantage via our analytical

staff at all levels of the business? Of

course, whilst business advantage

ultimately means profit, there are other

“softer” factors such as efficiency,

compliance, competitiveness and

technology advantage. What training

and development is really necessary or

most effective and how do we harness

it properly? Most important of all,

how do we verify that learning has

happened?

We need to realize first that

information isn’t knowledge and that

knowledge isn’t skills or aptitude — the

latter can only happen if the former is

delivered effectively in each case. This

cascade and the verification of learning

at each stage is badly done in our

organization and I could say the same

about many organizations the world over.

Our regulators (irrespective of industry

type) love statements such as “trained Ph

oto

Cre

dit

: A

nth

on

y M

ars

lan

d/G

ett

y I

ma

ge

s

A Testing Time…

Incognito espouses the benefi ts of a rigorous testing procedure in the workplace.


12


to the correct level of expertise to carry

out analysis”, “periodic retraining”, “have

the correct knowledge”, “demonstrate

competent performance”, and my

favourite — “continued competence must

be monitored, for example, using quality

control techniques.”

So here’s my problem: If you can’t

get someone to demonstrate skills

or knowledge they can’t be verified,

and areas for improvement don’t get

highlighted and problems therefore

don’t get fixed.

Let’s look at the world from

the perspective of an “average”

laboratory-based analytical chemist.

You have access to vendor training and

application information, independent

face-to-face training events, web-based

training courses, live web training,

webcasts, internal training courses,

the experience of other analysts and

managers, trade and academic journals

and magazines and so forth. Which of

these do you participate in? How do

know that these make your performance

better? How does your business know

that these things are improving your

knowledge or skills?

Back then to “analyst validation” —

it’s such a crass description that it raises

a smile — however, the demonstration

of competence that it provides is

exactly what our regulators require

and what our businesses need in order

to gain that extra advantage. And it’s

success depends largely on you being

“examined” to some extent on how

good you are, or how much you

understand about your job. We typically

go through an exercise in which the

learner carries out a “test protocol”. For

high pressure liquid chromatography

(HPLC) this involves correctly making

up an eluent (which tests skills and

knowledge in gravimetry and volumetry),

setting up the instrument and data

system and performing an analysis on a

test solution in which the result has to

fall within certain limits. Combine this

with testing of the underlying knowledge

and one begins to move towards

a system in which competence is

demonstrable. Other organizations

have similar training using specific

analyses which the analyst will perform,

with “test” solutions of known

concentration.

The key to this approach is being

tested, examined, questioned and

challenged in a rigorous but fair manner

and being able to demonstrate that you

can perform to a required standard.

There should be no wriggle room, no

“grandfathering”, no going through

on “a nod and a wink”, no benefit of

the doubt. Many of us shy away from

rigorous work-based performance

testing of our staff — why? We produce

data which may affect people’s health,

wellbeing or standard of life. Why

should we not expect to meet exacting

standards?

How many webcasts have you ever

attended that have a mandatory test

at the end? How many training courses

which have given you a certificate of

attendance, which is duly filed into your

training record without a subsequent

practical or theory examination. I suspect

the answer is pretty much every

webcast or course you ever attended

after your academic study. Then ask

yourself if this helps with demonstrating

that you learned anything, or to what

extent they improve your laboratory

performance? My experience is certainly

that “testing” focuses the mind,

highlights what you are not capable of or

do not know and allows these gaps to be

filled. Good testing needs to have many

facets — multiple choice questions

Contact author: IncognitoE-mail: [email protected]

alone tend not to be so effective! Verbal

reasoning, demonstration, inference,

analysis and synthesis (of concepts

not chemicals!) are all necessary

depending upon what is being studied

and to what level.

Our “analyst validation” programme

contains many of the testing elements

described above, and whatever name you

chose for it, it does validate competence

and demonstrate ability and knowledge.

Yes, people do get nervous about

their “test”. Yes, it does cause the odd

dispute, because, quite frankly, not

everyone passes the “test” first time,

and some not at all. But we will

continue to rigorously test the skills,

knowledge and aptitude of our staff, I’m

even thinking of writing post-webcast

questions for those folks who request

time to attend these events. The strains

of Tom Petty have long since subsided —

but why not aim for the sky in training

and development. Long live testing — it’s

what we do every day.

Incognito

Verifying Data Quality of an Untargeted Approach to Analyse Terroir in Wine Grape Juice Mark Dreyer1, Paul Tarr2 and Michael Athanas1, 1Thermo Fisher Scientifi c, San Jose,

California, USA, 2California Institute of Technology, Pasadena, California, USA.

Data quality and instrument performance can be easily monitored using several high performance liquid chromatography–mass spectrometry (HPLC–MS) parameters. This allows for the possibility of large-scale data acquisition within one experiment to assess metabolic changes over long time periods or during any number of sample processing steps. In addition, when analysing raw data, taking the time to look at how the data is affected by different processing techniques enables one to gain a better understanding of the changes within the samples and how post-processing can manipulate the results. Careful attention needs to be paid to ensure that data processing does not remove or minimize important data points that may be critical for the understanding of how certain procedures alter the metabolic profi le of samples.

a particular target class of molecules within

wine, must or juice samples. More recent

studies have begun to use high-resolution

mass spectral techniques to take an

untargeted approach to understand the

impact of oak wood on the fi nal wine product

to characterize the surface active aerosols

of champagne wines, and to characterize

varietal, origin, vintage and quality attributes

in wine.

The article, “A Metabolomics-based

Approach for Under-standing the Infl uence

of Terroir in Vitis Vinifera L.”1 aims to

understand how the metabolomic

signatures are infl uenced by terroir

(cultivation conditions on both the

micro and macro-environmental scale)

and varietal character (the genetic

determination of grape berry

physiology). Using high pressure liquid

chromatography (HPLC) coupled in

tandem with a high resolution mass

spectrometer, we analysed 41 samples of

various varietal compositions from several

designated AVA’s (American Viti-cultural

Areas) in Santa Barbara County, USA. In this

Wine is a complex mixture that is infl uenced

by many genetic, environmental and

production factors. Strategies for consistent

and robust qualitative and quantitative analysis

at various stages during the production

sequence is becoming increasingly important

in viticulture for both the production of wine,

table grapes, juice and raisins.

Much of the chemical analysis has focused

on the identifi cation of certain classes of

compounds in a wine sample, primarily the

phenolic compounds. While informative,

these studies are limited to understanding Ph

oto

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Afa

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/Ge

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13



(a)

(b)

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

9.5500

9.5700

9.5900

9.6100

9.6300

9.6500

9.6700

9.6900

Sample Number50403020100


Mas

s Er

ror

(pp

m)

Rete

n�on

Tim

e (m

in)

y = 0.0048x + 1.4066

y = 0.0008x + 9.6152

(c)

(d)


Retention Time (min)8.35 8.40 8.45 8.50 8.558.308.258.208.158.10

Mas

s Er

ror

(pp

m)

0.0100

0.0150

0.0200

0.0250

0.0300

0.0350

0.0400

0.0450

0.0500

0.E+00

1.E+08

2.E+08

3.E+08

4.E+08

5.E+08

6.E+08

7.E+08

8.E+08

9.E+08

1.E+09

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

Peak

Wid

th (

min

)

Peak

Inte

nsi

ty (

cou

nts

)

Figure 1: Various characteristics of the spiked internal standard nordiazepam are measured over the course of the 41 sample/52 h experiment. All parameters are plotted using acquisition time as the x-axis.: (a) The mass error for nordiazepam using positive ionization mode, in PPM, calculated using the formula ∆M/M * 1000000, illustrates that the mass drift, while slightly negative, is well within the instrument specifi cation of ±3 PPM and the slope of the line is 0.0014. (b) Chromatographic retention time showing a very slight positive drift of approximately 0.045 min or 2.7 s (slope = 0.0303) over the course of the experiment. (c) The nordiazepam peak width had a negligible change over the timeframe of this experiment ~-0.0002 min. (d) The error in PPM of a concentrated peak, quercetin, is plotted along with the mass error in PPM, highlighting that there is not an effect of peak intensity on the mass error when the instrument is properly setup.

Dreyer et al

1414

Helle and Chmelka2 Helle and Chmelka2 News9 Incognito11 Dreyer et al13 Dreyer et al131399 News99 11 Incognito11CHROMacademy18 CHROMacademy1818 Training & Events19 Training & Events1919 Staff21 Staff21


ISs nordiazepam (m/z 271.0633) for positive

polarity and chloramphenicol (m/z 323.0196)

for negative polarity. The mass stability was

assessed for the base peak mass in each

sample during the run. Only the positive

data will be presented here because of space

considerations.

Understanding how viticultural decisions

impact the metabolome of grape berries

and the vinifi cation of grape juice is of great

interest to the grape and wine industry.

To begin to understand the relationship

between each of the samples analysed, we

focused on hierarchical clustering analysis.

Following standard convention data from

published studies, we normalized the data

output from SIEVE software for differential

expression (Thermo Fisher Scientifi c) and LOG

transformed this to better visualize the entire

data set. This allowed us to visualize all the

data points at the extremes of the data set in

the fi nal graphic output.

However, we found this data smoothing

approach minimized component information

by diminishing the infl uence of highly

discriminating data points within the complete

data set. To illustrate this point, Figure 2(a)

and (b) show the hierarchical clustering

results from the terroir grape juice subset

of the 41 data samples processed by LOG

transformation followed by hierarchical

clustering [Figure 2(a)], or hierarchical

clustering of the original data set with no

LOG transformation [Figure 2(b)]. While the

LOG transformed data set displays more

of the components from the data set, it

article we outline the procedures that were

used to assess the data quality following

the metabolomic analysis of the chemical

composition from the 41 samples. To ensure

that data quality remained high across

extended chromatographic runs, several

methods to analyse instrument performance

were chosen and monitored to ensure

consistent results. These parameters included:

HPLC peak retention time, chromatographic

peak width, base peak mass accuracy of the

internal standard and data transformation.

Experimental

Chromatography was performed using an

Ultimate 3000 HPLC system (Thermo Fisher

Scientifi c), injecting 10 μl directly onto an

Accucore C18 column (Thermo Fisher Scientifi c)

(100 × 2.1 mm, 2.6 μm particle), equilibrated

in 95% solvent A (0.1% aqueous solution

of formic acid), 5% solvent B (methanol

containing 0.1% formic acid). The HPLC

column temperature was maintained at 40 °C

and the autosampler sample compartment

at 10 °C. The compounds were eluted from

the column using a fl ow rate of 300 μL/min

by linearly increasing solvent B concentration

from 5% to 95% over 15 min. The column was

then washed with 95% solvent B (2 min) and

re-equilibrated in 95% solvent A, 5% solvent B.

The total run time, including column wash and

equilibration, was 20 min.

Mass spectrometry was performed using

a Q Exactive mass spectrometer (Thermo

Fisher Scientifi c) operated using electrospray

with positive and negative polarities

at 70,000 resolving power (defi ned as

FWHM @ m/z 200), IT = 250 ms and AGC

Target = 1,000,000, for full scan analysis

(mass range 70–1000 amu). Source conditions

for both ionization polarities were: spray

voltage: 3.5 kV; sheath gas: 45 arb units;

auxillary gas: 20 arb units; sweep gas: 2 arb

units; heater temperature: 450 °C; cap

temperature: 300 °C; S-Lens RF level: 50. The

instrument was calibrated using Calibration

Solution (Thermo Fisher Scientifi c) before

beginning the analysis. Nordiazepam and

chloramphenicol were added to each sample

as an internal standard at a fi nal concentration

of 10 ng/mL.

Samples were run in positive mode

followed by a duplicate run in negative mode,

using the same parameters as positive to

maximize the data collected in each polarity.

For each mode, solvent blanks containing the

internal standards (ISs) were run before and

between each sample set of fi ve samples.

In an effort to assess the data quality,

we looked at several measures of HPLC

stability, mass spectrometer stability and

mass accuracy. To assess the stability and

reproducibility of the HPLC, we looked at

the base peak mass retention time of the

(a) (b) 0.0 10 .0

EC

R-G

r

WR

-Gr

WR

-HH

-Sy

WR

-FM

-Sy

WR

-LB

-Sy

EC

R-G

r

WR

-HH

-Sy

WR

-FM

-Sy

WR

-Gr

WR

-LB

-Sy

Figure 2: (a) The hierarchical clustering result obtained from the LOG transformed data set. (b) shows the hierarchical clustering result from the average peak intensity values obtained from the software output.

Dreyer et al

15



misses the fact that there are two statistically

distinct sets of components in the data

set [see Figure 2(b)], not the high values

at the top and bottom of the clustering

plot. Because the differential expression

software requires a reference to be selected

before chromatographic peak alignment

and component extraction, the output from

the software is normalized, to allow for

direct statistical analysis. The result obtained

from LOG transformation and subsequent

hierarchical clustering demonstrated that

varietal character was a dominant inf uence

in the clustering of the samples with a minor

inf uence of terroir [Figure 2(a)].

However, hierarchical clustering of the

average peak intensities from the normalized

software output shows a more complex

interaction where distinct clusters in the data

set are inf uencing the data so that varietal

character is no longer the dominant factor

[Figure 2(a) vs. Figure 2(b)]. From a statistical

perspective this is the correct approach

because prior data smoothing runs the risk

of eliminating important clusters within the

data. Further analysis of the data is needed

to determine what components of the data

set are responsible for inf uencing similarities

and differences in these complex metabolome

data sets. Of particular interest will be the

analysis of the full diverse 41-sample data

set to determine what component clusters

are linked to values such as varietal character

or terroir. Molecular identif cation of these

components promises to identify the origin

or phase of the viticultural and vinif cation

process from which these metabolomic

differences arise.

In both Figure 2(a) and 2(b), hierarchical

clustering was done with Euclidean distance

with complete linkage. Two vineyards are

represented in the data set: the El Camino

Real (ECR) vineyard and the Windmill Ranch

(WR) vineyard both in the Santa Ynez AVA

of Santa Barbara County, USA. Gr represents

juice sample from grenache clone 1A from

the El Camino Real vineyard (ECR-Gr) and

the Windmill Ranch vineyard (WR-Gr). Sy

represents three syrah clone 877 (Sy) juice

samples taken from three distinct vineyard

blocks within Windmill Ranch, Fat Man (FM)

Little Boy (LB) and High Hill (HH).

Conclusion

Data quality and instrument performance can

be easily monitored using retention time shift,

chromatographic peak width, mass accuracy

versus. time and mass accuracy across a

chromatographic peak. In our study, there was

minimal, if any, shift in any of the parameters,

indicating that the instrument performance

remained excellent across the duration of

the analysis. This allows for the possibility

of large-scale data acquisition within one

Dreyer et al

16


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ES212645_LCTC032013_V16.pgs 03.12.2013 23:46 ADV blackyellowmagentacyan


experiment to assess metabolic changes over

long time periods or during any number of

sample processing steps, such as those that

occur during vinifi cation of grape juice. In

addition, when analysing raw data, taking the

time to look at how the data is affected by

different processing techniques enables one

to gain a better understanding of the changes

within the samples and how post-processing

can manipulate the results. Careful attention

needs to be paid to ensure that data

processing does not remove or minimize

important data points that may be critical for

the understanding of how certain procedures

alter the metabolic profi le of samples.

Reference

1. Paul T. Tarr, Mark L. Dreyer, Michael Athanas, Mona

Shahgholi, Keith Saarloos and Tonya P. Second,

Metabolomics Special Issue: Mass Spectrometry for

Metabolomics 9(1) Supplement, 170–177 (2013).

Mark Dreyer has over 20 years of

industry experience developing methods in

chromatography and mass spectrometry,

including 15 years working in small

pharmaceutical laboratories developing analytical

HPLC–MS methods. Dreyer joined Thermo

Fisher Scientifi c in 2010 and has responsibility

for supporting the sales representatives and

prospective customers in pre-sales and post-sales

activities focusing on the chromatography of

small molecules, both profi ling and quantitation,

wine analysis and surfactants.

Paul Tarr is a post-doctoral research scholar

at the California Institute of Technology,

Pasadena, California, USA. His research focuses

on the genetic control of metabolism. His

current research interests include understanding

how a paracrine hormone regulates cell-cell

communication within plant stem cell niches

and its impact on the cellular metabolome;

and understanding the metabolic changes

that occur during the cultivation of grapes for

viticulture and the impact of vinifi cation on the

metabolome of wine.

Michael Athanas, PhD, is an informatics

solution architect at Thermo Fisher Scientifi c,

focused on melding statistical analysis,

scalable computing, deep data mining

and extreme visualization into new and

contemporary approaches in scientifi c analysis.

Previously, Athanas was a scientifi c informatics

consultant and founder of VAST Scientifi c.

E-mail: [email protected]: http://www.thermoscientific.com

Dreyer et al

17


18


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Website: http://www.chromacademy.com/

lc-hplc-overview.asp

Basics of Preparative HPLCOn-line training from CHROMacademy


Preparative_HPLC_Essential_Guide.

asp?tpm=1_2

Fundamental LC–MSOn-line training from CHROMacademy


mass_spec-overview.asp

HPLC TroubleshooterOn-line training from CHROMacademy


hplc_troubleshooting.html

METHOD VALIDATIONIntroduction to Analytical Method Validation23 April 2013

Hilton Milton Keynes, Milton Keynes, UK

Website: http://www.crawfordscientific.

com/training-online-calendar.asp

Validation of Analytical Methods for Pharmaceutical Analysis6–7 May 2013

Berlin, Germany

Website: http://www.

mournetrainingservices.co.uk/course_list.

html#vampa

SAMPLE PREPARATIONSolid-Phase ExtractionOn-line training from CHROMacademy


sample-prep-training.html

GPC/SECPolyRMC GPC Academy20–22 May 2013PolyRMC Facilities at Tulane University in

New Orleans, LA, USA

Website: http://tulane.edu/sse/

polyRMC/polyrmc-gpc-academy.cfm

MISCELLANEOUS Polymer Chemistry: Principles and Practice 24–29 March 2013Blacksburg, Virginia, USA

Website: http://proed.acs.org/course-

catalog/courses/PCPP/

Light Scattering Training16–18 April 2013Santa Barbara, California, USA

Website: http://www.wyatt.com/

training/training/light-scattering-

training.html

Please send your event and training course information to Kate Mosford [email protected]

The Column www.chromatographyonline.com Training & Events

20


16–20 June 2013HPLC2013 Amsterdam

Amsterdam RAI Conference Centre, Amsterdam, The Netherlands

Co-chairs: Peter Schoenmakers and Wim Kok

E-mail: [email protected]

Website: www.hplc2013.org

1–4 July 2013 9th Annual Conference of the Metabolomics Society

Scottish Exhibition and Conference Centre (SECC), Glasgow, Scotland, UK

Organizers: Metabolomics Society

Tel: +44 (0)131 339 9235

Fax: +44 (0)131 339 9798


Website: www.metabolomics2013.org

28 July–2 August 2013ICMGP International Conference on Mercury as a Global Pollutant

Edinburgh, Scotland

Organizers: International Labmate Ltd

Tel: +44 (0)1727858840

Fax: +44 (0)1727840310


Website: www.mercury2013.com

6–9 October 2013 20th International Symposium on Electro- and Liquid-Phase Separation

Techniques, ITP2013

Puerto de la Cruz, Tenerife, Canary Islands, Spain

Tel: +34 922 318990

Fax: +34 922 318003


Website: http://www.itp2013.ull.es

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20 March 2013 Volume 9 Issue 5 Cover Story

Documents

Transcript of 20 March 2013 Volume 9 Issue 5 Cover Story