Automated Sample Preparation E-Book

Hans-Joachim Hübschmann

• Herausgeber: Wiley-VCH GmbH
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 743

Veröffentlichungsjahr: 2021

Beschreibung

An essential guide to the proven automated sample preparation process

While the measurement step in sample preparation is automated, the sample handling step is manual and all too often open to risk and errors. The manual process is of concern for accessing data quality as well as producing limited reproducibility and comparability.

Handbook of Automated Sample Preparation for CG-MS and LC-MS explores the advantages of implementing automated sample preparation during the handling phase for CG-MS and LC-MS. The author, a noted expert on the topic, includes information on the proven workflows that can be put in place for many routine and regulated analytical methods.

This book offers a guide to automated workflows for both on-line and off-line sample preparation. This process has proven to deliver consistent and comparable data quality, increased sample amounts, and improved cost efficiency. In addition, the process follows Standard Operation Procedures that are essential for audited laboratories. This important book:

• Provides the information and tools needed for the implementation of instrumental sample preparation workflows
• Offers proven and detailed examples that can be adapted in analytical laboratories
• Shows how automated sample preparation can reduce cost per sample, increase sample amounts, and produce faster results
• Includes illustrative examples from various fields such as chemistry to food safety and pharmaceuticals

Written for personnel in analytical industry, pharmaceutical, and medical laboratories, Handbook of Automated Sample Preparation for CG-MS and LC-MS offers the much-needed tools for implementing the automated sample preparation for analytical laboratories.

Leseprobe

Table of Contents

Cover

Title Page

Copyright

Dedication

Foreword

Disclaimer

Preface

References

1 Introduction

1.1 A Perspective on Human Performance

References

2 The Analytical Process

2.1 Laboratory Logistics

References

3 Workflow Concepts

3.1 Sample Preparation Workflow Design

3.2 Instrumental Concepts

3.3 Sample Processing

3.4 Tool Change

3.5 Object Transport

3.6 Vial Decapping

References

4 Analytical Aspects

4.1 Liquid Handling

4.2 Solid Materials Handling

4.3 Weighing

4.4 Extraction

4.5 Clean‐Up Procedures

4.6 Centrifugation

4.7 Evaporation

4.8 Derivatization

4.9 Temperature Control

4.10 Mixing

References

5 Integration into Analysis Techniques

5.1 GC Volatiles Analysis

5.2 GC Liquid Injection

5.3 LC–GC Online Injection

5.4 LC Injection

References

6 Solutions for Automated Analyses

First About Safety

6.1 Dilution

6.2 Derivatization

6.3 Taste and Odor Compounds Trace Analysis

6.4 Sulfur Compounds in Tropical Fruits

6.5 Ethanol Residues in Halal Food

6.6 Volatile Organic Compounds in Drinking Water

6.7 Geosmin and 2‐MIB

6.8 Solvent Elution from Charcoal

6.9 Semivolatile Organic Compounds in Water

6.10 Polyaromatic Hydrocarbons in Drinking Water

6.11 Fatty Acid Methylester

6.12 MCPD and Glycidol in Vegetable Oils

6.13 Mineral Oil Hydrocarbons MOSH/MOAH

6.14 Pesticides Analysis – QuEChERS Extract Clean‐Up

6.15 Glyphosate, AMPA, and Glufosinate by Online SPE‐LC‐MS

6.16 Pesticides, PPCPs, and PAHs by Online‐SPE Water Analysis

6.17 Residual Solvents

6.18 Chemical Warfare Agents in Water and Soil

6.19 Shale Aldehydes in Beer

6.20 Phthalates in Polymers

References

Appendix

A.1 Robotic System Control

A.2 System Maintenance

A.3 Syringe Needle Gauge

A.4 Pressure Units Conversion

A.5 Solvents

A.6 Material Resistance

References

Glossary

References

Index

End User License Agreement

List of Tables

Chapter 2

Table 2.1 Steps in laboratory chemical analysis.

Table 2.2 Principles of green analytical chemistry [17].

Chapter 3

Table 3.1 Transfer of the published method for FAMEs transesterification to ...

Table 3.2 Tool parameter information and tracking for process safety and sel...

Chapter 4

Table 4.1 Droplet volumes from a syringe with different needle gauges.

Table 4.2 Default and maximum syringe dispensing speeds.

Table 4.3 Precision of automated pipetting.

Table 4.4 Recommendations for pipetting.

Table 4.5 Optimum pipet tip immersion depth.

Table 4.6 Parameters for a liquid class definition with example “Water”.

Table 4.7 Suggested solvents for PLE of different matrices.

Table 4.8 Sorbent materials used for PLE in‐cell clean‐up.

Table 4.9 SPME applications by sorbent phase.

Table 4.10 Sorbent material volumes of different micro‐extraction techniques...

Table 4.11 Official methods and applications applying SPME.

Table 4.12 Recommended GC inlet liner ID for SPME fiber and SPME Arrow desor...

Table 4.13 Recommended sample volume and penetration depth for DI‐SPME liqui...

Table 4.14 Characteristics of elution solvents commonly used in SPE.

Table 4.15 Comparison of Micro‐SPE with classical SPE.

Table 4.16 Micro‐SPE sorbent material mix for pesticide clean‐up. Source: IT...

Table 4.17 Azeotropic mixture compositions and boiling points (after [224]).

Chapter 5

Table 5.1 Partition coefficients of selected compounds in water at different...

Table 5.2 Full evaporation method compared to the conventional static headsp...

Table 5.3 Peak area results of six consecutive MHE headspace analyses.

Table 5.4 Comparison of the MHE experiment using all six or the first two me...

Table 5.5 Applications using automated thermal desorption for analysis.

Table 5.6 Official standard methods and applications applying thermal desorp...

Table 5.7 Performance characteristics of common sorbent materials (after Mar...

Chapter 6

Table 6.1 Calculation scheme for a calibration dilution: Dn dilution levels ...

Table 6.2 Selected analysis parameters for metabolite analysis.

Table 6.3 Analysis parameter for the T&O analysis by HS‐SPME GC‐MS.

Table 6.4 SIM masses used for T&O compound detection (in bold quantifier ion...

Table 6.5 Selected important analysis parameters by static and dynamic heads...

Table 6.6 Percent of alcohol permitted in Halal food.

Table 6.7 Selected analysis parameters for the HS GC‐MS analysis of residual...

Table 6.8 Ethanol screening results from different foods using the automated...

Table 6.9 Analysis parameter for the purge and trap concentrator and GC‐MS a...

Table 6.10 SVOC standard for method development.

Table 6.11 Selected analysis parameters for the GC‐MS analysis of SVOCs.

Table 6.12 SVOC compounds by LLE GC‐MS analysis with linear range, MDL, and ...

Table 6.13 Selected analysis parameters for the SPME GC‐MS analysis of PAHs ...

Table 6.14 PAH analytes GC‐MS acquisition parameter.

Table 6.15 Overview of the current MCPD and glycidol analytical methods.

Table 6.16 Selected analysis parameter for MCPD and glycidol analysis.

Table 6.17 MRM mass transitions for MCPDs (bold the quantifier ions).

Table 6.18 Analytical parameter for MOSH/MOAH analysis by LC‐GC coupling.

Table 6.19 μSPE clean‐up workflow published by Morris and Schriner [92].

Table 6.20 Fast μSPE workflow published by Lehotay, Han and Sapozhnikova [95...

Table 6.21 Analysis parameter for the FMOC derivatization, online SPE clean‐...

Table 6.22 Gradient profile for LC separation.

Table 6.23 Limits of quantification (LOQ) and detection (LOD).

Table 6.24 Selected analysis parameters for the online SPE LC‐MS analysis.

Table 6.25 Gradient program for the loading pump.

Table 6.26 Gradient program for the analysis pump.

Table 6.27 Class 1 residual solvents to be avoided.

Table 6.28 Class 2 residual solvents to be limited.

Table 6.29 Class 3 residual solvents with low toxic potential

a)

.

Table 6.30 Selected analysis parameters for the USP <467> HS‐GC analysis of ...

Table 6.31 Concentrations of the Class 2 calibration test solution.

Table 6.32 Analysis parameter for the extraction of CWAs from water and soil...

Table 6.33 Recovery results of the automated LLE of chemical warfare agents ...

Table 6.34 Recovery results of chemical warfare agents from contaminated aqu...

Table 6.35 Flavor aldehydes derived from multiple chemical transformations w...

Table 6.36 Selected analysis parameters for the HS‐SPME GC‐MS analysis of al...

Table 6.37 Aldehyde detection limits using SPME Arrow GC‐MS with online deri...

Table 6.38 Analysis parameter for the extraction and GC‐MS analysis of phtha...

Table 6.39 GC‐MS SIM acquisition parameters for phthalate analysis (bold qua...

Appendix

Table A.1 Dimension of cleaning wires (after [9]).

Table A.2 Precision of automated syringe dispensing, gravimetric measurement...

Table A.3 Nominal needle gauge dimensions [13–16].

Table A.4 Pressure units conversion table.

Table A.5 Miscibility of solvents, boiling point (BP), and polarity of often...

Table A.6 Solvent viscosity, density, and surface tension, sorted by increas...

Table A.7 Resistance of the most used polymer materials with typical solvent...

List of Illustrations

Preface

Figure 1 Causes of error in chemical analysis, after [3].

Chapter 1

Figure 1.1 Human performance in analytical laboratories.

Figure 1.2 Survey results about sources of errors with impact in sample anal...

Chapter 2

Figure 2.1 Frequently used sample preparation steps and their potential for ...

Chapter 3

Figure 3.1 Multidilutor valve port connections.

Figure 3.2 Typical carousel GC autosampler with a rotating large sample vial...

Figure 3.3 Current modular automatic liquid sampler comprising two GC inject...

Figure 3.4 Circular workspace of a SCARA robot.

Figure 3.5 Top view SCARA robot for the automated metabolomics sample proces...

Figure 3.6 Cartesian system with linear

x

,

y

, and

z

axes and potential worki...

Figure 3.7 Cartesian robot system for off‐line sample preparation.

Figure 3.8 Cartesian robot system installed instrument top for online sample...

Figure 3.9 Use of automated sample preparation systems in analytical laborat...

Figure 3.10 YASKAWA's dual‐arm robot CSDA10F shown at the Analytica exhibiti...

Figure 3.11 Six‐axis Cobot working for automated powder dosing and online sa...

Figure 3.12 “Prep‐ahead” principle for optimized instrument duty cycle. Top:...

Figure 3.13 Scheduler operation for headspace sample preparation and analysi...

Figure 3.14 Overlapping with different incubation times. (a) Regular incubat...

Figure 3.15 Dual‐head x,y,z‐robotic system for parallel workflow operation w...

Figure 3.16 Recommended 1D barcode labeling on different vial sizes.

Figure 3.17 Thermal desorption tubes RFID and barcode labeled.

Figure 3.18 Syringe plate for manual syringe exchange in a programmable auto...

Figure 3.19 SPME syringe adapter for manual exchange. 1 Syringe exchange pla...

Figure 3.20 Ball‐lock mechanism for tool change in a robotic autosampler....

Figure 3.21 Exchangeable liquid tool for a robotic autosampler.

Figure 3.22 Smart syringe plunger head with identification chip.

Figure 3.23 SPME Fiber and Arrow devices with “smart” ID chip. Color Code of...

Figure 3.24 Magnetic screw caps for 20 and 2 mL vials.

Figure 3.25 Gripper tool with parallel jaw movement for the transport of obj...

Figure 3.26 Customized Grabber with 12 mm jaws operating Thomson filters....

Figure 3.27 Grabber for the transport of deep well or microplates.

Figure 3.28 Needle transport for a micro‐SPE cartridge.

Figure 3.29 Decapper for screw cap vials.

Chapter 4

Figure 4.1 Droplet formation at the end of a tube.

Figure 4.2 Microliter syringe schematics.

Figure 4.3 Syringe needle point styles [3].

Figure 4.4 HD‐Type plunger design.

Figure 4.5 Active syringe wash module for two wash solvents. The arrow point...

Figure 4.6 Micro‐vials and micro‐inserts for autosampler use. (A) Standard 2...

Figure 4.7 Tapered vial for low sample volume.

Figure 4.8 Bottom sensing allows 3 replicate 1 μL GC injections from a 5 μL ...

Figure 4.9 Forward pipetting technique (stops are related to manual pipet op...

Figure 4.10 Reverse pipetting technique (stops are related to manual pipet o...

Figure 4.11 Sequential dispensing into several vials.

Figure 4.12 Liquid‐Level Tracking keeps a constant tip immersion depth.

Figure 4.13 Liquid class parameter representation in the pipet tip.

Figure 4.14 Height of pipet tips for automated operations.

Figure 4.15 Pipet tips with filter as aerosol barrier against cross‐contamin...

Figure 4.16 Design of the Disposable Pipet Extraction Tip.

Figure 4.17 Workflow steps for the automated drug residue analysis using DPX...

Figure 4.18 Configuration for automated C18 pipet tip de‐salting and online ...

Figure 4.19 Dilutor module for multiple solvent sources with dispensing tool...

Figure 4.20 Dilutor operation in delivery and transfer mode..

Figure 4.21 Dilutor module linearity for liquid delivery from 10 μL to 1000 ...

Figure 4.22 Flow cell for water analysis in open access design, shown with a...

Figure 4.23 Flow cell with septum sampling point, shown with syringe samplin...

Figure 4.24 Automated powder dosing – One powder to many vials.

Figure 4.25 Automated powder dosing – many powders to many vials.

Figure 4.26 Automated powder dosing and sample processing workstation with (...

Figure 4.27 Weighing step with magnetic vial transport using a system‐integr...

Figure 4.28 The classical manual Soxhlet continuous liquid extraction appara...

Figure 4.29 Ultrasound module with a perforated cover for vial access used w...

Figure 4.30 Instrument configuration for pressurized liquid extraction ASE™....

Figure 4.31 Miniaturized online PLE with LVI to GC analysis.

Figure 4.32 PLE extraction cartridge packing for in‐cell clean‐up.

Figure 4.33 Classical liquid/liquid extraction procedure in a routine water ...

Figure 4.34 Camera module on a x,y,z‐robotic sampler for meniscus and phase ...

Figure 4.35 Automated optical control of the LLE phase separation level (red...

Figure 4.36 Increase of scientific publications using DLLME for sample prepa...

Figure 4.37 Vials with conical bottom or narrow neck for automated DLLME sam...

Figure 4.38 DLLME steps with an aqueous sample and

high

‐density extraction s...

Figure 4.39 DLLME steps with an aqueous sample and

low

‐density extraction so...

Figure 4.40 SPME extraction – physical factors affecting sample recovery.

Figure 4.41 Molecular weight extraction range for selected SPME sorbent mate...

Figure 4.42 HiSorb sorptive extraction probe with the enlarged view of the s...

Figure 4.43 SPME fiber assembly for automated applications.

Figure 4.44 SPME Arrow tip closed (A) and with exposed black WR carbon/PDMS ...

Figure 4.45 SPME Arrow and fiber dimensions.

Figure 4.46 SPME Arrow analytical improvements compared to SPME fiber extrac...

Figure 4.47 Comparison of HS‐SPME response using SPME Arrow (red) and SPME f...

Figure 4.48 BSTFA.

Figure 4.49 MSTFA.

Figure 4.50 MTBSTFA.

Figure 4.51 PFBHA·HCl.

Figure 4.52 PFPH.

Figure 4.53 PFBBr.

Figure 4.54 Automated SPME on‐line flow cell sampling and derivatization for...

Figure 4.55 Schematic of the instrument configuration for automated direct S...

Figure 4.56 Conventional ESI vs. SICRIT™ DBDI ionization technique. Particle...

Figure 4.57 TIC‐MS (a) and HRMS spectrum (b) of the direct SPME‐MS analysis ...

Figure 4.58 Automated direct SPME‐MS analyses (bottom, five minutes extracti...

Figure 4.59 Twister™ stir bar.

Figure 4.60 Liquid (SBSE) and headspace (HSSE) application of the magnetic S...

Figure 4.61 SBSE Thermal desorption tube loaded with a stir bar.

Figure 4.62 SBSE Thermal desorption unit for GC‐MS.

Figure 4.63 Thin‐film membrane in‐vial immersion extraction.

Figure 4.64 Luer tip syringe filter.

Figure 4.65 Automated use of syringe filters with Luer tip and pre‐installed...

Figure 4.66 Filter vial operation. (1) Plunger with filter material on botto...

Figure 4.67 Automated sample filtration using Thomson filter vials. (1) Vial...

Figure 4.68 Various dimensions of commercial SPE tubes.

Figure 4.69 Automated SPE workflow steps using a positive pressure system....

Figure 4.70 On‐line SPE analyte elution profile.

Figure 4.71 On‐line SPE concept for automated method development.

Figure 4.72 Results from the automated method development for the most suita...

Figure 4.73 μSPE cartridge design and operation.

Figure 4.74 Micro‐SPE prep‐ahead clean‐up on the time axis parallel to the G...

Figure 4.75 MEPS device detail.

Figure 4.76 Syringe base μSPEed device.

Figure 4.77 Principle μSPEed operation steps.

Figure 4.78 Schematic of GPC separation – Bed material pores vs. analyte siz...

Figure 4.79 GPC Elution profile for the clean‐up of pesticides extracts. (1)...

Figure 4.80 On‐line μGPC configuration for GC‐MS, combined with optional μSP...

Figure 4.81 Automated μGPC clean‐up system for on‐line GC‐MS analysis.

Figure 4.82 Centrifuge rotors symmetrically loaded with (A) 4 × 2 mL and (B)...

Figure 4.83 Time‐based gravimetric calibration for the evaporation of chloro...

Figure 4.84 Workflow evaporation with a tool‐based double‐needle device.

Figure 4.85 Vacuum‐assisted evaporation module.

Figure 4.86 Benzoyl chloride.

Figure 4.87

p

‐Nitrobenzoyl chloride.

Figure 4.88

p

‐Methoxybenzoyl chloride.

Figure 4.89

m

‐Toluoyl chloride.

Figure 4.90 Ninhydrin.

Figure 4.91 3,5‐Dinitrobenzoyl chloride (DNBC).

Figure 4.92 Dansylchloride.

Figure 4.93 Fluorenylmethyloxycarbonyl chloride (FMOC‐Cl)

Figure 4.94 HDMS.

Figure 4.95 TMCS.

Figure 4.96 Acetic acid anhydride.

Figure 4.97 TFAA.

Figure 4.98 PFPA.

Figure 4.99 HFBA.

Figure 4.100 PFBAY.

Figure 4.101 TTBB.

Figure 4.102 PFBB.

Figure 4.103 TMAH.

Figure 4.104 TMSH.

Figure 4.105 TBAHS.

Figure 4.106 TBAC.

Figure 4.107 TBH.

Figure 4.108 TBAH.

Figure 4.109 HFBA.

Figure 4.110 MBTFA.

Figure 4.111 PFBCI.

Figure 4.112 PFPOH.

Figure 4.113 “Lavistoma” large volume injector cutaway.

Figure 4.114 In‐port analyte derivatization using the “Lavistoma” injector. ...

Figure 4.115 Incubator/Agitator device for vial heating and shaking.

Figure 4.116 Headspace overlapping incubation principle. (1) Set/check the a...

Figure 4.117 Operation principle of a vial cooling device using dried air. (...

Figure 4.118 Principle of the vortex mixer with shaking in three spatial axe...

Figure 4.119 Agitator inserts for 2 mL vials, left the insert with 2 mL vial...

Figure 4.120 Cycloid mixing module with an 8‐fold cycloidal mixing pattern....

Figure 4.121 Comparison of cycloidal with stir bar mixing for PAH analysis f...

Chapter 5

Figure 5.1 Partition of analytes in headspace analysis – compounds with low ...

Figure 5.2 Headspace sampling process with a heated syringe.(A) Carrier ...

Figure 5.3 Headspace syringe tool with sideport syringe installed, showing p...

Figure 5.4 Operation sequence of a static headspace system with sample loop ...

Figure 5.5 MHE workflow with MHE tool attached to a headspace syringe.(A...

Figure 5.6 Graphical representation of the MHE measurements of Table 5.3. (a...

Figure 5.7 Sensitivity range comparison of static and dynamic headspace meth...

Figure 5.8 Purge & Trap schematics in purging status.

Figure 5.9 Dilutor functions and connection ports for automated purge and tr...

Figure 5.10 Components of the ITEX DHS syringe trap.

Figure 5.11 ITEX DHS tool cutaway.

Figure 5.12 ITEX DHS syringeoperation principle.

Figure 5.13 Dynamic headspace unit using sorbent tubes.

Figure 5.14 DHS process using sorbent tubes.

Figure 5.15 Needle trap schematics with approx. dimensions ().

Figure 5.16 Desorption of the needle trap device using a tapered liner in th...

Figure 5.17 Sorbent material strength (after Markes International Ltd).....

Figure 5.18 Principle of the two‐stage thermal desorption with backflush of ...

Figure 5.19 Thermal desorption tubes for the thermal desorption unit (TDU). ...

Figure 5.20 Device for automated capping/decapping of desorption tubes.

Figure 5.21 Desorption tube insertion sequence with gripper tool into the op...

Figure 5.22 Diffusion lock tube cap.

Figure 5.23 Diffusion lock performance for > 24h storage.

Figure 5.24 Sorbent pen cross section.

Figure 5.25 Sorbent pen inserted to a 20 mL headspace vial with vacuum line ...

Figure 5.26 Sandwich injection principle: Solvents 1 and 2 can be optionally...

Figure 5.27 “Hot Needle Injection” with thermospray formation.(1) Pull u...

Figure 5.28 Fast Injection with Liquid Band Formation into a glass wool fitt...

Figure 5.29 Phthalate standard mixture GC chromatograms using a standard spl...

Figure 5.30 GC oven temperature program for up to 10 μL injection volume – N...

Figure 5.31 Concurrent solvent recondensation (CSR) principle.

Figure 5.32 Chromatogram of 35 μL alkane standard in hexane, CSV injection m...

Figure 5.33 Robotic liner exchange with the MPS ALEX System.

Figure 5.34 Exploded view of the transport adapter for the automated GC inle...

Figure 5.35 LC fraction transfer to GC (Transfer valve).

Figure 5.36 On‐line LC‐GC configuration. The analyte LC fraction is transfer...

Figure 5.37 LC Injection ports with sample loops for accessible automated li...

Figure 5.38 LC Injection valve – load with partial loop filling – inject in ...

Figure 5.39 Dynamic load and wash injection sequence.

Figure 5.40 Dynamic load and wash tool for high‐throughput and low carryover...

Figure 5.41 Liquid monitoring with the sensor of an LC‐MS injection tool. (A...

Figure 5.42 LC injection with backflush for pipette injections.

Chapter 6

Figure 6.1 Automated dilution scheme for a geometrical dilution of a concent...

Figure 6.2 System configuration for geometric dilutions (benchtop installati...

Figure 6.3 Automated geometrical dilution result (using a blue dye).

Figure 6.4 Automated calibration dilution scheme with individual syringes fo...

Figure 6.5 Minimum configuration for the calibration dilution workflow. 1 To...

Figure 6.6 Workflow steps for the dilution of standards for calibration curv...

Figure 6.7 Automated quantitative calibration dilution result (using a green...

Figure 6.8 Quantitative calibration after automated dilution in the range of...

Figure 6.9 Dual‐head

x

,

y

,

z

‐robotic system for analytical multi‐compound stan...

Figure 6.10 Recommended system configuration for a derivatization workflow w...

Figure 6.11 Workflow steps for the MSTFA derivatization with GC injection.

Figure 6.12 Robotic sampler configuration for SPME derivatization workflows....

Figure 6.13 Workflow steps for SPME on‐fiber derivatization, e.g. silylation...

Figure 6.14

x

,

y

,

z

‐Robot configuration for the metabolomics two‐step MeOx‐TMS...

Figure 6.15 Workflow steps for the automated two‐step MeOx and TMS derivatiz...

Figure 6.16 A metabolomics profile after MeOx‐TMS derivatization. The elutin...

Figure 6.17 Overlapped MeOx‐TMS workflow for high sample throughput.

Figure 6.18 3‐Isobutyl‐2‐methoxypyrazine (IBMP).

Figure 6.19 3‐Isopropyl‐2‐methoxypyrazine (IPMP).

Figure 6.20 2,4,6‐Trichloroanisole (TCA).

Figure 6.21 2,4,6‐Tribromoanisole (TBA).

Figure 6.22 β‐Ionone (BIN).

Figure 6.23 Recommended configuration for the SPME Arrow extraction workflow...

Figure 6.24 Robotic system configuration for static and dynamic headspace an...

Figure 6.25 Part A – Automated workflow for static headspace analysis (*init...

Figure 6.26 Part B – Automated workflow for dynamic headspace analysis (*ini...

Figure 6.27 GC‐MS full scan total ion chromatogram of the major VSCs by stat...

Figure 6.28 GC‐MS full scan total ion chromatogram of the trace VSCs by dyna...

Figure 6.29 Minimum configuration for the ethanol HS‐GC screening. 1

Multipl

...

Figure 6.30 Workflow steps for the automated determination of ethanol residu...

Figure 6.31 Analysis of the “Naturally Brewed Dark Soy Sauce” with a concent...

Figure 6.32 Robotic autosampler sampling from different vial sizes connected...

Figure 6.33 Online water sampling from two water streams using the Purge & T...

Figure 6.34 Robotic sampler configuration for automated purge and trap analy...

Figure 6.35 Workflow steps for the automated Purge & Trap analysis of VOCs i...

Figure 6.36 Linear calibration of naphthalene in tap water, automatically pr...

Figure 6.37 Geosmin (GSM).

Figure 6.38 2‐Methylisoborneol (2‐MIB).

Figure 6.39 Workflow steps for the automated SPME Arrow analysis of GSM and ...

Figure 6.40 Calibration for 2‐MIB in the low range up to 10 ng/L.

Figure 6.41 Calibration for geosmin in the low range up to 10 ng/L.

Figure 6.42 Geosmin HS‐SPME Arrow signal at 10 ng/L spiked to drinking water...

Figure 6.43 2‐MIB HS‐SPME Arrow signal at 10 ng/L spiked to drinking water (

Figure 6.44 Robotic sampler configuration for automated charcoal elution. 1 ...

Figure 6.45 Workflow for automated charcoal solvent elution and GC injection...

Figure 6.46 Robotic system configuration for the automated LLE extraction of...

Figure 6.47 Automated workflow of the LLE extraction for SVOC analysis (*ini...

Figure 6.48 Quantitative relative response calibration of benzo(

a

)pyrene in ...

Figure 6.49 Benzo(

a

)pyrene LVI chromatograms of 1, 10, 20, and 50 μL of the ...

Figure 6.50 GC‐MS chromatogram (MRM TIC) of 35 SVOC compounds and 4 ISTDs af...

Figure 6.51 “Bay regions” of selected cancerogenic PAH compounds (Source: Ad...

Figure 6.52 Robotic system configuration for SPME extraction of PAHs from wa...

Figure 6.53 Automated workflow steps for the DI‐SPME analysis of PAHs in dri...

Figure 6.54 GC‐MS total ion chromatogram of the PAH standard after DI‐SPME A...

Figure 6.55 Quantitative calibration for Benzo(

a

)pyrene in the range of 0.01...

Figure 6.56 Configuration for the BF

3

‐catalyzed FAMEs derivatization workflo...

Figure 6.57 Automated workflow steps of the FAMEs preparation for GC analysi...

Figure 6.58 3‐MCPD (3‐monochloropropane‐1,2‐diol).

Figure 6.59 2‐MCPD (2‐monochloropropane‐1,3‐diol).

Figure 6.60 Glycidol (2,3‐epoxy‐1‐propanol).

Figure 6.61 Phenylboronic acid (PBA).

Figure 6.62 Sample preparation of the automated AOCS Cd29c‐13 method for MCP...

Figure 6.63 Typical configuration for an MCPD workstation installed on a GC‐...

Figure 6.64 Automated workflow of the AOCS Cd 29c‐13 method for MCPD analysi...

Figure 6.65 Comparison of automated workflow data with manual measurements o...

Figure 6.66 Overlaid chromatograms of six repeat sample runs of spiked virgi...

Figure 6.67 Overlapped sample preparation in prep‐ahead mode provides 2-/3-M...

Figure 6.68 meta‐Chloroperoxybenzoic acid (mCPBA) for olefin epoxidation.

Figure 6.69 Suggested robot configuration for the automated MOSH/MOAH analys...

Figure 6.70 Automated workflow of the MOSH/MOAH analysis with the optional e...

Figure 6.71 Calibration curve for C40 MOSH with a correlation coefficient of...

Figure 6.72 Parallel MOSH and MOAH LC‐GC‐FID analysis (chromatogram overlay,...

Figure 6.73 Comparison of GC elution times of a pesticide standard acquired ...

Figure 6.74 Suggested configuration for the μSPE clean‐up workflow for QuECh...

Figure 6.75 Configuration and setup of the μSPE trayholder.

Figure 6.76 Eluate rack, 54× 2 mL vials with pre‐cut septa.

Figure 6.77 μSPE vial lock installed on the eluate rack.

Figure 6.78 54 Position μSPE cartridge rack on top of the waste receptacle w...

Figure 6.80 Visual μSPE clean‐up results for black tea (a) and spinach (b)....

Figure 6.79 Automated workflow steps for the μSPE QuEChERS extract clean‐up ...

Figure 6.81 Black tea sample pesticides GC‐MS/MS peaks at 1 ppb after μSPE c...

Figure 6.82 Comparison between μSPE and dSPE clean‐up for spices using simil...

Figure 6.83 Results from fatty food matrices pork (a) and salmon (b) using t...

Figure 6.84 GC inlet liner and septum after 230 matrix samples of different ...

Figure 6.85 LC‐MS/MS chromatogram of 195 pesticides compounds after μSPE cle...

Figure 6.86 Method robustness with 200 injections of μSPE cleaned black tea ...

Figure 6.87 Glyphosate (

PMG

,

N

‐(phosphonomethyl)glycine

)

Figure 6.88 AMPA (aminomethylphosphonic acid).

Figure 6.89 Glufosinate ((

RS

)‐2‐amino‐4‐(hydroxy(methyl)phosphonoyl)butanoic...

Figure 6.90 Derivatization reaction principle: glyphosate reacts with FMOC‐C...

Figure 6.91 Robotic system dual head configuration for the automated glyphos...

Figure 6.92 Automated workflow for glyphosate analysis from derivatization, ...

Figure 6.93 Analysis sequence in prep‐ahead mode of the samples showing the ...

Figure 6.94 Robotic sampling system configuration for online SPE analysis. 1...

Figure 6.95 Online SPE configuration and automated workflow steps. The sampl...

Figure 6.96 Potential sources of residual solvents in pharmaceutical drug pr...

Figure 6.97 Robotic sampler configuration for USP <467> high throughput resi...

Figure 6.98 Chromatogram of a Class 2 compound standard illustrating the wid...

Figure 6.99 Quantitative calibration for the low‐level compound acetonitrile...

Figure 6.100 Robotic sampler in the configuration for the LLE and μSPE clean...

Figure 6.101 Automated LLE extraction workflow with optional μSPE clean‐up s...

Figure 6.102 GC‐MS chromatogram of a diesel fuel contaminated water sample a...

Figure 6.103 PFBHA derivatization reaction to form pentafluorobenzyl oxime d...

Figure 6.104 Beer analysis for aldehydes using automated SPME on‐fiber deriv...

Figure 6.105 Phthalate general formula, with

R

,

R

′ variety of linear and bra...

Figure 6.106 Robotic sampler configuration for the phthalate extraction work...

Figure 6.107 Automated workflow for the analysis of phthalates in polymers (...

Figure 6.108 Chromatogram with the phthalate elution series (after,

Appendix

Figure A.1 Maestro PrepBuilder user interface.

Figure A.2 Chronos method editor.

Figure A.3 Graphical user interface for methods creation by Drag & Drop.

Figure A.4 PAL Sample Control window with task and activity list.

Figure A.5 Black‐contaminated syringe plunger impeding even blocking the mov...

Figure A.6 Wiping the syringe plunger of Figure A.5 with a methanol‐soaked t...

Figure A.7 Autoclavable parts of a pipetting tool for automated operation....

Guide

Cover

Table of Contents

Title Page

Copyright

Dedication

Foreword

Preface

Begin Reading

Appendix

Glossary

Index

WILEY END USER LICENSE AGREEMENT

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Automated Sample Preparation

Methods for GC-MS and LC-MS

Hans‐Joachim Hübschmann

Author

Dr. Hans‐Joachim HübschmannUrbane Namba 12132-1-34 Minatomachi, Naniwa-kuOsaka 556-0017Japan

Cover Design: © ADAM DESIGN, Weinheim, GermanyCover Image: © CTC Analytics AG

All books published by WILEY‐VCH are carefully produced. Nevertheless, authors, editors, and publisher do not warrant the information contained in these books, including this book, to be free of errors. Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate.

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© 2022 WILEY‐VCH GmbH, Boschstr. 12, 69469 Weinheim, Germany

All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form – by photoprinting, microfilm, or any other means – nor transmitted or translated into a machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not specifically marked as such, are not to be considered unprotected by law.

Print ISBN: 978‐3‐527‐34507‐6ePDF ISBN: 978‐3‐527‐81750‐4ePub ISBN: 978‐3‐527‐81752‐8oBook ISBN: 978‐3‐527‐81751‐1

For PaulinaIn Memory of Amalia

Foreword

I feel honored by Hans‐Joachim Hübschmann asking me to write this Foreword to his newest book, Automated Sample Preparation: Methods for GC‐MS and LC‐MS. I have read the near‐final draft with pleasure, and I am impressed in all respects with the work, which was a great deal of work indeed! Hans‐Joachim's choices of topic, title, chapters, sections, and their organization were splendid, and he presented the right amount of detailed yet concise information with much consideration and care.

Many books are written to follow trends that cover the same information as other books already available, but Hans‐Joachim found a perfect niche by focusing on automation of sample preparation for instrumental chromatographic analysis. It is a topic that has rarely been published before, not even in journals due to the profitable advantages gained by laboratories that successfully implement automated methods.

Hans‐Joachim and I met for the first time in 2016 at a food science conference in Singapore. We had an immediate connection in our work involving automated mini‐cartridge solid‐phase extraction, which has expanded to a larger connection after many conversations and email exchanges since then. I am grateful to Hans‐Joachim for his valuable knowledge and input when I have requested it, and I am pleased to gain the perspective from the author of this work, as well as an 880‐page 3rd Edition opus, Handbook of GC‐MS: Fundamentals and Applications.

In my opinion, the three most important trends in sample preparation are streamlining, miniaturization, and automation. The fundamental chemistry of dissolution, precipitation, vaporization, partitioning, adsorption, hydration, chelation, and other phenomena that form the basis of all old and new sample preparation techniques have been known for centuries. However, the technical art of analytical chemistry requires the skill to manipulate those chemical properties in the most efficient way possible to still achieve accurate results for the purpose of the analysis.

Michelangelo Anastassiades and I did not invent any new chemistry when we developed the “quick, easy, cheap, rugged, and safe” (QuEChERS) approach for sample preparation in 2002, but we streamlined and miniaturized existing tools in an elegant solution at the right time when commercial GC‐MS and LC‐MS instruments were sensitive and universally selective enough to allow analysis of a wide scope of analytes. In this book, Hans‐Joachim describes the next step for QuEChERS, which entails automation, and I've been calling it QuEChERSER (more than QuEChERS is also “elegant and robust”).

Devising and implementing automated methods takes “brains,” which constitutes the fourth important trend in sample preparation. Unlike the other trends that continually entail “more more more,” required intelligence among laboratory workers is trending in both directions the same time. The growth in sheer knowledge and the complexity of problems, tools, and technology has required more brains to solve modern problems, but less brains are needed to perform routine operations, especially when using automated methods. For decades now, laboratories have saved money by hiring less educated, less talented, and less skilled technicians (thereby less expensive) to perform chemical analyses. In fact, many laboratory owners consider it a “no brainer” to fully automate as much of their operations as possible so they can hire as few staff as possible.

Despite what CSI and other fictional depictions of analytical chemistry may show, in which perfectly accurate results are beautifully displayed on colorful viewscreens in a matter of seconds, real‐world analyses are not that fast and easy! Real‐world analysts are not as smart as Abby Sciuto from NCIS either, and nobody is writing the plots and scripts for them leading to high certainty results that neatly solve the critical problem of the hour. With respect to reliability and data quality, brainless robots can outperform even Abby (without the personality quirks, need for sleep, or salary demands). The real‐life Abby is a smart and savvy technical operator who writes the script and maintains the automated instruments.

Analytical chemists tend to be responsible, hard‐working people who are motivated by laziness, which is a perfect combination to traits needed to implement laboratory automation. If it was easy, laboratory automation would have been implemented ages ago. Unfortunately, reliable, inexpensive, and user‐friendly automated tools have not been commercially available until very recently, and the lack of technical know‐how and abilities of laboratory staff remains the greatest obstacle.

This is how Hans‐Joachim Hübschmann's book about the Automated Sample Preparation: Methods for GC‐MS and LC‐MS comes in handy. The smartest (and the dumbest) brains do not have to work hard to solve problems at all if the owners of these brains have knowledge that the same or similar problems have already been solved by others! Hans‐Joachim and this book can help both the brainy and not so brainy analysts of the world, and their bosses, make the fictional world of CSI and NCIS become a step closer to reality.

Disclaimer

The opinions expressed in this foreword are the author's own and do not reflect the view of the USDA.

Wyndmoor, Pennsylvania; USAJune 25, 2020

Steven J. Lehotay, PhD (Lead Scientist)USDA Agricultural Research ServiceEastern Regional Research Center

Preface

Although sample preparation is considered an enabling technology, it is among the most vital components of an analytical scheme.

Douglas E. Raynie, 2019 [1]

This textbook about Automated Sample Preparation originated from the frequent request of many analytical laboratories for integrated analytical sample preparation methods.

But, why the focus on “automated”? In fact, the motivation to employ instrumental workflow‐based sample preparation is not “automation” by itself. In contrast, “automation” is the solution to several voiced drawbacks and bottlenecks in the laboratory. The traditional manual sample preparation is often slow and labor‐intensive, not to mention the potential exposure of the laboratory staff to hazardous chemicals. A main aspect to be discussed is “manual,” with all human impact on data quality, data comparability, and error. Multistep manual sample preparation can amount to 75% of the total method error [2]. Driven by the needs of international food, health care, and life science industries with regulations and standards, the data of chemical analyses are not for an only local approach anymore, but of global use and impact. Also, the potential environmental impact of the traditional analytical methods in use is increasingly scrutinized. This is due to the growing required number of samples for a steadily increasing number of contaminants and residues, and not to neglect, the environmental impact of the also growing usage of consumables and solvents, creating critical laboratory waste.

In the past, such topics did not receive much attention until quite recently with the overdue discussion and awareness of the impact of sample preparation on the error of analysis. On the recent survey question of the Analytical Methods Committee of the Analytical Division of the Royal Society of Chemistry “What caused your last poor proficiency test score?” the unequivocal response with 41% of impact was sample preparation, equipment problems, and human error, with “sample preparation” on top of all responses [3]. Figure 1 illustrates graphically the high man‐made impact on analytical results. Significant improvements in standardized and automated sample preparation are required to keep pace with quality and productivity demands.

Figure 1 Causes of error in chemical analysis, after [3].

Considering all these, sample preparation has evolved as a separate discipline within the analytical and measurement sciences [2]. This compendium Automated Sample Preparation: Methods for GC‐MS and LC‐MS informs in detail on the available tools for the transfer of current manual preparation procedures to the automated level with x,y,z‐robotic systems. The discussed concepts for automation try to answer on how to improve data quality, elaborate on the inherent potential for a green analytical chemistry, and finally conclude on improvements for sample throughput, method robustness, and productivity.

The objective of this textbook is to provide an overview of the current potential and proven examples for employing such robotic systems installed instrument‐top or standalone with established tools, modules, and flexible workflows for a variety of sample preparation techniques.

The Automated Sample Preparation: Methods for GC‐MS and LC‐MS covers in the first part the technical concepts used in robotic systems for automated sample preparation workflows. This part enables the reader to create well‐targeted and efficiently operating workflows with enough safety margin for robustness. It provides guidelines to transfer manual procedures to robotic systems. Besides the analytical expertise in the design of preparation methods, the knowledge of the technical possibilities and limitations, as well as a sense of logistics for the required sequential or parallel steps of operation, are discussed.

A second part goes into detail of popular analytical sample preparation workflows. It covers typical program‐driven sample preparation examples from food, environmental, forensic, and pharmaceutical analyses. These examples are proven workflows found in many analytical laboratories and follow the published official guidelines. Each of them can serve as a template for individual use or additional modification.

My sincere thanks to many friends and colleagues for discussions, critical review, and contributions. I am very grateful to Steve Lehotay for the highly appreciated exchange on all aspects of laboratory sample processing, André Althoff for the valuable discussions, and Atsuko Bansho for her continued support, advice, and patience, my family and friends who did not get much of my time and attention during the preparation of this project.

The comprehensive compilation of technical material, images, and detailed operational and analytical background information would not be possible without the contribution by many companies actively involved in automated sample preparation. I am especially indebted to the following companies for kindly providing review, discussions, and approvals for the use of in‐depth information, graphics, and image material for tools, modules, and data making the illustration of automated workflows possible: Agilent Technologies, namely, Paul Barboni and Eric Denoyer; Aisti Science Co. Ltd., namely, Sasano Sadato; Axel Semrau GmbH & Co KG, namely, Andreas Bruchmann; Tobias Uber, Brechbühler AG, namely, Philippe Mottay; CTC Analytics AG, namely, Jonathan Beck, Chiew Mei Chong, Stefan Cretnik, Toni Eberwein, Florian Gafner, Thomas Läubli, Gwen Lim, Jianxia Lv, Gerhard Nagel, Günter Böhm, Thomi Preiswerk, Kai Schüler, and Melchior Zumbach; CDS Analytical, namely, Roger Tank; Entech Instruments, namely, Dan Cardin and John Quintana; ePrep Pty Ltd., namely, Peter Dawes; Evosep, namely, Eric Verschuuren; GERSTEL GmbH & Co KG, namely, Ralf Bremer, Oliver Lerch, and Kaj Petersen; GL Sciences B.V., namely, Geert Alkema; Hamilton Bonaduz AG, namely, Beat Scheu; ITSP Solutions Inc., namely, Kim Gamble; LabTech Instruments Ltd., namely, Xue Liu; Leco Corporation Japan, namely, Michico Kanai; Markes International Ltd., namely, Massimo Santoro; Mettler Toledo AG, namely, Joanne Laukart; Plasmion GmbH, namely, Jan‐Christoph and Thomas Wolf; SIM GmbH, namely, Rolf Eichelberg; Spark Holland B.V., namely, Florian van der Hoeven; Thermo Fisher Scientific, namely, Christina Jacob, Claudia Martins, and Fausto Pigozzo; Thomson Instrument Company, namely, Sam Ellis; Trajan Scientific and Medical, namely, Glenn Clivaz and Andrew Gooley; and Yaskawa Europe GmbH, namely, Richard Tontsch.

I am sure that there are many more colleagues contributing directly or indirectly with discussions during collaborations, conferences, and publications whom I have not mentioned and I apologize to them for any omissions.

Hans‐Joachim HübschmannMainz, July 2020

References

1

Raynie, D.E. (2019). The (mis)education of an analyst.

LCGC North America

37 (11): 796–800.

2

Mitra, S. (ed.) (2003).

Sample Preparation Techniques in Analytical Chemistry

. Hoboken, New Jersey, USA: Wiley‐Interscience.

3

Analytical Methods Committee, AMCTB No 56 (2013). What causes most errors in chemical analysis?

Analytical Methods

5 (12): 2914–2915.

https://doi.org/10.1039/c3ay90035e

.

1Introduction

Sample preparation remains the single most challenging aspect of chemical analysis

Mary Ellen P. McNally [1]

Samples arriving in the analytical laboratory usually cannot be applied directly to analytical instruments. After homogenization of the material, a more or less complex pre‐treatment consisting of several steps is required for almost every sample before analysis. Each step is critical and can be a source of error and additional contamination. Often the used methods generate hazardous waste from solvents, chemicals, and consumables used. Most of the cost in chemical analysis is associated with the efforts in sample preparation procedures.

The goal of the sample preparation process is the extraction of the analytes from the incompatible matrix. Especially in trace analysis, the target analytes in low concentration are embedded in a high excess of a hard to eliminate matrix. The enrichment of the analytes to a suitable level of concentration for detection and quantification often also includes a necessary clean‐up from the co‐extracts. The sample preparation requires most of the time and the best skills in the analytical laboratory. Many of these tasks are still manual today. A recent survey revealed that two‐thirds of the analysis time is spent on sample preparation [2]. Optimum and fit‐for‐purpose preparation methods for analytes in diverse matrices are subject to discussion in thousands of scientific publications every year. Google Scholar finds more than 2 million hits featuring specific sample preparations alone in the last 10 years! [3]. The demand for multi‐analyte methods for larger groups of compounds with a potential occurrence in the same sample, targeted or non‐targeted, like pesticides, mycotoxins, drugs, or personal care products, just to name a few areas, is increasingly addressed. The continuously growing number of analytes in different matrices is the main reason for steadily ongoing improvements.

These very first steps in the analytical workflow have the highest impact on the quality of the analytical data. Many laboratories use manual, time‐ and labor‐intensive procedures. But the manual sample pre‐processing is the biggest known source of error in the analytical sequence, no matter how precise and sensitive a mass spectrometer in the final step may be, it cannot correct for [4]. Errors in analytical measurements are the random variability, expressed as the precision of the method, systematic bias affecting the trueness of results, and gross mistakes, the handling errors. Method validation assesses precision and trueness of a method, while human spurious errors, reported as the greatest source of errors, cannot be corrected in the course of analysis [5]. It is reported from the survey that operator and sample processing errors amount up to 50% of all known potential sources of error. Evidently, measures in this area improve analytical quality significantly. Here is the standardization of sample preparation procedures a general goal. The introduction of instrumental and integrated sample preparation workflows addresses these weak points in the sample processing sequence of the otherwise excellently validated procedures.

Standardization of analytical methods including sample preparations for different kinds of samples is available for all routine areas with validated procedures published as European standards (EN), the methods of the International Organization for Standardization (ISO), the Association of Official Analytical Chemists (now AOAC International), American Oil Chemists' Society (AOCS), the Food and Drug Administrations (FDA), the Environmental Protection Agencies (EPA), Pharmacopias, and similar organizations. In these established methods, sample preparation is often the rate‐determining step for sample throughput and too often the error‐prone part of the analytical method. In this context, standardization calls for automation, not vice versa. Automation reduces the quite normal human manual variation and mistakes in the sample processing using adequately configured robots for the standard workflows.

In chemical analysis, the instrumentation for chromatographic separation and detection, in particular with the use of mass spectrometry, reached an operational and performance level of high technical maturity. Sensitivity, selectivity, separation power and mass resolution, speed of analysis, and data processing were the major instrumental developments with significant enhancements in recent years. Barely exploited are features for a major step ahead for an instrumental and robotic sample processing. The obvious gap is the missing use of such amazing instrument specifications for a greener sample preparation at the front end. Much smaller sample sizes are possible to reach legally required quantitation limits today. The miniaturization and standardization with automated robotic workflows for the analytical sample processing significantly release from the human impact on data quality. This handbook about Automated Sample Processing focuses on the tools, modules, and workflows for the next level of a greener analytical chemistry.

1.1 A Perspective on Human Performance

In contrast to the notable improvement in instrument performance and reliability, there was not much focus in the past on the instrumental integration of the traditional manually performed sample preparation workflows, knowing the significant impact of human error on reliable and true analysis results. A general industry view of human performance and root cause of events monitored is shown in Figure 1.1. Human error is not random. Mistakes are systematically connected to features of people's tools, the tasks they perform, and the operating environment in which they work [6]. About 80% of all events are attributed to human error. Further broken down, 70% are related to organizational weaknesses caused by humans in the past and 30% are directly related to human mistakes in the manual workflow.

Figure 1.1 Human performance in analytical laboratories.

Source: Adapted from U.S. Department of Energy [6].

Survey results confirm dramatically the sources of error in chemical analysis, illustrated in Figure 1.2 with the size of the square areas representing their impact. The major source of error is seen in such manual sample preparation steps with about 30% of all the potential causes. On top, operator‐generated error and variation are estimated here to contribute to an additional 19%. The introduction of instrumental automated sample preparation workflows hence is expected to reduce such well‐known human errors by half in routine analysis methods [5].

Figure 1.2 Survey results about sources of errors with impact in sample analysis (square areas in %).

Source: Adapted from Majors [2].

Samples to be analyzed usually require an often multifaceted preparation over several steps for extraction and concentration before application to an analytical instrument for analyte detection. More than 65% of the respondents to the recent survey reported the use of three or more sample preparation steps for one sample. The rate of more complex sample preparation went up significantly from previous years' responses, a number and apparent demand strongly increasing [7]. The degree of sample preparation depends mainly on the analytes, the physical status of the sample, the single or multi‐compound approach, and finally the analytical method to be applied. It can cover a wide range from just dilute‐and‐shoot to multistep methods with extraction, concentration, and derivatization. The common goal is to reach just enough and fit‐for‐purpose sample clean‐up to keep the analytical instrumentation in the validated status for large series of samples and reduce preventive maintenance downtime to the necessary. In the vast majority of analytical methods, the required sample preparation in the past and still today is manual. A large gap was left here, with only a few exceptions with some selected techniques, for the adaptation of analytical sample preparation methods on the instrument level. The solutions provided in this textbook address this notorious gap with the integration of robotic workflows for many routine tasks. A dedicated section covers automated turnkey solutions like the analysis of pesticides, off‐odors, polyaromatic hydrocarbons (PAH), fatty acid methyl esters (FAMEs), mineral oil hydrocarbon contaminations (MOHs), or volatile organic compounds (VOCs), just to name a few that are presented in detail for reproduction.

The recent remarkable improvements in instrument sensitivity and selectivity, in particular in mass spectrometry using tandem instrumentation (MS/MS) and high mass resolution and accurate mass capabilities (HR/AM), greatly facilitated the miniaturization of such methods, allowing greener analytical chemistry with the reduction of sample sizes and solvent use. This sample volume reduction to the micro‐scale opens up the way for compatible, instrument‐top automation and further standardization of multi‐methods. This welcome trend answers also the demand for improved data quality and higher sample throughput, especially in food, environmental, and pharmaceutical safety analyses.

Another important aspect and a strong improvement is seen in the hyphenation of instruments with workflows integrating the currently separated processes. The availability of online sample preparation, which finally includes the transfer and injection of the prepared extract to the analytical instrument, is today still the exception in analytical instrument design. Ongoing technical development starts covering some selected sample preparation procedures with benchtop or instrument top‐mounted robotic preparation systems. The integrated software control for ease of use with just one sample acquisition sequence on screen is still a big but solvable gap to comply with operator demands and the required error‐free operation. Several independent developments of integrated software control of the robotic sample processing systems with external devices and the hyphenated analytical instruments demonstrate successfully the benefits and feasibility.

And, even in light of the continuously increasing laboratory automation, a necessary practical remark at this point for the hands‐on laboratory work, for safety and a green analytical chemistry: good laboratory practice dictates that all who handle solvents and chemicals should familiarize themselves with the compounds' material safety data sheets (MSDS) and manufacturer's recommendations for handling, use, storage, and disposal of the used chemicals.

References

1

McNally, M.E.P. (2013). Sample preparation: the state of the art.

LCGC Europe

26 (2): 110–112.

2

Majors, R.E. (1991). Overview of sample preparation.

LC‐GC The Magazine of Separation Science

9 (1): 16–20.

3

Google Scholar (2018). scholar.google.com/, search for “sample preparation” in years “2008–2018” (20 April 2018).

4

Hein, H. and Kunze, W. (2004).

Umweltanalytik mit Spektroskopie und Chromatographie

. 3rd Ed., Weinheim, Germany: Wiley-VCH.

5

Lehotay, S.J., Mastovska, K. et al. (2008). Identification and confirmation of chemical residues in food by chromatography‐mass spectrometry and other techniques.

TrAC Trends in Analytical Chemistry

27 (11): 1070–1090.

https://doi.org/10.1016/j.trac.2008.10.004

.

6

U.S. Department of Energy (2009).

DOE Standard – Human Performance Improvement Handbook – Vol. 1 Performance and Principles

, vol. DOE‐HDBK‐1. Washington, DC, USA:

www.hss.energy.gov/nuclearsafety/ns/techstds/

.

7

Raynie, D.E. (2016). Trends in sample preparation.

LCGC Europe

29 (3): 142–154.

2The Analytical Process

The simplification of sample preparation and its integration with both sampling and convenient introduction of extracted components to analytical instruments is a significant challenge and an opportunity for the contemporary analytical chemist.

Janusz Pawliszyn [1]

2.1 Laboratory Logistics

All chemical analyses comprise a series of consecutive steps starting with the sample collection, transfer into the laboratory (if not analyzed onsite), registration, and allocation to one or more analytical testing groups as needed [2]. The first task, of utmost importance in the individual sample processing, is the preparation of a representative test portion of the sample [3]. The sample preparation follows with extraction, clean‐up, and/or concentration for the instrumental measurement. Data analysis and reporting conclude the usual laboratory procedures (Table 2.1).

Table 2.1 Steps in laboratory chemical analysis.

Bold‐faced steps are subject to automated instrumental workflows.

Looking at the sample processing steps in detail, we can distinguish those that are related to the bulk sample (lot, primary sample) typically with, for instance, freezing, cutting, cryogenic comminution, blending or milling, and finally dividing to achieve a homogeneous as possible laboratory test portion for analysis, the analytical sample [4–6]. It is mandatory to start with sufficiently large sample quantities in the gram to kilogram range (except for ab initio homogeneous samples water, beverages, blood, etc.), which are handled manually using appropriate homogenization equipment [7, 8]. The initial sample processing to achieve representative test portions of the original sample is of paramount importance, “or else the entire analysis is not just time, money, and effort wasted, it actually can provide false results to deceive and undermine the entire point of the analysis” [9]. The focus on careful sample processing prior to analysis is becoming even increasingly crucial for automated sample preparation and high‐throughput analysis of only small test portions on the milligram level. Even the best recoveries and most precise analysis cannot correct for errors and bias occurring during the initial sample processing.

Most of the frequently applied sample preparation steps, many of them have to be performed in sequence, have the inherent potential to be transferred to integrated sample preparation platforms using appropriately programmed workflows and tools [10]. A recent survey identified the most frequently used sample processing steps in analytical laboratories [11]. The results are shown in Figure 2.1. The area in this graphics represents the frequency of use. The blue highlighted boxes express their potential for workflow automation on integrated sample preparation platforms.

Figure 2.1 Frequently used sample preparation steps and their potential for workflow automation on integrated sample preparation platforms. Processes in blue can be automated more easily than those in red.

Source: Adapted from Majors [12].

A constant trend with a continued increasing or decreasing frequency of application is reported over the last two decades for the ten most important sample preparation techniques [11, 13]:

Evaporation

↗

Centrifugation

↗

Filtration

↗

Sonication

∼

Vortexing

∼

pH Adjustment

∼

Weighing

∼

Column chromatography

↘

Dilution

↘

Liquid–liquid extraction

↘

(with ∼ stable, ↗ increasing, and ↘ decreasing use)

Concerning the analysis techniques used in environmental and food safety analysis, Steven J. Lehotay and Yibai Chen analyzed the current trends based on the frequency of scientific publications during the last two decades [14]. Relating to the application area of publications, the subjects on food safety analyses outperform the former focus on environmental analysis. While gas chromatography (GC) slightly declined in attention over the years, a constant growth of liquid chromatography (LC