11


Why is Automated Sample Preparation Controlled? SmartSPE®


is an emblematic example of the power of controlled automated sample


preparation. In fact, one of the main limitations of manual SPE is the lack of fl ow control (the famous so to speak ‘drop rule’). This fl ow variability often translates in absolute recovery variability. On the contrary, the automation of online SPE using ITSP (Instrument Top Sample Preparation) single use miniaturised cartridges allows extremely precise fl ow control (down to 0.1µL/s), and this attains chromatographic performance which is not accessible with a manual method. Miniaturised cartridges are packed with customised sorbent to match the material in the standard size SPE cartridges. They come in a 96-well format, and they are handled by the robotic platform by means of the syringe needle. In SmartSPE®


fl ow profi les follow the expected Van Deemter curves with clearly defi ned optima [3] as shown in Figure 3.


as double bonds and branch–points. On the other hand, fatty acids picolinyl esters are a better option for structural elucidation. In these derivatives, it’s a nitrogen atom to carry the charge rather than the alkyl chain and this aspect minimises double bonds ionisation and migration, helping to provide very informative structural information. To perform the DoE, four factors were chosen together with their respective range and picolinyl derivatives peak area were chosen as responses. DoE generates a matrix of experiments where trials are run at all possible combinations of all factors settings. The response is recorded for each of the experiments. Using of hyphenated and automated sample preparation makes the experimental side of any DoE conveniently and consistently delivered. As a standard DoE approach, fi rstly scoping trials were performed to estimate the experimental random variation and evaluate the experimental design space. The scoping trials usually comprise a minimum of four trials: low, where all variables are set at the lowest level, centre points where all variables are set in the middle of the experimental range and high where all variables are set at the highest level. Centre points allow to evaluate for non-linearity and random variation. The pilot trials confi rmed signifi cant variability in the experimental space worthwhile to further investigate. Secondly, screening designs were used to differentiate signifi cant factors and fi nd existing interactions between factors. Statistical tools helped with the interpretation of the results. The screening design highlighted a signifi cant effect of temperature suggesting quadratic behaviour. To confi rm the trend, an optimisation step was conducted to fi nalise the relationship between the different factors and the response. Once the optimal conditions were confi rmed, the process was run in these conditions in replicates to assess robustness and variability. Figure 4 shows the results for repeatability and reproducibility obtained for a fatty acid mixture C4 to C22.


Figure 3. The SmartSPE® defi ned optima.


fl ow profi les follow the expected Van Deemter curves with clearly


As an outcome of the accurate fl ow control, absolute recoveries of >99% can be achieved along with a signifi cant reduction in background matrix. The importance of fl ow control is even more crucial when using ion exchange SPE. In fact, according to the fl ow optimisation studies done by Hayward, the range of optimal fl ow is much narrower, and recovery can drop drastically if that optimum is not met. Controlled automation of smart SPE-GC-MS was applied to the determination of four forensically relevant opiates in blood. Opiates encompass both natural and semi-synthetics alkaloids. They are found in the opium poppy plant and are used for the treatment of acute pain. Unfortunately, they are highly addictive and are therefore considered globally as drugs of abuse. The manual workfl ow involves an initial protein crash, a cation exchange SPE to enrich analyte concentration and reduce matrix interference followed by evaporation and silylation prior to GC-MS analysis. For this study, spiked blood samples were prepared and extracted using SmartSPE®


and then injected directly onto the GC-MS. Sample batches included blanks


with internal standard and fi ve calibrators which were prepared over three separate days to evaluate robustness and reproducibility. Linearity was excellent and error bars showed particularly good inter days variability. Recoveries were above 90% and signal to noise at the lowest calibration point was above 10 for all analytes except for 6-MAM.


Why is Automated Sample Preparation Consistent?


Consistency is a key requirement for analytical approaches which use data analytics to drive method development and data mining. Hence, automation offers a strong synergism with many valuable data analytics tools. Design of Experiments (DoE) is a systematic approach to determine the relationship between factors affecting a process and the output of that process. Once the relationship between the factors and the process has been established, this information can be used to optimise the response. It must become clear how essential is a consistent control of the factors to reduce to the minimum analytical variability. In fact, high analytical variability would mask the information produced by the investigation of the experimental space. This example describes the use of automation to perform design of experiments. DoE was used for the optimisation of fatty acids derivatisation to picolinyl derivatives. Derivatisations often involve optimisation of several parameters to achieve the best performances and therefore they offer a very good situation for DoE to shine. Traditionally, fatty acids are analysed by GC-MS using their methyl ester derivatives (FAMEs). However, FAMES mass spectra cannot provide ions indicative of structural features such


Figure 4. The results for repeatability and reproducibility obtained for a fatty acid mixture C4 to C22


A certain variability across the range of alkyl chain could be observed but this is understandable since the optimal conditions were selected as the best compromise across the whole target responses.


Conclusions


Automated sample preparation is an immensely powerful analytical tool which lends itself to several synergic combinations which can deliver remarkably high performances at different stages of the analytical workfl ow. Sample preparation and method development are essential yet incredibly challenging analytical aspects which can benefi t signifi cantly from the use of automation. In fact, the appeal of automation doesn’t lie exclusively in very good method robustness and batch-to-batch reproducibility. The extremely accurate fl ow control in liquid handling and the ability to control timing accurately (e.g., incubation time for derivatisation purposes) open the doors to what could be considered ‘high performance’ sample preparation. Convenient, control and consistency make indeed hyphenated automated sample preparation the missing hyphen to any system hypernation.


References 1. Hirschfeld, T. Analalytical Chem. 1980, 52, 297A−312A 2. Wilson I.D. et al., J. Chromatogr. A, 2003, 1000(1-2): 325-326 3. Hayward M. et. al. Am. Lab., 2016, 48(7): 14-17.


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