Data: Instrumentation Building a Smart Laboratory 2017


yield good results, but problems can occur if they are not managed and checked for each run.


Instrument data management


Te issue of instrument data management is a significant one and requires considerable planning. Connecting instruments to a LIMS or ELN is a common practice, though oſten not an easy one if the informatics vendor hasn’t provided a mechanism for interfacing equipment. Depending on how things are set up, only a portion of the information in the instrument data system is transferred to the informatics system. If the transfer is the result of a worklist execution of a quantitative analysis, only the final result may be transferred – the reference data still resides on the instrument system. Te result is a distributed data structure. In regulated environments, this means that links to the backup information have to be maintained within the LIMS or ELN, so that it can be traced back to the original analysis. Te situation becomes more interesting when instrument data systems change or are retired.


Type of A/D


Successive approximation


Capability


These are general-purpose devices suitable for a wide range of applications. They have limited resolution, but have amplifiers for low-level signals, and can sequentially access multiple input channels. Their resolutions are up to 18 bits (262,144 steps) and sampling speeds of up to five million samples per second (sps). The higher the resolution, the slower the sampling speed.


Integrating


Good for low speed sampling (<100 sps), high resolution >14 bits, single channel inputs, with good noise rejection. Often used in chromatography.


Sigma-Delta A/D


Up to 24 bits of resolution, single channel input – may not be efficient for multi- channel inputs, low speed, may replace integrating A/Ds.


Flash


Single channel input, 8-bit conversion, approximately 1 billion SPS. Good for very high-speed applications, where low resolution is not a problem. You can digitise electrical noise.


16


Property to be


measured (detector)


Electrical circuit


converting properly to voltage


A/D


Control processor


Communications Fig. 2: Analogue data acquisition Display


Product packaging


Access still has to be maintained to the data those systems hold. One approach is virtualising the instrument data system so that the operating system, instrument support soſtware, and the data are archived together on a server. (Virtualisation is, in part, a process of making a copy of everything on a computer so that it can be stored on a server as a file or ‘virtual container’ and then executed on the server without the need for the original hardware. It can be backed up or archived, (so that it is protected from loss). In the smart laboratory, system management is a significant function – one that may be new to many facilities. Te benefits of doing it smartly are significant.


Computer-controlled experiments and sample processing


Adding intelligence to lab operations isn’t limited to processing instrument data, it extends to an earlier phase of the analysis: sample preparation. Robotic systems can take samples – as they are created – and transfer the format to that needed by the instrument. Robotic arms – still appropriate for many applications – have been replaced with components more suitable to the task, particularly where liquid handling is the dominant activity, as in life science applications. Success in automating sample preparation depends heavily on thoroughly analysing the


process in question and determining: l Whether or not the process is well documented and understood (no undocumented short-cuts or workarounds that are critical to success), and whether improvements or changes can be made without adversely impacting the underlying science;


l Suitability for automation: whether or not there are any significant barriers (equipment, etc.) to automation and whether they can be resolved;


l Tat the return in investment is acceptable


Digital I/0 (switches, LEDs, etc.)


and that automation is superior to other alternatives such as outsourcing, particularly for shorter-term applications; and


l Tat the people implementing the project have the technical and project management skills appropriate for the work.


Te tools available for successfully implementing a process are clearly superior to what was available in the past. Rather than having a robot adapt to equipment that was made for people to work with, equipment has been designed for automation – a major advance. In the life sciences, the adoption of the microplate as a standard format multi- sample holder (typically 96 wells, but can have 384 or 1,536 wells – denser forms have been manufactured) has fostered the commercial availability of readers, shakers, washers, handlers, stackers, and liquid additions systems, which makes the design of preparation and analysis systems easier. Rather than processing samples one at a time, as was done in early technologies, parallel processing of multiple samples is performed to increase productivity. Te world is a bigger place than life sciences,


and other equipment has been developed to support analytical work. Te basic auto-sampler used to inject samples into instruments has been upgraded to address internally standard additions: heating, stirring, dissolution, derivitisation, chilling stations, headspace analysis, and barcode readers. Another area of development is the ability to


centralise sample preparation and then distribute the samples to instrumentation outside the sample prep area through pneumatic tubes. Tis technology offers increased efficiency by putting the preparation phase in one place so that solvents and preparation equipment can be easily managed, with analysis taking place elsewhere. Tis is particularly useful if safety is an issue. Te sample vials can be returned to a centralised disposal area. Across the landscape of laboratory types and industries, the application of sample preparation


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