Data: Instrumentation


to a LIMS or ELN, make the connections as simple as possible. If an instrument supported by the soſtware needs to be replaced, changing the connection will be simpler. Licence costs are also a factor. Dedicated


formats require a licence for each system. Shared- access systems have more flexible licensing considerations. Some have a cost per user and connected instrument; others have a cost per active user/instrument schedules. In the latter case, there are eight instruments


and four analysts, of which only half may be simultaneously active, licenses for only four instruments and two users are needed. One factor that needs attention is the


education of laboratory staff in the use of computer-instrument systems. While instrument soſtware systems are


capable of doing a great deal, their ability to function is oſten governed by user-defined parameters that affect, at least in chromatography, baseline-corrections, area allocation for unresolved peaks, etc. Carefully adjusted and tuned parameters will yield good results, but problems can occur if they are not managed and checked for each run.


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 FIG 2 Display


Property to be


measured (detector)


Electrical circuit


converting properly to voltage


A/D


Building a Smart Laboratory 2018 Analogue data acquisition


Control processor


Communications


Product packaging


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


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. 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: nWhether 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;


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


nTat the return in investment is acceptable and that automation is superior to other alternatives such as outsourcing, particularly for shorter-term applications; and


nTat 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. Another area of development is the ability to centralise sample preparation and then distribute


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