Building a Smart Laboratory 2017


robotics is patchy at best. Success and commercial interest have favoured areas where standardisation in sample formats has taken place. Te development of microplate sample


formats, including variations such as tape systems that maintain the same sample cell organisation in life sciences, and standard sample vials for auto- samplers, are common examples. Standard sample geometries give vendors a basis for successful product development if those products can have wider use rather than being limited to niche markets.


Putting the pieces together


It’s not enough to consider in isolation sample preparation, the introduction of samples into instruments, the instruments themselves, and the data systems that support them. Linking them together provides a train of tasks that can lead to an automated sample processing system as shown in Figure 3. Te control/response link is needed to


synchronise sample introduction and data acquisition. Depending on the nature of the work, that link can extend to sample preparation. Te end result is a system that not only provides higher productivity than manual methods, but does so with reduced operating costs (aſter the initial development investment). However, building a smart laboratory needs to


look beyond commonplace approaches and make better use of the potential that exists in informatics technologies. Extending that train of elements to include a LIMS, for example, has additional benefits. Te initial diagram above would result in a worklist of samples with the test results that would be sent to a LIMS for incorporation into its database. Suppose there was a working link between


a LIMS and the data system that would send sample results individually, and that each sample processed by the instrument would wait until the data system told it to go ahead. Te LIMS has the


Fig. 3: An automated sample processing system


expected range for valid results and the acceptable limits. If a result exceeded the range, several things


could happen: l Te analyst would be notified; l Te analysis system would be notified that the test should be repeated to confirm the result;


l If confirmed, standards would be run to confirm that the system was operating properly; and


l If the system were not operating according to SOPs, the system would stop to avoid wasting material and notify the analyst.


Te introduction of a feedback facility would significantly improve productivity. At the end of the analysis, any results that are outside expected limits would have been checked and the systems integrity verified. Making this happen depends on connectivity and the ability to integrate the components.


Instrument integration


In order for the example described above to work, components must be connected in a way that permits change without rebuilding the entire processing train from scratch. Information technology has learned those lessons repeatedly as computing moved from proprietary products and components to user-friendly consumer systems. Consumer level systems aren’t any less capable


than the earlier private-brand-only systems, they are just easier to manage and smarter in design. Small Computer Systems Interconnect,


Firewire and Universal Serial Bus are a few examples of integration methods that enabled the user to extend the basic capability and have ready access to a third-party market of useful components. It also allowed the computer vendors to


concentrate on their core product and satisfy end-user needs through partnerships; each vendor could concentrate on what they did best and the resulting synergy gave the users what they needed. Now these traditional methods are being


Control/response


Data: Instrumentation


surpassed by the IOT or wireless connected devices but the argument for connecting devices still remains the same – is the value added worth the investment? Te answer depends on the instrument, but generally it is more effective to connect the most widely used instruments such as PH meters and weighing scales. Connections are only part of the issue. Te


more significant factor is the structure of the data that is being exchanged: how it is formatted; and the organisation of the content. In the examples above, that is managed by the


use of standard device drivers or, when called for, specialised device handlers that are loaded once by the user.


“Building a smart laboratory needs to look beyond commonplace approaches and make better use of the potential that exists in informatics technologies”


In short, hardware and soſtware are designed


for integration, otherwise vendors find themselves at a disadvantage in the marketplace. Laboratory soſtware comes with a different


mindset. Instrument support soſtware was designed first and foremost to support the vendor’s instrument and provide facilities that weren’t part of the device, such as data analysis. Integration with other systems wasn’t a factor. Tat is changing. Te increasing demand


for higher productivity and better return on investment has resulted in the need for systems integration to get overall better systems performance; part of that measure is to reduce the need for human interaction with the system.


Integration should result in: l Ease-of-use: integrated systems are expected to take less effort to get things done;


l Improved productivity, streamlined operations: the number of steps needed to accomplish a task should be reduced;


Instrument Sample preparation www.scientific-computing.com/BASL2017 Sample introduction


Data acquisition, analysis, reporting, storage, etc


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