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Laboratory 4.0, the internet of things (IoT), big data, cloud computing or storage, blockchain, cybercrime, thingbots, predictive maintenance, artificial intelligence et al.: the thicket of buzzwords is getting denser almost by the day. They feature in just about every newspaper article about digitization and where IT is taking us. The underlying technologies are highly complex; understanding how they interact is difficult. It is becoming a challenge to take business decisions based on the little we know and understand. Digital transformation has long reached the laboratories. Some of the process’s key outcomes, interconnectedness and intelligent machines and devices, are already enabling forward-looking solutions and more efficient workflows.
Let us start by looking at the pivotal topic of data security. Digitization generates electronically processible data from analog information. This data is a valuable asset that needs to be protected as much as computer systems, hardware or software. The internet and our local area networks offer many opportunities for using malware to tap data illegally or to infect and manipulate systems. IoT instruments that can be infected without leaving a trace carry a particular high risk [1]. The company Cybersecurity Ventures estimates that, by 2021, the damage through cybercrime will reach $ 6 trillion per year [2]. In 2017, 86.4 billion US dollars were spent on data security alone. Judging by past cyberattacks, there is no doubt that sensitive or valuable data is hard to protect and that this requires professionals. Despite all the enthusiasm about digitization, there should always be a focus on data security, privacy and related issues.
The term “internet of things” (IoT) stands for linking up “intelligent” objects – i.e. ones equipped with processors and embedded sensors – to each other and to the internet so they can communicate and act autonomously. In industry, the term “industrial internet of things” (IIoT) has become established. The technologies subsumed under IoT form the basis for the so-called fourth industrial revolution, which in German is referred to as “Industrie 4.0” (industry 4.0). The most popular IIoT application examples were identified in a survey by the IoT Institute, New York, among 73 experts. (Fig. 1) [3]. A lot can already be learned from this about today’s state-of-the-art laboratories.
Fig. 1 A selection of the most popular IIoT applications according to a survey by the IoT Institute, New York, among 73 experts [3]
A key aspect is that individual appliances of a work or production workflow make available their stored information as well as the data collected by their embedded sensors to the other components, without a worker or technician having to note anything on a clipboard that requires transmitting from one to the other. However, it is not possible to meet the regulatory requirements on the traceability of data by passing on mere results. A complete set of information about the instrument and its user (User ID), including the calibration state, serial number, time and date, has to be part of any transmitted result. Noting such “metadata” on paper and passing them on can slow down the process considerably and involve the risk of transmission errors. This is why there is a need to equip instruments, appliances and components with the ability to communicate and automatically pass on these metadata as well.
In early 2018, “blockchain” was predicted to become one of the key trends of the year [4, 5]. It is something like a decentralized, open database. Considered to be tamper-proof and invulnerable, the technology does not require an administrator. The visibility of the data can be assigned, they are verifiable. The most well-known example of blockchain is undoubtedly the cryptocurrency Bitcoin. Blockchain technology adds new data records to an existing chain of data records. The new information is cryptographically linked to the information of the previous record. Each entry is provided with a date and time stamp, and the user information is also recorded. Blockchain may seem like the ideal technology for lab data. Key data integrity requirements, as summarized in WHO guidelines and the FDA’s cGMP regulations as the ALCOA principle (attributable, legible, contemporaneous, original, accurate) [6, 7], would be met. However, using a blockchain platform based on the Bitcoin model to develop data processes for laboratory analyses is problematic due to the enormous energy consumption, see Digiconimist [8]. In addition, reports have appeared suggesting that blockchain processes are increasingly being focused by hackers [9]. But blockchain technology is also developing further. It is quite conceivable, for example, that future standards can be integrated into Mettler-Toledo workflows with added value such as increase confidence in the data integrity of laboratory processes.
“Big data” is another huge hype. It’s about collecting all the data you can get your hands on. The ambition is to analyze them at a later stage by processing them with suitable algorithms in order to recognize patterns that the individual data sets do not reveal. Unfortunately, big data is a bit like a gold rush. Your shovel rarely surfaces a nugget, and even if you do recognize a pattern, it will not automatically give you the answer you are hoping to get. A successful example of lab-related big data is DNA sequencing, which has revolutionized the life sciences. Due to ever more sensitive and automated measurement methods as well as to increased digitization, huge amounts of data are being generated. The challenge is to use appropriate tools to leverage the wealth of information contained in the masses of data and to transform them into useful knowledge.
For manufacturers of laboratory equipment, the key question is how users can benefit from the trends in digitization while minimizing the risks. A company like Mettler-Toledo considers carefully which new concepts have potential. In product development, for example, instrument components have been given a degree of intelligence so they can contribute meaningfully to the flow of data. However, the components are still “stupid” enough not to be misused as thingbots. The RFID (radio frequency identification) technology used transmits data via radio detection. The RFID chips have a small memory and can be written on and read without physical contact. They draw the power they need from induction.
Fig. 2 Data are transmitted contactlessly via radio frequency identification (RFID).
Mettler-Toledo uses RFID technology for its autotitrators, for example. The burets are able to learn and pass on which chemical feeds they are connected to, what the nominal concentration and what the titer currently is, i.e. the exact concentration. pH sensors learn, too, and transfer calibration data, including the date and time, to ensure that no scheduled calibration event is ignored. As a consequence, no invalid results can be generated due to missing, outdated or incorrectly transmitted calibration data (Fig. 2).
RFID is also used in cross-device data flows. Most sample flows begin with accurately weighing the sample. Rather than having to note it on paper or print it out, the weight – together with some metadata – is placed on an RFID chip under the sample vessel (e.g. a titration cup), and henceforth becomes a SmartSample (Fig. 3).
Fig. 3 State-of-the-art RFID technology makes samples intelligent and enables cross-device automated data flows. Weighing data and metadata are stored on an RFID chip under the sample container, e.g. a titration beaker.
This information is automatically imported by the instrument of the next step, for example an autotitrator, without any need to intervene manually – no typos and no mix-ups, as everything is automatically and unambiguously documented.
So-called “SmartChemicals” are currently in the development stage. In collaboration with the manufacturers of titration agents and standards, work is under way to equip their bottles with RFID chips that carry data such as identity, batch number, manufacture date and shelf life. This would ensure easy traceability and cover all relevant details.
Another RFID application example is micro pipettes. These appliances can be rather unfaithful: when put aside for a due calibration, they like disappear out of sight to another lab (Fig. 4).
Fig. 4 An all too frequent issue: where’s my pipette? An intelligent pipette station, connected to a computer, solves the problem and keeps an eye on all the pipettes connected wirelessly via RFID. An example of asset management based on IIoT.
One solution is an intelligent pipette station. The SmartStand, for example, contains an RFID reader that can read the corresponding chips of the micro pipettes. The status is shown on a display, and the connected software allows each micro pipette to be localized. This technology helps the laboratory follow standard procedures and maintain GLP/GMP compliance.
Even cable ends can be given learning abilities. When operating a large laboratory reactor, for which several components (such as for dosing, stirring, measuring various parameters, etc.) must be put together, it can be set up much easier using the SmartConnect technology, which uses a control box as its hub. SmartConnect stores an earlier configuration in the cables and allows the system to be reconfigured identically with little effort, controlled during use and their data delivered to the IC data center.
Even without RFID, intelligence and automatic read-out capabilities can be bestowed on components. If a balance is to be tested with an external calibration weight, a weight marking system can be used. Etched as a QR code onto the underside of the test weight is a unique identification number. The calibration certificate is deposited on a plastic storage card, also as a QR code. By scanning in first the certificate and then the weight, the information is automatically processed.
The digital transformation depends heavily on software. However, software validation is time-consuming, and so is the maintenance, including the installation of updates and so on. Is all this worth the effort for such simple appliances as scales or pH meters? Laboratories for pharmaceutical products increasingly believe it is. The regulatory pressure on cGMP processes has increased so much in recent years that electronic data logging is very much trending.
Fig. 5 Up to 80% of laboratory equipment can already be digitally linked to a central laboratory data processing system with two standard software solutions when using a chromatography data system (CDS) and LabX. This simplifies implementation (including validation, etc.) and maintenance.
Device control software such as LabX allows a large number of laboratory instruments to be controlled. Thus, balances (including an automatic high-precision dosing system), pH meters (including conductivity, ions, etc.), titrators, Karl Fischer titrators, UV/VIS spectrometers, refractometers, density and melting point devices can be integrated into the data system. Customers estimate their proportion to now be over 40% of their total laboratory equipment. Algorithms optimize the results. User and method management with audit trail and raw data archiving make this an important component of the digitized laboratory (Fig. 5).
To protect the control software, the central memory is designed so that the various small displays on the devices are only “clients” of the central LabX server, although using them is almost like in a “stand alone” mode. “Capture at Origin” means that the individual device no longer stores data such as results, timestamps or method programs. The lengthy synchronization of time measurement has thus become obsolete and it is no longer possible to work with the wrong method version. Also, the audit trail can be managed centrally. User administration supports “Active Directory” and can therefore be linked to the administration of Windows. Especially in times when we constantly need to change our passwords, this makes life so much easier for users and administrators. The central LabX memory can be secured by any current method. Not more, but not less either.
You do not have to be a prophet to understand that digitization has found its way into the lab and that an instrument’s expected useful life depends on how smart and connected it is.
Footnote: LabX, SmartSample, SmartStand and SmartConnect are trademarks of the Mettler Toledo group.
Literature:
[1] Riepen A., https://www.industry-of-things.de/neue-gefahr-thingbots-a-655363, 2017 Oct 23 (accessed on 2018 May 29)
[2] Morgan, S., https://cybersecurityventures.com/hackerpocalypse-cybercrime-report-2016/, 2017 Oct 16 (accessed on 2018 May 29)
[3] Schreier, J., https://www.industry-of-things.de/die-neun-beliebtesten-iiot-use-cases-a-658485/, 2017 Nov 02 (accessed on 2018 May 29)
[4] Schreier, J., https://www.industry-of-things.de/blockchain-wird-2018-das-iot-revolutionieren-a-671563/, 2017 Dec 14 (accessed on 2018 May 29)
[5] Streim, A., https://www.bitkom.org/Presse/Presseinformation/Blockchain-wird-zu-einem-Top-Thema-in-der-Digitalwirtschaft.html, 2018 Feb 20 (accessed on 2018 May 29)
[6] U.S. Department of Health and Human Services, Food and Drug Administration, https://www.fda.gov/downloads/drugs/guidances/ucm495891.pdf, 2016 April (accessed on 2018 May 29)
[7] Food and Drug Administration, www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm, 2018 Mar 16 (accessed on 2018 May 29)
[8] Digiconomist, https://digiconomist.net/bitcoin-energy-consumption (accessed on 2018 May 29)
[9] cash zweiplus ag, https://www.cash.ch/news/politik/it-sicherheit-kudelski-warnt-vor-cyberattacken-auf-blockchain-und-cloud-1170767, 2018 May 07 (accessed on 2018 May 29)
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