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==Sandbox begins below==
==Sandbox begins below==
<div class="nonumtoc">__TOC__</div>
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'''Title''': ''What are the key elements of a LIMS for animal feed testing?''


==1. What is cloud computing?==
'''Author for citation''': Shawn E. Douglas
[[File:1000px-Cloud computing.svg.png|right|650px|thumb|'''Figure 1.''' A basic visualization of [[cloud computing]] architecture layers and some of the activities that take place on those layers.]]
If you were alive in the late 2000s and doing most anything related to computers and the internet, you were bound to encounter the latest internet buzzword: [[cloud computing]].<ref name="PogueInSync08">{{cite web |url=https://www.nytimes.com/2008/07/17/technology/personaltech/17pogue.html |archiveurl=https://web.archive.org/web/20180105205750/https://www.nytimes.com/2008/07/17/technology/personaltech/17pogue.html |title=In Sync to Pierce the Cloud |author=Pogue, D. |work=The New York Times |date=17 July 2008 |archivedate=05 January 2018 |accessdate=28 July 2023}}</ref><ref name="WangCloud10">{{cite journal |title=Cloud Computing: A Perspective Study |journal=New Generation Computing |author=Wang, L.; von Laszewski, G.; Younge, A. et al. |volume=28 |pages=137–46 |year=2010 |doi=10.1007/s00354-008-0081-5 |url=https://scholarworks.rit.edu/cgi/viewcontent.cgi?article=1748&context=other}}</ref> A certain mysticism was seemingly attached to the concept, that your files and applications could reside on the internet, "out there in the 'cloud.'"<ref name="PogueInSync08" /> "But what is this 'cloud'?" many would ask. A plethora of media articles, journal articles, blogs, and company websites were published to give practically everyone's take on what the cloud was and wasn't meant to be.<ref name="ChamberlinCloud08">{{cite web |url=https://www.billchamberlin.com/cloud-computing-what-is-it/ |title=Cloud Computing: What is it? |author=Chamberlin, B. |work=BillChamberlin.com |date=28 October 2008 |accessdate=28 July 2023}}</ref> However, the then growing consensus of cloud computing as networked and scalable architecture meant to rapidly provide application and infrastructure services at reasonable prices to internet users<ref name="WangCloud10" /><ref name="ChamberlinCloud08" /> largely matches up with today's definition. Pulling from both The Institution of Engineering and Technology<ref name="FrenchCloud21">{{cite web |url=https://www.theiet.org/publishing/inspec/researching-hot-topics/cloud-computing-and-web-services/ |title=Cloud computing and web services |author=French, J. |publisher=The Institution of Engineering and Technology |date=2021 |accessdate=28 July 2023}}</ref> and Amazon Web Services<ref name="AWSCloud21">{{cite web |url=https://aws.amazon.com/what-is/cloud-native/ |title=What Is Cloud Native? |publisher=Amazon Web Services |accessdate=28 July 2023}}</ref>, we come up with cloud computing as:


<blockquote>an internet-based computing paradigm in which standardized and [[Virtualization|virtualized]] resources are used to rapidly, elastically, and cost-effectively provide a variety of globally available, "always-on" computing services to users on a continuous or as-needed basis</blockquote>
'''License for content''': [https://creativecommons.org/licenses/by-sa/4.0/ Creative Commons Attribution-ShareAlike 4.0 International]


Of course, those computing services come in a variety of flavors, the most common being [[Software as a service|software]], [[Platform as a service|platform]], and [[Infrastructure as a service|infrastructure]] "as a service" (SaaS, PaaS, and IaaS, respectively). These conveniently correspond to the underlying architectural layers of the services, with infrastructure at the base, platform on top of that, and software (or application) on top of that.
'''Publication date''': May 2024


Figure 1 portrays a simplified visualization of cloud computing architecture layers, as well as examples of activities that happen on those layers. This concept has also been visualized by others using pyramids and pancake stacks of layers, but the concept remains the same. At the base is the computing infrastructure, including the physical data centers and their networking equipment, servers, [[hypervisor]]s, [[application programming interface]]s (APIs), and operating systems. This infrastructure is the foundation that supports not only applications users want to run but also that acts as the developmental foundation of users not wanting to implement their own infrastructure. On top of all that can be found platforms or [[middleware]], which serve as software development and deployment environments (that include databases, web servers, load balancers, etc.) or connectivity tools for analytics, [[workflow]] management, system integration, and security management. And on top of that are applications, typically designed to run optimally in cloud environments and accessed via web browsers or apps using internet—i.e. networking—connectivity and computing devices.<ref name="MaurerCloud20">{{cite web |url=https://carnegieendowment.org/2020/08/31/cloud-security-primer-for-policymakers-pub-82597 |title=Cloud Security: A Primer for Policymakers |author=Maurer, T.; Hinck, G. |publisher=Carnegie Endowment for International Peace |date=31 August 2020 |accessdate=28 July 2023}}</ref>
==Introduction==


Customers who require application hosting, internet-hosted software development platforms, or underlying computing infrastructure (e.g., data storage, computational time, etc.)—particularly when they can't or don't want to invest in their own hardware—are increasingly turning to the cloud computing paradigm. Even before a worldwide [[COVID-19]] [[pandemic]] started to take shape in late 2019, the global cloud services market was expected to reach $266.4 billion by the end of 2020, with Gartner expecting that to represent a 17 percent increase from 2019.<ref name="CostelloFore19">{{cite web |url=https://www.gartner.com/en/newsroom/press-releases/2019-11-13-gartner-forecasts-worldwide-public-cloud-revenue-to-grow-17-percent-in-2020 |title=Gartner Forecasts Worldwide Public Cloud Revenue to Grow 17% in 2020 |author=Costello, K.; Rimol, M. |publisher=Gartner |date=13 November 2020 |accessdate=28 July 2023}}</ref> As work-from-home practices expanded significantly in 2020 due to the pandemic, expectations that the trend would last post-pandemic pushed estimates of overall cloud-based workloads moving from physical work offices to the cloud to 55 percent by 2022, with the cloud services market reaching $600 billion in 2023<ref name="GartnerFore23">{{cite web |url=https://www.gartner.com/en/newsroom/press-releases/2023-04-19-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-reach-nearly-600-billion-in-2023 |title=Gartner Forecasts Worldwide Public Cloud End-User Spending to Reach Nearly $600 Billion in 2023 |publisher=Gartner |date=19 April 2023 |accessdate=14 August 2023}}</ref> and $1 trillion by 2030.<ref name="ReinickeThree20">{{cite web |url=https://markets.businessinsider.com/news/stocks/wedbush-reasons-own-cloud-stocks-coronavirus-pandemic-tech-buy-2020-3-1029045273#2-the-move-to-cloud-will-accelerate-more-quickly-amid-the-coronavirus-pandemic2 |title=3 reasons one Wall Street firm says to stick with cloud stocks amid the coronavirus-induced market rout |author=Reinicke, C. |work=Market Insider |date=30 March 2020 |accessdate=28 July 2023}}</ref> This growing migration to cloud computing has many implications for organizations of all types, including [[Laboratory|laboratories]].


This brief topical article will examine ...


===1.1 History and evolution===
'''Note''': Any citation leading to a software vendor's site is not to be considered a recommendation for that vendor. The citation should however still stand as a representational example of what vendors are implementing in their systems.
Cloud computing has its strongest origins in the "web services" phase of internet development. In November 2000, Mind Electric CEO and [[distributed computing]] visionary Graham Glass, writing for IBM, described web services as "building blocks for creating open distributed systems" that "allow companies and individuals to quickly and cheaply make their digital assets available worldwide," while prognosticating that web services "will catalyze a shift from client-server to peer-to-peer architectures."<ref name="GlassTheWeb00">{{cite web |url=http://www-106.ibm.com/developerworks/library/ws-peer1.html |archiveurl=https://web.archive.org/web/20010424015036/http://www-106.ibm.com/developerworks/library/ws-peer1.html |title=The Web services (r)evolution, Part 1: Applying Web services to applications |author=Glass, G. |work=IBM developerWorks |publisher=IBM |date=November 2000 |archivedate=24 April 2001 |accessdate=28 July 2023}}</ref> At that point, the likes of Microsoft and IBM were already developing toolkits for creating and deploying web services<ref name="GlassTheWeb00" />, with IBM releasing an initial high-level report in May 2001 on IBM's web services architecture approach. In that paper, web services were described by its author Heather Kreger as allowing "companies to reduce the cost of doing e-business, to deploy solutions faster, and to open up new opportunities," while also allowing "applications to be integrated more rapidly, easily, and less expensively than ever before."<ref name="KregerWeb01">{{cite web |url=https://www.researchgate.net/profile/Heather-Kreger/publication/235720479_Web_Services_Conceptual_Architecture_WSCA_10/links/563a67e008ae337ef2984607/Web-Services-Conceptual-Architecture-WSCA-10.pdf |format=PDF |title=Web Services Conceptual Architecture (WSCA 1.0) |author=Kreger, H. |date=May 2001 |publisher=IBM Software Group |accessdate=28 July 2023}}</ref>


Here's a recap of thinking on web services at the turn of the century:
==Feed testing laboratory workflow, workload, and information management==
A feed testing lab can operate within a number of different production, research and development (R&D&#59; academic and industry), and public health contexts. They can<ref name="WardObtain24">{{cite web |url=https://animal.ifas.ufl.edu/media/animalifasufledu/dairy-website/ruminant-nutrition-symposium/archives/12.-WardRNS2024.pdf |format=PDF |author=Ward, R. |title=Obtaining value from a feed/forage lab engagement |work=Florida Ruminant Nutrition Symposium |date=27 February 2024 |accessdate=22 May 2024}}</ref>:


* "[act as] building blocks for creating open distributed systems"<ref name="GlassTheWeb00" />
*act as a third-party consultant, interpreting analytical data;
* "quickly and cheaply make ... digital assets available worldwide"<ref name="GlassTheWeb00" />
*provide research and development support for new and revised formulations;
* "catalyze a shift from client-server to peer-to-peer architectures"<ref name="GlassTheWeb00" />
*provide analytical support for nutrition and contaminant determinations;
* "reduce the cost of doing e-business, to deploy solutions faster, and to open up new opportunities"<ref name="KregerWeb01" />
*provide development support for analytical methods;
* "[allow] applications to be integrated more rapidly, easily, and less expensively than ever before"<ref name="KregerWeb01" />
*ensure quality to specifications, accreditor standards, and regulations;
*develop informative databases and data libraries for researchers;
*manage in-house and remote sample collection, labeling, and registration, including on farms; and
*report accurate and timely results to stakeholders, including those responsible for monitoring public health.


We'll come back to that. For the next stop, however, we have to consider the case of Amazon and how they viewed web services at that time. Leading up to the twenty-first century, Amazon was beginning to expand beyond its book selling roots, opening up its marketplace to other third parties (affiliates) to sell their own goods on Amazon's platform. That effort required an expansion of IT infrastructure to support web-scale third-party selling, but as it turned out, a lot of that IT infrastructure, while reliable and cost-effective, had been previously added piecemeal, with many components getting "tangled" along the way. Amazon project leads and external partners were clamoring for better infrastructure services. This required untangling the IT and associated provider data into an internally scalable, centralized infrastructure that allowed for smoother communication and [[Information management|data management]] using well-documented APIs.<ref name="FurrierExclusive15">{{cite web |url=https://medium.com/@furrier/original-content-the-story-of-aws-and-andy-jassys-trillion-dollar-baby-4e8a35fd7ed |title=Exclusive: The Story of AWS and Andy Jassy’s Trillion Dollar Baby |author=Furrier, J. |work=Medium.com |date=29 January 2015 |accessdate=28 July 2023}}</ref><ref name="MillerHowAWS16">{{cite web |url=https://techcrunch.com/2016/07/02/andy-jassys-brief-history-of-the-genesis-of-aws/ |title=How AWS came to be |author=Miller, R. |work=TechCrunch |date=02 July 2016 |accessdate=28 July 2023}}</ref> By 2003, the company was indirectly acting as a services industry to its partners. "Why not act upon this strength?" was the sentiment that quickly developed that year, with Amazon choosing to use its internal compute, storage, and database infrastructure and related expertise to its advantage.<ref name="MillerHowAWS16" />
This wide variety of roles further highlights the already obvious cross-disciplinary nature of analyzing animal feed ingredients and products, and interpreting the resulting data. The human [[Biology|biological]] sciences, [[Veterinary medicine|veterinary sciences]], [[environmental science]]s, [[chemistry]], [[microbiology]], [[radiochemistry]], [[botany]], [[epidemiology]], and more may be involved within a given animal feed analysis laboratory.<ref>{{Cite journal |last=Schnepf |first=Anne |last2=Hille |first2=Katja |last3=van Mark |first3=Gesine |last4=Winkelmann |first4=Tristan |last5=Remm |first5=Karen |last6=Kunze |first6=Katrin |last7=Velleuer |first7=Reinhard |last8=Kreienbrock |first8=Lothar |date=2024-02-06 |title=Basis for a One Health Approach—Inventory of Routine Data Collections on Zoonotic Diseases in Lower Saxony, Germany |url=https://www.mdpi.com/2813-0227/4/1/7 |journal=Zoonotic Diseases |language=en |volume=4 |issue=1 |pages=57–73 |doi=10.3390/zoonoticdis4010007 |issn=2813-0227}}</ref><ref name="PFPLSWHumanAnim18">{{cite web |url=https://www.aphl.org/programs/food_safety/APHL%20Documents/LBPM_Dec2018.pdf |format=PDF |title=Human and Animal Food Testing Laboratories Best Practices Manual |author=Partnership for Food Protection Laboratory Science Workgroup |date=December 2018 |accessdate=22 May 2024}}</ref><ref name=":0">{{Cite journal |last=Wood |first=Hannah |last2=O'Connor |first2=Annette |last3=Sargeant |first3=Jan |last4=Glanville |first4=Julie |date=2018-12 |title=Information retrieval for systematic reviews in food and feed topics: A narrative review |url=https://onlinelibrary.wiley.com/doi/10.1002/jrsm.1289 |journal=Research Synthesis Methods |language=en |volume=9 |issue=4 |pages=527–539 |doi=10.1002/jrsm.1289 |issn=1759-2879}}</ref> Given this significant cross-disciplinarity, it's arguably more challenging for software developers creating [[laboratory informatics]] solutions like a [[laboratory information management system]] (LIMS) that has the breadth to cover the production, R&D, and public health contexts of animal feed testing. In fact, an industry lab performing [[quality control]] (QC) work for a company will likely have zero interest in public health reporting functionality, and a LIMS that focuses on QC workflows may be more highly desirable.  


At that point, the paradigm of web services expanded to include infrastructure as a service or IaaS, with compute, storage, and database services running over the internet for web developers to utilize.<ref name="FurrierExclusive15" /><ref name="MillerHowAWS16" /> "If you believe developers will build applications from scratch using web services as primitive building blocks, then the operating system becomes the internet,” noted AWS CEO Andy Jassy in a 2015 retrospective interview.<ref name="FurrierExclusive15" /> From that concept evolved the idea of determining what it would take to allow any entity to run their technology applications over their web-service-based IaaS platform. In August 2006, Amazon introduced its Amazon Elastic Compute Cloud (Amazon EC2), "a web service that provides resizable compute capacity in the cloud."<ref name="AWSAnnounc06">{{cite web |url=https://aws.amazon.com/about-aws/whats-new/2006/08/24/announcing-amazon-elastic-compute-cloud-amazon-ec2---beta/ |title=Announcing Amazon Elastic Compute Cloud (Amazon EC2) - beta |publisher=Amazon Web Services |date=24 August 2006 |accessdate=28 July 2023}}</ref><ref name="ButlerAmazon06">{{cite journal |title=Amazon puts network power online |journal=Nature |author=Butler, D. |volume=444 |issue=528 |year=2006 |doi=10.1038/444528a}}</ref> This quickly prompted others in academic and scientific fields to continue the conversation of turning IT and its infrastructure into a service.<ref name="ButlerAmazon06" /><ref name="KeITeS06">{{cite journal |title=ITeS - Transcending the Traditional Service Model |journal=Proceedings of the 2006 IEEE International Conference on e-Business Engineering |author=Ke, J.-s. |page=2 |year=2006 |doi=10.1109/ICEBE.2006.66}}</ref> In turn, conversations changed, discussing the opportunities inherent to "cloud computing," including Google and IBM partnering to virtualize computers on new data centers for boosting academic research and teaching new computer science students<ref name="LohrGoogle07">{{cite web |url=http://www.nytimes.com/2007/10/08/technology/08cloud.html?_r=1&or |archiveurl=http://www.csun.edu/pubrels/clips/Oct07/10-08-07E.pdf |format=PDF |title=Google and I.B.M. Join in 'Cloud Computing' Research |author=Lohr, S. |work=The New York Times |date=08 October 2007 |archivedate=08 October 2007 |accessdate=28 July 2023}}</ref><ref name="HandHead07">{{cite journal |title=Head in the clouds |journal=Nature |author=Hand, E. |volume=449 |issue=963 |year=2007 |doi=10.1038/449963a}}</ref>, IBM releasing a white paper on cloud computing<ref name="BossCloud07">{{cite web |url=http://download.boulder.ibm.com/ibmdl/pub/software/dw/wes/hipods/Cloud_computing_wp_final_8Oct.pdf |archiveurl=https://web.archive.org/web/20090206015244/http://download.boulder.ibm.com/ibmdl/pub/software/dw/wes/hipods/Cloud_computing_wp_final_8Oct.pdf |format=PDF |title=Cloud Computing |author=Boss, G.; Malladi, P.; Quan, D. et al. |publisher=IBM Corporation |date=08 October 2007 |archivedate=06 February 2009 |accessdate=28 July 2023}}</ref> and announcing its Blue Cloud initiative<ref name="LohrIBM07">{{cite web |url=https://www.nytimes.com/2007/11/15/technology/15blue.html |title=I.B.M. to Push 'Cloud Computing,' Using Data From Afar |author=Lohr, S. |work=The New York Times |date=15 November 2007 |accessdate=28 July 2023}}</ref>, and Google doubling down on its cloud-based software offerings in competition with Microsoft.<ref name="LohrGoogleGets07">{{cite web |url=https://www.nytimes.com/2007/12/16/technology/16goog.html |archiveurl=https://signallake.com/innovation/GoogleMicrosoft121607.pdf |format=PDF |title=Google Gets Ready to Rumble With Microsoft |author=Lohr, S.; Helft, M. |work=The New York Times |date=16 December 2007 |archivedate=16 December 2007 |accessdate=28 July 2023}}</ref>
That said, this Q&A article will examine LIMS functionality that addresses the needs of all three contexts for animal feed analyses. Understand that the LIMS solution your feed lab may be looking for doesn't require some of the functionality addressed here, particularly in the specialty LIMS requirements section. But also understand the broader context of feed testing and how it highlights some of the challenges of finding a feed testing LIMS that is just right for your lab.


In IBM's 2007 white paper, they described cloud computing as a "pool of virtualized computer resources" that can<ref name="BossCloud07" />:
==Base LIMS requirements for animal feed testing==
Given the above ...


*  "host a variety of different workloads, including batch-style back-end jobs and interactive, user-facing applications";
What follows is a list of system functionality important to most any feed testing laboratory, with a majority of that functionality found in many vendor software solutions.<ref name="WardObtain24" /><ref name="PFPLSWHumanAnim18" />
*  "allow workloads to be deployed and scaled-out quickly through the rapid provisioning of virtual machines or physical machines";
*  "support redundant, self-recovering, highly scalable programming models that allow workloads to recover from many unavoidable hardware/software failures";
*  "monitor resource use in real time to enable rebalancing of allocations when needed"; and
*  "be a cost efficient model for delivering information services, reducing IT management complexity, promoting innovation, and increasing responsiveness through real-time workload balancing."


In 2011, the National Institute of Standards and Technology (NIST) came up with a more standards-based definition to cloud computing. They described it as "a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction."<ref name="MellTheNIST11">{{cite web |url=https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-145.pdf |format=PDF |title=The NIST Definition of Cloud Computing |author=Mell, P.; Grance, T. |publisher=NIST |date=September 2011 |accessdate=28 July 2023}}</ref> They went on to highlight the five essential characteristics further<ref name="MellTheNIST11" />:
'''Test, sample and result management'''


* On-demand self-service: The unilateral provision of computing resources should be an automatic or nearly automatic process.
*Sample log-in and management, with support for unique IDs
* Broad network access: Thin- or thick-client platforms, both hardwired and mobile, should allow for standardized, networkable access to those computing resources.
*Sample batching
* Resource pooling: A multi-tenant model requires the provisioning of resources to serve a wide customer base, with a layer of abstraction that gives the user a sense of location independence from those resources.
*[[Barcode]] and RFID support
* Rapid elasticity: The platform's resources should be readily and/or automatically scalable commensurate with demand, such that the user sees no negative impact in their activities.
*End-to-end sample and inventory tracking, through to reporting and disposition
* Measured service: The resources should be automatically controlled and optimized by a measured service or metering system, transparently providing accurate and timely information about resource usage.
*Pre-defined and configurable industry-specific test and method management, including for bacteria (i.e., microbiology), heavy metals (i.e., chemistry), radionuclides (i.e., radiochemistry), and other substances
*Pre-defined and configurable industry-specific workflows, including for production, R&D, and public health contexts
*Configurable screens and data fields
*Specification management
*Test, sampling, instrument, etc. scheduling and assignment
*Test requesting
*Data import and export
*Raw data management
*Robust query tools
*Analytical tools, including [[data visualization]], statistical analysis, and [[data mining]] tools
*Document and image management
*Version control
*Project and experiment management
*Method and protocol management
*Investigation management
*Facility and sampling site management
*Storage management and monitoring


When we compare these 2007 and 2011 definitions of cloud computing with the comments on web services by Glass and Kreger at the turn of the century (as well as our own derived definition prior), we can't help but see how the early vision for cloud computing has taken shape today. First, web services can indeed be paired with other technologies to form a distributed system, in this case a centralized and scalable computing infrastructure that can be used by practically anyone to run software, develop applications, and "host a variety of different workloads."<ref name="BossCloud07" /> Second, those workloads can be quickly deployed worldwide, wherever there is internet access, and typically at a fair price, when compared to the costs of on-premises data management.<ref name="ViolinoWhere20">{{cite web |url=https://www.infoworld.com/article/3532288/where-to-look-for-cost-savings-in-the-cloud.html |title=Where to look for cost savings in the cloud |author=Violino, B. |work=InfoWorld |date=16 March 2020 |accessdate=28 July 2023}}</ref> Third, new opportunities are indeed developing for organizations seeking to tap into the on-demand, rapid, scalable, and cost-efficient nature of cloud computing.<ref name="OjalaDiscover16">{{cite journal |title=Discovering and creating business opportunities for cloud services |journal=Journal of Systems and Software |author=Ojala, A. |volume=113 |pages=408–17 |year=2016 |doi=10.1016/j.jss.2015.11.004}}</ref><ref name="PetteyCloud20">{{cite web |url=https://www.gartner.com/smarterwithgartner/cloud-shift-impacts-all-it-markets/ |title=Cloud Shift Impacts All IT Markets |author=Pettey, C. |work=Smarter with Gartner |date=26 October 2020 |accessdate=28 July 2023}}</ref> And finally, benefits are being seen in the integration of applications via the cloud, particularly as more options for multicloud and hybrid cloud integration develop.<ref name="PetteyFiveApp19">{{cite web |url=https://www.gartner.com/smarterwithgartner/5-approaches-cloud-applications-integration/ |title=5 Approaches to Cloud Applications Integration |author=Pettey, C. |work=Smarter with Gartner |date=14 May 2019 |accessdate=28 July 2023}}</ref> The early vision that perhaps hasn't been realized is found in Glass' "shift from client-server to peer-to-peer architectures," though discussions about the promise of peer-to-peer cloud computing have occurred since.<ref name="BabaogluEscape14">{{cite journal |title=The People's Cloud |journal=IEEE Spectrum |author=Babaoglu, O.; Marzolla, M. |volume=51 |issue=10 |pages=50–55 |year=2014 |doi=10.1109/MSPEC.2014.6905491 |url=https://spectrum.ieee.org/escape-from-the-data-center-the-promise-of-peertopeer-cloud-computing}}</ref>
'''Quality, security, and compliance'''


Though clearly linked to web services and the early vision of cloud computing in the 2000s, the cloud computing of the 2020s is a remarkably more advanced and continually evolving technology. However, it's still not without its challenges today. The data security, privacy, and governance of computing in general, and cloud computing in particular, will continue to require more rigorous approaches, as will reducing remaining data silos in organizations with pivots to hybrid cloud, multicloud, and serverless cloud implementations.<ref name="Goodison10Fut20">{{cite web |url=https://www.crn.com/news/cloud/10-future-cloud-computing-trends-to-watch-in-2021 |title=10 Future Cloud Computing Trends To Watch In 2021 |author=Goodison, D. |work=CRN |date=20 November 2020 |accessdate=28 July 2023}}</ref><ref name="DTCCCloud20">{{cite web |url=http://www.dtcc.com/-/media/Files/Downloads/WhitePapers/DTCC-Cloud-Journey-WP |format=PDF |title=Cloud Technology: Powerful and Evolving |author=DTCC |date=November 2020 |accessdate=28 July 2023}}</ref> But what is "hybrid cloud"? "Serverless cloud?" The next section goes into further detail.
*[[Quality assurance]] / [[quality control]] mechanisms
*Mechanisms for compliance with ISO 17025 and HACCP, including support for critical control point (CCP) specifications and limits
*Result, method, protocol, batch, and material validation, review, and release
*Data validation
*Trend and control charting for statistical analysis and measurement of uncertainty
*User qualification, performance, and training management
*[[Audit trail]]s and [[chain of custody]] support
*Configurable and granular role-based security
*Configurable system access and use (i.e., authentication requirements, account usage rules, account locking, etc.)
*[[Electronic signature]] support
*Data [[encryption]] and secure communication protocols
*Archiving and [[Data retention|retention]] of data and information
*Configurable data [[backup]]s
*Status updates and alerts
*Environmental monitoring support
*Incident and non-conformance notification, tracking, and management
 
'''Operations management and reporting'''
 
*Configurable dashboards for monitoring, by product, process, facility, etc.
*Customizable rich-text reporting, with multiple supported output formats
*Custom and industry-specific reporting, including certificates of analysis (CoAs)
*Industry-compliant labeling
*Email integration and other communication support for internal and external stakeholders
*Instrument interfacing and data management, particularly for [[near-infrared spectroscopy]] (NIRS) instruments
*Third-party software interfacing (e.g., LES, scientific data management system [SDMS], other databases)
*Data import, export, and archiving
*Instrument and equipment management, including calibration and maintenance tracking
*Inventory and material management
*Supplier/vendor/customer management
*Flexible but secure client portal for pre-registering samples, printing labels, and viewing results
*Integrated (or online) system help
 
==Specialty LIMS requirements==
 
*'''Mechanisms to make data and information more FAIR''': Like many other disciplines, modern academic and industrial research of feed ingredient selection, feed formulation, and feed production is plagued by interdisciplinary research data and information (i.e., objects) "in a broad range of [heterogeneous] information formats [that] involve inconsistent vocabulary and difficult‐to‐define concepts."<ref name=":0" /> This makes increasingly attractive data discovery options<ref name=":0" /> such as text mining, cluster searching, and [[artificial intelligence]] (AI) methods less effective, in turn hampering innovation, discovery, and improved health outcomes. As such, research labs of all sorts are increasingly turning to the FAIR principles, which encourage processes that make research objects more findable, accessible, interoperable, and reusable. A handful of software developers have become more attuned to this demand and have developed or modified their systems to produce research objects that are produced using [[metadata]]- and [[Semantics|semantic-driven]] technologies and frameworks.<ref name="DouglasWhyAre24">{{cite web |url=https://www.limswiki.org/index.php/LIMS_Q%26A:Why_are_the_FAIR_data_principles_increasingly_important_to_research_laboratories_and_their_software%3F |title=LIMS Q&A:Why are the FAIR data principles increasingly important to research laboratories and their software? |author=Douglas, S.E. |work=LIMSwiki |date=May 2024 |accessdate=22 May 2024}}</ref> Producing FAIR data is more important to the academic research and public health contexts of feed testing, but can still be useful to other industrial contexts, as having interoperable and reusable data in industry can lead to greater innovation and process improvement.<ref>{{Cite journal |last=van Vlijmen |first=Herman |last2=Mons |first2=Albert |last3=Waalkens |first3=Arne |last4=Franke |first4=Wouter |last5=Baak |first5=Arie |last6=Ruiter |first6=Gerbrand |last7=Kirkpatrick |first7=Christine |last8=da Silva Santos |first8=Luiz Olavo Bonino |last9=Meerman |first9=Bert |last10=Jellema |first10=Renger |last11=Arts |first11=Derk |date=2020-01 |title=The Need of Industry to Go FAIR |url=https://direct.mit.edu/dint/article/2/1-2/276-284/10011 |journal=Data Intelligence |language=en |volume=2 |issue=1-2 |pages=276–284 |doi=10.1162/dint_a_00050 |issn=2641-435X}}</ref> Of course, all animal feed testing labs can benefit when, for example, FAIR-driven, internationally accepted vocabulary and data descriptors for mycotoxin contamination data are used in research and laboratory software.<ref>{{Cite journal |last=Mesfin |first=Addisalem |last2=Lachat |first2=Carl |last3=Vidal |first3=Arnau |last4=Croubels |first4=Siska |last5=Haesaert |first5=Geert |last6=Ndemera |first6=Melody |last7=Okoth |first7=Sheila |last8=Belachew |first8=Tefera |last9=Boevre |first9=Marthe De |last10=De Saeger |first10=Sarah |last11=Matumba |first11=Limbikani |date=2022-02 |title=Essential descriptors for mycotoxin contamination data in food and feed |url=https://linkinghub.elsevier.com/retrieve/pii/S0963996921007833 |journal=Food Research International |language=en |volume=152 |pages=110883 |doi=10.1016/j.foodres.2021.110883}}</ref> This leads into...
*'''Support for standardized and controlled vocabularies''': By extension, this gets into the matter of improved interoperability of feed testing results from different laboratories, particularly government labs in different jurisdictions responsible for monitoring contaminates in animal feed.<ref name="AAFCOSACStrat22">{{cite web |url=https://www.aafco.org/wp-content/uploads/2023/07/SAC_Strategic_Plan_2023-2025.pdf |format=PDF |title=Strategic Plan 2023-2025 |author=The Association of American Feed Control Officials, Strategic Affairs Committee |publisher=AAFCO |page=14 |date=16 November 2022 |accessdate=22 May 2024}}</ref> The Association of American Feed Control Officials (AAFCO) Strategic Affairs Committee (SAC) highlight this in their Strategic Plan for 2023–2025, stating that in order to "promote and integrate laboratory technology, methods, quality systems, and collaboration in support of animal food safety systems," the different LIMS used across various states demand an integrated IT environment where "comparable results from different labs" can effectively be made.<ref name="AAFCOSACStrat22" />
 
==Conclusion==
 
 
==References==
{{Reflist|colwidth=30em}}
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[[File:|right|400px]] Title: What are the key elements of a LIMS for animal feed testing?

Author for citation: Shawn E. Douglas

License for content: Creative Commons Attribution-ShareAlike 4.0 International

Publication date: May 2024

Introduction

This brief topical article will examine ...

Note: Any citation leading to a software vendor's site is not to be considered a recommendation for that vendor. The citation should however still stand as a representational example of what vendors are implementing in their systems.

Feed testing laboratory workflow, workload, and information management

A feed testing lab can operate within a number of different production, research and development (R&D; academic and industry), and public health contexts. They can[1]:

  • act as a third-party consultant, interpreting analytical data;
  • provide research and development support for new and revised formulations;
  • provide analytical support for nutrition and contaminant determinations;
  • provide development support for analytical methods;
  • ensure quality to specifications, accreditor standards, and regulations;
  • develop informative databases and data libraries for researchers;
  • manage in-house and remote sample collection, labeling, and registration, including on farms; and
  • report accurate and timely results to stakeholders, including those responsible for monitoring public health.

This wide variety of roles further highlights the already obvious cross-disciplinary nature of analyzing animal feed ingredients and products, and interpreting the resulting data. The human biological sciences, veterinary sciences, environmental sciences, chemistry, microbiology, radiochemistry, botany, epidemiology, and more may be involved within a given animal feed analysis laboratory.[2][3][4] Given this significant cross-disciplinarity, it's arguably more challenging for software developers creating laboratory informatics solutions like a laboratory information management system (LIMS) that has the breadth to cover the production, R&D, and public health contexts of animal feed testing. In fact, an industry lab performing quality control (QC) work for a company will likely have zero interest in public health reporting functionality, and a LIMS that focuses on QC workflows may be more highly desirable.

That said, this Q&A article will examine LIMS functionality that addresses the needs of all three contexts for animal feed analyses. Understand that the LIMS solution your feed lab may be looking for doesn't require some of the functionality addressed here, particularly in the specialty LIMS requirements section. But also understand the broader context of feed testing and how it highlights some of the challenges of finding a feed testing LIMS that is just right for your lab.

Base LIMS requirements for animal feed testing

Given the above ...

What follows is a list of system functionality important to most any feed testing laboratory, with a majority of that functionality found in many vendor software solutions.[1][3]

Test, sample and result management

  • Sample log-in and management, with support for unique IDs
  • Sample batching
  • Barcode and RFID support
  • End-to-end sample and inventory tracking, through to reporting and disposition
  • Pre-defined and configurable industry-specific test and method management, including for bacteria (i.e., microbiology), heavy metals (i.e., chemistry), radionuclides (i.e., radiochemistry), and other substances
  • Pre-defined and configurable industry-specific workflows, including for production, R&D, and public health contexts
  • Configurable screens and data fields
  • Specification management
  • Test, sampling, instrument, etc. scheduling and assignment
  • Test requesting
  • Data import and export
  • Raw data management
  • Robust query tools
  • Analytical tools, including data visualization, statistical analysis, and data mining tools
  • Document and image management
  • Version control
  • Project and experiment management
  • Method and protocol management
  • Investigation management
  • Facility and sampling site management
  • Storage management and monitoring

Quality, security, and compliance

  • Quality assurance / quality control mechanisms
  • Mechanisms for compliance with ISO 17025 and HACCP, including support for critical control point (CCP) specifications and limits
  • Result, method, protocol, batch, and material validation, review, and release
  • Data validation
  • Trend and control charting for statistical analysis and measurement of uncertainty
  • User qualification, performance, and training management
  • Audit trails and chain of custody support
  • Configurable and granular role-based security
  • Configurable system access and use (i.e., authentication requirements, account usage rules, account locking, etc.)
  • Electronic signature support
  • Data encryption and secure communication protocols
  • Archiving and retention of data and information
  • Configurable data backups
  • Status updates and alerts
  • Environmental monitoring support
  • Incident and non-conformance notification, tracking, and management

Operations management and reporting

  • Configurable dashboards for monitoring, by product, process, facility, etc.
  • Customizable rich-text reporting, with multiple supported output formats
  • Custom and industry-specific reporting, including certificates of analysis (CoAs)
  • Industry-compliant labeling
  • Email integration and other communication support for internal and external stakeholders
  • Instrument interfacing and data management, particularly for near-infrared spectroscopy (NIRS) instruments
  • Third-party software interfacing (e.g., LES, scientific data management system [SDMS], other databases)
  • Data import, export, and archiving
  • Instrument and equipment management, including calibration and maintenance tracking
  • Inventory and material management
  • Supplier/vendor/customer management
  • Flexible but secure client portal for pre-registering samples, printing labels, and viewing results
  • Integrated (or online) system help

Specialty LIMS requirements

  • Mechanisms to make data and information more FAIR: Like many other disciplines, modern academic and industrial research of feed ingredient selection, feed formulation, and feed production is plagued by interdisciplinary research data and information (i.e., objects) "in a broad range of [heterogeneous] information formats [that] involve inconsistent vocabulary and difficult‐to‐define concepts."[4] This makes increasingly attractive data discovery options[4] such as text mining, cluster searching, and artificial intelligence (AI) methods less effective, in turn hampering innovation, discovery, and improved health outcomes. As such, research labs of all sorts are increasingly turning to the FAIR principles, which encourage processes that make research objects more findable, accessible, interoperable, and reusable. A handful of software developers have become more attuned to this demand and have developed or modified their systems to produce research objects that are produced using metadata- and semantic-driven technologies and frameworks.[5] Producing FAIR data is more important to the academic research and public health contexts of feed testing, but can still be useful to other industrial contexts, as having interoperable and reusable data in industry can lead to greater innovation and process improvement.[6] Of course, all animal feed testing labs can benefit when, for example, FAIR-driven, internationally accepted vocabulary and data descriptors for mycotoxin contamination data are used in research and laboratory software.[7] This leads into...
  • Support for standardized and controlled vocabularies: By extension, this gets into the matter of improved interoperability of feed testing results from different laboratories, particularly government labs in different jurisdictions responsible for monitoring contaminates in animal feed.[8] The Association of American Feed Control Officials (AAFCO) Strategic Affairs Committee (SAC) highlight this in their Strategic Plan for 2023–2025, stating that in order to "promote and integrate laboratory technology, methods, quality systems, and collaboration in support of animal food safety systems," the different LIMS used across various states demand an integrated IT environment where "comparable results from different labs" can effectively be made.[8]

Conclusion

References

  1. ↑ 1.0 1.1 Ward, R. (27 February 2024). "Obtaining value from a feed/forage lab engagement" (PDF). Florida Ruminant Nutrition Symposium. https://animal.ifas.ufl.edu/media/animalifasufledu/dairy-website/ruminant-nutrition-symposium/archives/12.-WardRNS2024.pdf. Retrieved 22 May 2024. 
  2. ↑ Schnepf, Anne; Hille, Katja; van Mark, Gesine; Winkelmann, Tristan; Remm, Karen; Kunze, Katrin; Velleuer, Reinhard; Kreienbrock, Lothar (6 February 2024). "Basis for a One Health Approach—Inventory of Routine Data Collections on Zoonotic Diseases in Lower Saxony, Germany" (in en). Zoonotic Diseases 4 (1): 57–73. doi:10.3390/zoonoticdis4010007. ISSN 2813-0227. https://www.mdpi.com/2813-0227/4/1/7. 
  3. ↑ 3.0 3.1 Partnership for Food Protection Laboratory Science Workgroup (December 2018). "Human and Animal Food Testing Laboratories Best Practices Manual" (PDF). https://www.aphl.org/programs/food_safety/APHL%20Documents/LBPM_Dec2018.pdf. Retrieved 22 May 2024. 
  4. ↑ 4.0 4.1 4.2 Wood, Hannah; O'Connor, Annette; Sargeant, Jan; Glanville, Julie (1 December 2018). "Information retrieval for systematic reviews in food and feed topics: A narrative review" (in en). Research Synthesis Methods 9 (4): 527–539. doi:10.1002/jrsm.1289. ISSN 1759-2879. https://onlinelibrary.wiley.com/doi/10.1002/jrsm.1289. 
  5. ↑ Douglas, S.E. (May 2024). "LIMS Q&A:Why are the FAIR data principles increasingly important to research laboratories and their software?". LIMSwiki. https://www.limswiki.org/index.php/LIMS_Q%26A:Why_are_the_FAIR_data_principles_increasingly_important_to_research_laboratories_and_their_software%3F. Retrieved 22 May 2024. 
  6. ↑ van Vlijmen, Herman; Mons, Albert; Waalkens, Arne; Franke, Wouter; Baak, Arie; Ruiter, Gerbrand; Kirkpatrick, Christine; da Silva Santos, Luiz Olavo Bonino et al. (1 January 2020). "The Need of Industry to Go FAIR" (in en). Data Intelligence 2 (1-2): 276–284. doi:10.1162/dint_a_00050. ISSN 2641-435X. https://direct.mit.edu/dint/article/2/1-2/276-284/10011. 
  7. ↑ Mesfin, Addisalem; Lachat, Carl; Vidal, Arnau; Croubels, Siska; Haesaert, Geert; Ndemera, Melody; Okoth, Sheila; Belachew, Tefera et al. (1 February 2022). "Essential descriptors for mycotoxin contamination data in food and feed" (in en). Food Research International 152: 110883. doi:10.1016/j.foodres.2021.110883. https://linkinghub.elsevier.com/retrieve/pii/S0963996921007833. 
  8. ↑ 8.0 8.1 The Association of American Feed Control Officials, Strategic Affairs Committee (16 November 2022). "Strategic Plan 2023-2025" (PDF). AAFCO. p. 14. https://www.aafco.org/wp-content/uploads/2023/07/SAC_Strategic_Plan_2023-2025.pdf. Retrieved 22 May 2024. 
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