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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:

• 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. 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.

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." This makes increasingly attractive data discovery options 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. 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.