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{{raw:wikipedia::Detection limit}}
'''Title''': ''What are the organizational justifications for a laboratory information management system (LIMS)?''
 
'''Author for citation''': Shawn E. Douglas
 
'''License for content''': [https://creativecommons.org/licenses/by-sa/4.0/ Creative Commons Attribution-ShareAlike 4.0 International]
 
'''Publication date''': December 2023
 
==Introduction==
As a lab manager or stakeholder in your organization, you've concluded that a [[laboratory information management system]] (LIMS) makes the most sense for better managing [[workflow]]s and data management practices. However, you may not be the primary decision maker for LIMS acquisition and deployment within your organization, which means you may have to present your case (i.e., provide justification) for the LIMS to those primary decision makers. This justification should first be based on factors that are closest to the lab's essential laboratory functions, and then on more traditional economic and practical considerations, justifications, and benefits to the lab, as well as the overall organization.
 
This brief topical article will examine organizational, economic, and practical justifications for LIMS acquisition, allowing you to better build a stronger case for LIMS acquisition.
 
==LIMS justification focused on your organization==
When discussing the justification of LIMS acquisition for an organization, it's easy to broadly speak about the typical challenges, requirements, and considerations for labs of all types. While this basic approach provides important deductions about LIMS for the laboratory industry as a whole, no two laboratories are alike, and the challenges, requirements, and considerations for your laboratory may very well differ from the typical laboratory's. If your organization has already clearly stated its goals and potential risks, then rest assured that it has a head start on any organization-based justification for a LIMS; many of the justifications for a LIMS can be tied to meeting those organizational goals and minimizing those potential risks.
 
Four questions can be asked when focusing on the organizational justification of a LIMS<ref name="LiscouskiJustif23">{{cite book |url=https://www.limswiki.org/index.php/LII:Justifying_LIMS_Acquisition_and_Deployment_within_Your_Organization |title=Justifying LIMS Acquisition and Deployment within Your Organization |chapter=2. Organizational, economic, and practical justifications for a LIMS |author=Liscouski, J.; Douglas, S.E |publisher=LIMSwiki.org |date=July 2023 |accessdate=16 December 2023}}</ref>:
 
* Why is acquiring a LIMS important to meeting the goals of your lab?
* What problems does the LIMS solve that currently affect your lab?
* What operational, financial, and personnel improvements do you expect to see in your lab because of LIMS implementation?
* Why are those answers important to the larger organization, as well as those outside the lab?
 
Before answering these questions, it may be necessary to familiarize yourself with a LIMS and what it's capable of doing to support laboratory operations. One could, for example, examine a document like [[LII:LIMSpec 2022 R2|LIMSpec]]—a specification document for [[laboratory informatics]] systems—to gain a better understanding of those capabilities. Once more informed, it will be easier to answer the four questions as they relate to your organization.
 
Regarding the first question, if your organization has already described its goals, you may start matching those LIMS capabilities to workflow and method improvement, as well as time savings, and then in turn link that to better achieving those goals. This becomes organizational justification for the LIMS. If you can further make that goal-guided justification relevant and current to what's presently happening in the lab, then it’s all the better; "the goals that are timely and pressing are those that earn priority."<ref name="CoteHowToSet20">{{cite web |url=https://online.hbs.edu/blog/post/strategic-planning-goals |title=How to Set Strategic Planning Goals |author=Cote, C. |work=Business Insights Blog |publisher=Harvard Business School Online |date=29 October 2020 |accessdate=16 December 2023}}</ref> Then that goal-driven priority can be emphasized during the formal justification process.
 
As for the second question on problems the LIMS may solve, those problems may be identified risks to the longevity of the organization, or they may be specific to a particular challenge posed by an existing laboratory process. Ideally, those risks and challenges have already been identified through a strategic planning process that successfully captures currently observed and potential future risks to the business and how it achieves its mission-critical goals and priorities.<ref name="GartnerStrat23">{{cite web |url=https://emtemp.gcom.cloud/ngw/globalassets/en/insights/strategic-planning/2023/documents/strategic-planning-ebook-2023-risk.pdf |format=PDF |title=Strategic Planning Essentials |publisher=Gartner, Inc |date=2023 |accessdate=19 December 2023}}</ref> Drawing upon these real and potential risks and process challenges helps you better justify how a LIMS can mitigate or prevent them.
 
Finally, addressing questions three and four involves analyzing the economic and practical benefits to the lab (and the overall organization; see the next section) and—along with your answers to questions one and two—laying out the value judgment of the LIMS to not only the organization but also its internal and external stakeholders. That final question in particular recognizes that the same value judgment and "superior worth" of a LIMS applied internally also must be applied to the data and information recipients, i.e., the external stakeholders. It's easy to ask what you, the laboratorian, gain by shifting from paper-based methods to electronic methods, but it's also worth asking how clientele benefits from that transition. Similar to how lab personnel may get surveyed as part of the LIMS acquisition process, one can imagine how conducting interviews with critical external stakeholders should reveal strong evidence that the long-term future of the lab and its customers will surely benefit from a LIMS.<ref name="LiscouskiJustif23" />
 
==Panning out to broader economic and practical justifications for LIMS adoption==
Lab management often thinks in terms of cost, and the further away management is in the organizational chart from lab operations, the more financial issues become a driving factor in understanding the impact of a LIMS. As such, it's inevitable that when justifying LIMS adoption you'll have to look at it (and address it) in economic terms. In particular, it's easy for primary decision makers to fall into the trap of looking at such an acquisition in purely economic terms (i.e., as return on investment).<ref name="LiscouskiJustifChp3_23">{{cite book |url=https://www.limswiki.org/index.php/LII:Justifying_LIMS_Acquisition_and_Deployment_within_Your_Organization |title=Justifying LIMS Acquisition and Deployment within Your Organization |chapter=3. Gaining buy-in from management and other stakeholders |author=Liscouski, J.; Douglas, S.E |publisher=LIMSwiki.org |date=July 2023 |accessdate=16 December 2023}}</ref> While addressing the costs and cost savings is important, the justification needs to be viewed from more than this singular perspective. One might look at the cost components of LIMS acquisition, implementation, and maintenance (including project management, networking, hardware, training, licensing, configuration, and support<ref name="LiscouskiJustif23" />) and break it down between in-house solutions and cloud-based solutions, coming to the conclusion that a cloud-based solution reduces many of those costs, making LIMS acquisition more economically feasible. Additional offsets like investment tax credits and accelerated depreciation rates may make those costs even more palatable.<ref name="LiscouskiJustif23" /> But this only part of the justification process.
 
A LIMS can also be argued as a "survival system," without which the lab could not effectively meet its goals. Bringing a LIMS into the mix isn't simply an incremental addition to a lab but rather the basis for better reorganizing and optimizing the lab's [[workflow]] to meet its goals, support its clients, and prepare it for its future development. In short, with a LIMS, the lab will be moving from completely or near-manual operations to a system that allows for an improved working environment with reduced administrative overhead.<ref name="LiscouskiJustifChp3_23" />
 
Then there's the process of looking at cost savings in a practical, quantifiable way. For example, a lab could attempt to quantify the costs of operating paper-based methods, addressing the number of data entry errors and time spent on them, as well as the time spent entering orders, filing paperwork, waiting for resources, and performing quality checks on manually entered results. From there, they could estimate the time savings a LIMS and its automation tools bring in terms of full-time equivalent (FTE) hours. Tangible, relatively quantifiable functional aspects one could examine for LIMS justification include<ref name="LiscouskiJustif23" />:
 
* Analytical support functions such as data entry through automated instrument interfacing;
* Work and resource management functions such as approving reports and managing reagent inventories;
* Quality assurance functions such as result validation, limit checking, instrument calibration, and maintenance management;
* Management support functions such as turn-around time analysis and equipment utilization analysis; and
* Business support functions such as customer billing and compliance reporting.
 
Of course, not all justification is tangible and quantifiable; a LIMS also brings intangible, difficult-to-quantify benefits to the laboratory.<ref name="ODriscollSeven23">{{cite web |url=https://clpmag.com/lab-essentials/information-technology/middleware-software/7-best-practices-for-a-successful-lims-lis-implementation/ |title=7 Best Practices for a Successful LIMS/LIS Implementation |work=Clinical Lab Products |author=O'Driscoll, A. |date=16 February 2023 |accessdate=19 December 2023}}</ref><ref name="NovakTheBen20">{{cite web |url=https://www.csolsinc.com/blog/the-benefits-of-implementing-a-lims-beyond-roi/ |title=The Benefits of Implementing a LIMS – Beyond ROI |author=Novak, C. |publisher=CSols, Inc |date=27 February 2020 |accessdate=19 December 2023}}</ref> However, what one lab views as an intangible benefit may be viewed as tangible by another, and vice versa. Regardless, it's important to note that any inability to quantify a benefit does not make it any less important to an organization acquiring and deploying a LIMS. Practical yet intangible benefits that can be used as justification may include<ref name="LiscouskiJustif23" />:
 
* Improved support for manufacturing industries by providing a more rapid means of identifying production/quality issues;
* Improved perception of lab capabilities due to improved performance through better client relations and improved attitudes about the work environment by personnel;
* Centralized and streamlined lab information, data, and operations by enabling easier evaluation of workloads and smoother regulatory compliance activities; and
* Improved data management/governance through enabling greater data integrity and reducing transcription errors.
 
==Conclusion==
 
 
==References==
{{Reflist|colwidth=30em}}

Latest revision as of 18:25, 10 January 2024

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Template:Short description

"LOQ" redirects here. For the company listed as LOQ on the London Stock Exchange, see Lo-Q. For the airport in Botswana with IATA code LOQ, see Lobatse Airport.

The limit of detection (LOD or LoD) is the lowest signal, or the lowest corresponding quantity to be determined (or extracted) from the signal, that can be observed with a sufficient degree of confidence or statistical significance. However, the exact threshold (level of decision) used to decide when a signal significantly emerges above the continuously fluctuating background noise remains arbitrary and is a matter of policy and often of debate among scientists, statisticians and regulators depending on the stakes in different fields.

Significance in analytical chemistry

In analytical chemistry, the detection limit, lower limit of detection, also termed LOD for limit of detection or analytical sensitivity (not to be confused with statistical sensitivity), is the lowest quantity of a substance that can be distinguished from the absence of that substance (a blank value) with a stated confidence level (generally 99%).[1][2][3] The detection limit is estimated from the mean of the blank, the standard deviation of the blank, the slope (analytical sensitivity) of the calibration plot and a defined confidence factor (e.g. 3.2 being the most accepted value for this arbitrary value).[4] Another consideration that affects the detection limit is the adequacy and the accuracy of the model used to predict concentration from the raw analytical signal.[5]

As a typical example, from a calibration plot following a linear equation taken here as the simplest possible model:

where, corresponds to the signal measured (e.g. voltage, luminescence, energy, etc.), "Template:Mvar" the value in which the straight line cuts the ordinates axis, "Template:Mvar" the sensitivity of the system (i.e., the slope of the line, or the function relating the measured signal to the quantity to be determined) and "Template:Mvar" the value of the quantity (e.g. temperature, concentration, pH, etc.) to be determined from the signal ,[6] the LOD for "Template:Mvar" is calculated as the "Template:Mvar" value in which equals to the average value of blanks "Template:Mvar" plus "Template:Mvar" times its standard deviation "Template:Mvar" (or, if zero, the standard deviation corresponding to the lowest value measured) where "Template:Mvar" is the chosen confidence value (e.g. for a confidence of 95% it can be considered Template:Mvar = 3.2, determined from the limit of blank).[4]

Thus, in this didactic example:

There are a number of concepts derived from the detection limit that are commonly used. These include the instrument detection limit (IDL), the method detection limit (MDL), the practical quantitation limit (PQL), and the limit of quantitation (LOQ). Even when the same terminology is used, there can be differences in the LOD according to nuances of what definition is used and what type of noise contributes to the measurement and calibration.[7]

The figure below illustrates the relationship between the blank, the limit of detection (LOD), and the limit of quantitation (LOQ) by showing the probability density function for normally distributed measurements at the blank, at the LOD defined as 3 × standard deviation of the blank, and at the LOQ defined as 10 × standard deviation of the blank. (The identical spread along Abscissa of these two functions is problematic.) For a signal at the LOD, the alpha error (probability of false positive) is small (1%). However, the beta error (probability of a false negative) is 50% for a sample that has a concentration at the LOD (red line). This means a sample could contain an impurity at the LOD, but there is a 50% chance that a measurement would give a result less than the LOD. At the LOQ (blue line), there is minimal chance of a false negative.

Template:Wide image

Instrument detection limit

Most analytical instruments produce a signal even when a blank (matrix without analyte) is analyzed. This signal is referred to as the noise level. The instrument detection limit (IDL) is the analyte concentration that is required to produce a signal greater than three times the standard deviation of the noise level. This may be practically measured by analyzing 8 or more standards at the estimated IDL then calculating the standard deviation from the measured concentrations of those standards.

The detection limit (according to IUPAC) is the smallest concentration, or the smallest absolute amount, of analyte that has a signal statistically significantly larger than the signal arising from the repeated measurements of a reagent blank.

Mathematically, the analyte's signal at the detection limit () is given by:

where, is the mean value of the signal for a reagent blank measured multiple times, and is the known standard deviation for the reagent blank's signal.

Other approaches for defining the detection limit have also been developed. In atomic absorption spectrometry usually the detection limit is determined for a certain element by analyzing a diluted solution of this element and recording the corresponding absorbance at a given wavelength. The measurement is repeated 10 times. The 3σ of the recorded absorbance signal can be considered as the detection limit for the specific element under the experimental conditions: selected wavelength, type of flame or graphite oven, chemical matrix, presence of interfering substances, instrument... .

Method detection limit

Often there is more to the analytical method than just performing a reaction or submitting the analyte to direct analysis. Many analytical methods developed in the laboratory, especially these involving the use of a delicate scientific instrument, require a sample preparation, or a pretreatment of the samples prior to being analysed. For example, it might be necessary to heat a sample that is to be analyzed for a particular metal with the addition of acid first (digestion process). The sample may also be diluted or concentrated prior to analysis by means of a given instrument. Additional steps in an analysis method add additional opportunities for errors. Since detection limits are defined in terms of errors, this will naturally increase the measured detection limit. This "global" detection limit (including all the steps of the analysis method) is called the method detection limit (MDL). The practical way for determining the MDL is to analyze seven samples of concentration near the expected limit of detection. The standard deviation is then determined. The one-sided Student's t-distribution is determined and multiplied versus the determined standard deviation. For seven samples (with six degrees of freedom) the t value for a 99% confidence level is 3.14. Rather than performing the complete analysis of seven identical samples, if the Instrument Detection Limit is known, the MDL may be estimated by multiplying the Instrument Detection Limit, or Lower Level of Detection, by the dilution prior to analyzing the sample solution with the instrument. This estimation, however, ignores any uncertainty that arises from performing the sample preparation and will therefore probably underestimate the true MDL.

Limit of each model

The issue of limit of detection, or limit of quantification, is encountered in all scientific disciplines. This explains the variety of definitions and the diversity of juridiction specific solutions developed to address preferences. In the simplest cases as in nuclear and chemical measurements, definitions and approaches have probably received the clearer and the simplest solutions. In biochemical tests and in biological experiments depending on many more intricate factors, the situation involving false positive and false negative responses is more delicate to handle. In many other disciplines such as geochemistry, seismology, astronomy, dendrochronology, climatology, life sciences in general, and in many other fields impossible to enumerate extensively, the problem is wider and deals with signal extraction out of a background of noise. It involves complex statistical analysis procedures and therefore it also depends on the models used,[5] the hypotheses and the simplifications or approximations to be made to handle and manage uncertainties. When the data resolution is poor and different signals overlap, different deconvolution procedures are applied to extract parameters. The use of different phenomenological, mathematical and statistical models may also complicate the exact mathematical definition of limit of detection and how it is calculated. This explains why it is not easy to come to a general consensus, if any, about the precise mathematical definition of the expression of limit of detection. However, one thing is clear: it always requires a sufficient number of data (or accumulated data) and a rigorous statistical analysis to render better signification statistically.

Limit of quantification

The limit of quantification (LoQ, or LOQ) is the lowest value of a signal (or concentration, activity, response...) that can be quantified with acceptable precision and accuracy.

The LoQ is the limit at which the difference between two distinct signals / values can be discerned with a reasonable certainty, i.e., when the signal is statistically different from the background. The LoQ may be drastically different between laboratories, so another detection limit is commonly used that is referred to as the Practical Quantification Limit (PQL).

See also

References

  1. ↑ IUPAC, Compendium of Chemical Terminology, 2nd ed. (the "Gold Book") (1997). Online corrected version:  (2006–) "detection limit".
  2. ↑ "Guidelines for Data Acquisition and Data Quality Evaluation in Environmental Chemistry". Analytical Chemistry 52 (14): 2242–49. 1980. doi:10.1021/ac50064a004. 
  3. ↑ Saah AJ, Hoover DR (1998). "[Sensitivity and specificity revisited: significance of the terms in analytic and diagnostic language."]. Ann Dermatol Venereol 125 (4): 291–4. PMID 9747274. https://pubmed.ncbi.nlm.nih.gov/9747274. 
  4. ↑ 4.0 4.1 "Limit of blank, limit of detection and limit of quantitation". The Clinical Biochemist. Reviews 29 Suppl 1 (1): S49–S52. August 2008. PMC 2556583. PMID 18852857. https://www.ncbi.nlm.nih.gov/pmc/articles/2556583. 
  5. ↑ 5.0 5.1 "R: "Detection" limit for each model" (in English). search.r-project.org. https://search.r-project.org/CRAN/refmans/bioOED/html/calculate_limit.html. 
  6. ↑ "Signal enhancement on gold nanoparticle-based lateral flow tests using cellulose nanofibers". Biosensors & Bioelectronics 141: 111407. September 2019. doi:10.1016/j.bios.2019.111407. PMID 31207571. http://ddd.uab.cat/record/218082. 
  7. ↑ Long, Gary L.; Winefordner, J. D., "Limit of detection: a closer look at the IUPAC definition", Anal. Chem. 55 (7): 712A–724A, doi:10.1021/ac00258a724 

Further reading

  • "Limits for qualitative detection and quantitative determination. Application to radiochemistry". Analytical Chemistry 40 (3): 586–593. 1968. doi:10.1021/ac60259a007. ISSN 0003-2700. 

External links

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