Difference between revisions of "User:Shawndouglas/sandbox/sublevel5"

From LIMSWiki
Jump to navigationJump to search
 
(18 intermediate revisions by the same user not shown)
Line 7: Line 7:


==Sandbox begins below==
==Sandbox begins below==
[[File:Lab-notebook-spreadsheet-simulation.jpg|right|350px]]
{{raw:wikipedia::Detection limit}}
'''Title''': ''What are the alternatives to 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==
 
==Examples of LIMS alternatives==
A [[laboratory information management system]] (LIMS) is a modern solution to the increasingly demanding [[workflow]] needs of most [[Laboratory|laboratories]], particularly those performing activities in regulated industries. However, laboratory work wasn't always conducted with the help of such a software system, and a LIMS isn't always the answer for a lab looking to better manage its workflow and operations. Whether it's the simplicity of the lab's operations or the perceived costs of acquiring, maintaining, and updating a LIMS (or even heavy stakeholder resistance to updating old familiar processes to more modern ones), other alternatives still exist for laboratories, including paper-based systems, spreadsheet software, [[database]] software, or [[enterprise resource planning]] (ERP) software. This section will briefly discuss those options, while the following section will address their potential deficiencies.
 
===Paper-based systems===
Before software-based systems, labs used [[laboratory notebook]]s, notepads, Xeroxed report templates, and instrument printouts (granted, a step up from recording from pure observation) to document analytical and research processes. Even in 2023, we still find labs that stick to these older methods, and in some cases, this may still work. These methods are viewed as being low-cost, easily transportable, easy to copy (i.e., back up), relatively flexible to use (e.g., write, draw, chart, etc.), and easy to sign and date.<ref name="LiscouskiASci23" /> However, paper notes aren't always easy to read, can be readily damaged or destroyed, can be difficult to search, require more manual time-consuming methods, and can be difficult to integrate with other paper-based data and information.<ref name="LiscouskiASci23" />
 
===Spreadsheets===
As computing technology evolved and became more affordable, software makers had even more incentive to develop relevant and approachable software solutions to solve businesses' workflow challenges. Among these software solutions was the spreadsheet. Derived in concept from the paper-based ledgers accountants and traders would use, the electronic spreadsheet suddenly allowed businesses to perform calculations automatically, saving users time.<ref name="MeikleTheHist">{{cite web |url=https://blog.sheetgo.com/spreadsheets-tips/history-of-spreadsheets/ |title=The history of spreadsheets |author=Meikle, H |work=Sheetgo Blog |accessdate=15 December 2023}}</ref> Laboratories picked up on this electronic, ledger-based approach to documenting experimental results and making routine analytical calculations. However, as labs of all types have fallen under greater scrutiny from regulators, the electronic spreadsheet method of documentation and calculation of analytical results shows inefficiencies and inadequacies, including difficulty in preventing changes to fields and maintaining an accurate representation of the who, what, when, and where of recorded values.
 
===Databases===
Databases also came into popularity with the advent of computing technology. Tabular and relational representation of data points, with the ability to assign labels to those data points, became useful for the electronic storage and retrieval of all sorts of data.<ref name="FortuneABrief20">{{cite web |url=https://learn.saylor.org/mod/page/view.php?id=21059 |title=A Brief History of Databases |work=CS403: Introduction to Modern Database Systems |author=Fortune, S. |publisher=Saylor Academy |date=17 December 2020 |accessdate=15 December 2023}}</ref> Like the spreadsheet, it's not surprising that some laboratories latched on to the idea of keeping track of experimental, analytical, and [[quality control]] data in a database. However, these are best used for structured data, and as electronic types of data and information have evolved into more sophisticated forms such as images, audio, and other unstructured formats, the traditional database has shown its weaknesses.
 
===ERPs===
Compared to spreadsheets and databases, the ERP is a bit more modern software solution, specifically designed to help streamline and unify business processes across an entire organization. These systems were originally designed for larger enterprises such as banks, manufacturers, and insurance companies, helping them manage finances, assets, inventory and supply chain, training, and other business aspects. However, even today most don't have the functionality necessary to handle more laboratory-specific activities, stretching the ERP's functionality to the limit. (There are examples of savvy software vendors who've built laboratory-specific modules for existing ERPs like [[Open-source software|open-source]] [[Odoo]]<ref name="LSOdooLIMS">{{cite web |url=https://www.logicasoft.eu/en_US/lims |title=Odoo LIMS |publisher=LogicaSoft SPRL |accessdate=15 December 2023}}</ref>, but these appear to be few and far between.)
 
==Deficiencies in most LIMS alternatives==
To be sure, a LIMS is an investment for any sized laboratory, whether it's almost exclusively monetary (with some other organization doing a majority of the heavy lifting, as with a [[Cloud computing|cloud-based]] solution) or some combination of monetary and in-house resource expenditure (as with a self-hosted solution located on-premises, whether that solution is a commercial proprietary offering or an open-source offering). Even an open-source LIMS still requires the lab to lean on an employee or third-party consultant to set up, configure, and maintain the software (or even modify the source code), as well as maintain the local IT infrastructure to support it. The open-source route may make sense for small, single labs with a couple of instruments, but the lack of regulatory-driven functionality like an [[audit trail]] in all but a few open-source LIMS (e.g., [[SENAITE]]<ref name="SENAITEFeats">{{cite web |url=https://www.senaite.com/features |title=SENAITE - Features |publisher=SENAITE Foundation |accessdate=13 December 2023}}</ref>) may significantly restrict the available options to such labs.
 
This brings up the point of what a lab typically sacrifices with LIMS alternatives such as paper notebooks, spreadsheets, databases, and ERPs. These alternatives rarely address regulatory and/or internal need for<ref name="LiscouskiASci23">{{cite journal |url=https://www.limswiki.org/index.php/LII:A_Science_Student%27s_Guide_to_Laboratory_Informatics |title=LII:A Science Student's Guide to Laboratory Informatics |author=Liscouski, J. |editor=Douglas, S.E |journal=LIMSwiki.org |date=November 2023 |accessdate=13 December 2023}}</ref><ref name="LiscouskiImprov22">{{cite web |url=https://www.lablynx.com/wp-content/uploads/2023/03/Improving-Lab-Systems-From-Paper-to-Spreadsheets-to-LIMS.pdf |format=PDF |title=Improving Lab Systems: From Paper to Spreadsheets to LIMS |author=Liscouski, J. |publisher=LabLynx, Inc |date=April 2022 |accessdate=13 December 2023}}</ref><ref>{{Cite book |last=Ferrero, M.S. |date=2007 |editor-last=Weinberg |editor-first=Sandy |title=Good laboratory practice regulations |url=https://books.google.com/books?id=JR5i0Nz5UWEC&pg=PA233 |chapter=Chapter 8: GLP Documentation |series=Drugs and the pharmaceutical sciences |edition=4th ed |publisher=Informa Healthcare |place=New York |pages=223–240 |isbn=9780849375842}}</ref><ref>{{Cite journal |last=AlTarawneh |first=Ghada |last2=Thorne |first2=Simon |date=2017 |title=A Pilot Study Exploring Spreadsheet Risk in Scientific Research |url=https://arxiv.org/abs/1703.09785 |journal=arXiv |doi=10.48550/ARXIV.1703.09785}}</ref><ref name="McDowallAreSpr20">{{cite journal |url=https://www.chromatographyonline.com/view/are-spreadsheets-a-fast-track-to-regulatory-non-compliance |title=Are Spreadsheets a Fast Track to Regulatory Non-Compliance? |author=McDowall, R.D. |journal=LCGC Europe |volume=33 |issue=9 |pages=468–76 |year=2020 |accessdate=13 December 2023}}</ref><ref>{{Cite journal |last=Ma |first=Ming-Wei |last2=Gao |first2=Xian-Shu |last3=Zhang |first3=Ze-Yu |last4=Shang |first4=Shi-Yu |last5=Jin |first5=Ling |last6=Liu |first6=Pei-Lin |last7=Lv |first7=Feng |last8=Ni |first8=Wei |last9=Han |first9=Yu-Chen |last10=Zong |first10=Hui |date=2023-11-06 |title=Extracting laboratory test information from paper-based reports |url=https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-023-02346-6 |journal=BMC Medical Informatics and Decision Making |language=en |volume=23 |issue=1 |pages=251 |doi=10.1186/s12911-023-02346-6 |issn=1472-6947 |pmc=PMC10629084 |pmid=37932733}}</ref><ref>{{Citation |last=McDowall. R.D. |date= |year=2018 |title=Chapter 13. Get Rid of Paper: Why Electronic Processes are Better for Data Integrity |url=http://ebook.rsc.org/?DOI=10.1039/9781788013277-00281 |work=Data Integrity and Data Governance |language=en |publisher=Royal Society of Chemistry |place=Cambridge |pages=281–304 |doi=10.1039/9781788013277-00281 |isbn=978-1-78801-281-2 |accessdate=}}</ref>:
 
*ensuring analytical results haven't been maliciously or accidentally modified (such as with audit trails that clearly and properly maintain the [[metadata]] surrounding an inputted value, as well as any changes made to it);
*clearly and accurately documenting a wide variety of data and metadata about a given sample or analysis;
*ensuring software tools like spreadsheets are validated;
*ensuring data is contemporaneous (i.e., "current") and not fabricated post-analysis;
*maintaining data integrity beyond what audit trails provide;
*maintaining, archiving, and disposing of data and information for a designated period of time, whether it's paper or electronic;
*ensuring recorded values are treated uniformly for all lab operations, using the same units, rounding rules, formulas, limits, etc.;
*maintaining the security of proprietary lab data and information, including methods, analytical values, and associated reports;
*ensuring accurate and timely analytical results that have been officially validated/approved by one or more individuals (with that validation/approval getting properly documented);
*ensuring timely retrieval of data and information to more readily support decision-making and audits;
*allowing more than one user to access, add, and modify lab data and information;
*supporting more timely recording of analytical values from instruments; and
*supporting later conversion of paper-based data and information into structured, readable, and importable electronic formats.
 
This is not to say that paper-based laboratory notebooks, spreadsheets, databases, and ERPs can't work for small, lightly-regulated laboratories. However, as more laboratory activities across all industries gain more regulatory oversight, and as clientele of said labs increasingly demand more timely, accurate, and defensible analytical results, today's laboratories are under pressure to move beyond little print-out slips from instruments, paper notebooks, controlled worksheets, and non-validated software tools that aren't purpose-built for labs. That some LIMS vendors have recognized that not every lab needs a megalithic software solution—in turn offering slimmed-down, more affordable LIMS solutions—is even more encouraging for small labs that want to take the next step towards improved workflows and greater data integrity.
 
==Conclusion==
 
 
==References==
{{Reflist|colwidth=30em}}

Latest revision as of 18:25, 10 January 2024

Sandbox begins below

Template:Short description

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

Template:BranchesofChemistry Template:Authority control