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=What are the key elements of a LIMS for food and beverage testing?=
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'''Title''': ''What are the key elements of a LIMS for food and beverage testing?''
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'''Author for citation''': Shawn E. Douglas
==Sandbox begins below==
 
{{raw:wikipedia::Detection limit}}
'''License for content''': [https://creativecommons.org/licenses/by-sa/4.0/ Creative Commons Attribution-ShareAlike 4.0 International]
 
'''Publication date''': September 2022
 
==Introduction==
A food and beverage [[laboratory]] may analyze anything from ingredients and additives to finalized food and beverage products, and many things in between. The types of analyses associated with these and other substrates and matrices can be equally diverse, depending on the role the food and beverage laboratory is playing in the overall larger framework of the industry. As has been noted in other work, the lab work of the research and development (R&D) role, for example, may look different than that of the food and beverage lab conducting activities in the pre-manufacturing/manufacturing role and the post-production regulation and security role.<ref name="DouglasWhatTypes22">{{cite web |url=https://www.limswiki.org/index.php/LIMS_FAQ:What_types_of_testing_occur_within_a_food_and_beverage_laboratory? |title=What types of testing occur within a food and beverage laboratory? |author=Douglas, S.E. |publisher=LIMSwiki |date=24 August 2022 |accessdate=12 September 2022}}</ref>
 
Among all these activities is the driving goal of better ensuring a safer, more high-quality food and beverage product for consumers. This goal is furthered by the industry's past lessons and regulatory considerations that were made as a result of those lessons.<ref name="DouglasWhatIs22">{{cite web |url=https://www.limswiki.org/index.php/LIMS_FAQ:What_is_the_importance_of_a_food_and_beverage_testing_laboratory_to_society%3F |title=What is the importance of a food and beverage testing laboratory to society? |author=Douglas, S.E. |publisher=LIMSwiki |date=16 August 2022 |accessdate=12 September 2022}}</ref> However, these regulatory requirements place an additional burden on labs trying to meet this common goal, as well as their own internal goals towards quality and excellence. This broad array of analytical techniques and set of regulatory considerations means such labs will continue to turn to [[Informatics (academic field)|informatics]] solutions like the [[laboratory information management system]] (LIMS) and other food safety software, in turn requiring those [[information management]] solutions meet the unique needs of their lab.
 
This brief topical article will examine the typical food and beverage lab's operations and workload, and suggest a base set of LIMS functionality (i.e., system requirements) that is critical to fulfilling the information management and [[workflow]] requirements of this lab type. Additional unique requirements will also be briefly discussed.
 
'''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.
 
 
==Food and beverage laboratory workflow, workload, and information management==
Food traceability market: https://www.einnews.com/pr_news/589558926/food-traceability-market-to-reach-usd-9-75-billion-by-2028-food-traceability-industry-striking-cagr-of-10-2
 
 
Little information can be found as to the percentage of food and beverage laboratories using a LIMS in their workflow. Several surveys from 2020, however, hint that LIMS are important to these types of labs. A survey of 135 professionals—nine percent of them from the food and beverage industry—from laboratory consultancy Astrix Technology found that more than 77 percent of respondents had at least one LIMS implemented in their organization. A separate survey from ''Lab Manager'' about analytical instrument use among readers found that more than 16 percent of them were using instruments for food and beverage analysis. Combined, these surveys suggest that the food and beverage industry is not trivially represented among labs. By extension—and particularly given the importance of integrating instrumentation and their produced data in such an environment—a LIMS or other informatics solution appears to be increasingly critical to eliminating manual processes, improving sample management, increasing productivity, and improving regulatory conformance.
 
==Base LIMS requirements==
Given the above, it's clear LIMS adoption and use must continue in crime labs. But a generic LIMS won't do; it's imperative the lab find a solution that meets all or most its workflow requirements. This more often than not requires a configurable solution that enables trained users to quickly make the changes they need, if those changes make sense within the overall data structure of the LIMS.
 
What follows is a list of system functionality important to most any food and beverage laboratory, with a majority of that functionality found in many vendor software solutions.
 
LIMS vs. food safety software: https://corvium.com/what-is-a-food-intelligence-platform-lims-vs-food-safety-software/ and https://www.foodlogiq.com/solutions/safety-and-quality/ and https://safetychain.com/food-safety-software/ and https://agtech.folio3.com/food-safety-software/ and https://www.fooddocs.com/
 
Integrated informatics for food quality and safety: https://foodsafetytech.com/feature_article/integrated-informatics-optimizing-food-quality-and-safety-by-building-regulatory-compliance-into-the-supply-chain/
 
Digital Transformation of Global Food Security: https://foodsafetytech.com/feature_article/the-digital-transformation-of-global-food-security/
 
LIMS and ISO/IEC 17025 audit for food testing labs: https://foodsafetytech.com/column/is-your-food-testing-lab-prepping-for-an-iso-iec-17025-audit/ and https://foodsafetytech.com/feature_article/how-lims-facilitates-iso-17025-certification-in-food-testing-labs/
 
LIMS and food safety: https://foodsafetytech.com/feature_article/how-advanced-lims-brings-control-consistency-and-compliance-to-food-safety/
 
Supply chain transparency and traceability: https://foodsafetytech.com/column/how-digital-solutions-support-supply-chain-transparency-and-traceability/
 
LIMS and FDA inspections: https://foodsafetytech.com/column/using-lims-get-shape-fdas-visit/
 
FSMA and LIMS: https://foodsafetytech.com/feature_article/traceability-leveraging-automation-to-satisfy-fsma-requirements/
 
LIMS in beverage industry: https://web.archive.org/web/20171009015829/https://www.foodqualityandsafety.com/article/lims-benefits-beverage-sector/
 
LIMS and food safety testing: https://www.e3s-conferences.org/articles/e3sconf/abs/2021/68/e3sconf_netid21_03052/e3sconf_netid21_03052.html
 
LIMS and agriculture genotyping workflows: https://pubmed.ncbi.nlm.nih.gov/16914063/
 
Data management systems in the food industry: https://link.springer.com/chapter/10.1007/978-1-4939-0311-5_3
 
Manufacturing execution system in food and beverage: https://www.sciencedirect.com/science/article/abs/pii/S0260877420300315
 
LIMS and food processing factories (2004): https://www.sciencedirect.com/science/article/abs/pii/S0260877404000846
 
'''Test, sample and case management'''
 
*Sample, property and evidence log-in and management, with support for unique IDs
*Support for pre-logging of death and evidence data before physical materials arrive
*Body reception, location, and disposition support
*[[Barcode]] and RFID support
*End-to-end sample, body, property, and evidence tracking
*Custom and industry-specific test and method management, including for breath alcohol and sexual assault testing
*Test, instrument, subpoena, and other event scheduling
*Test requesting
*Configurable screens and data fields
*Analytical tools, including [[data visualization]], statistical analysis, and [[data mining]] tools
*Data import and export
*Robust query tools
*Document and image management
*Workflow management
*Case management, including case assignment, reassignment, and prioritization, with support for unique and third-party IDs:
**Story-related entry for cases
**Geolocation capture and support
**Mass casualty event support
**Cluster and crime scene mapping tools
**Mobile and offline data entry support for remote work
**Dashboard view of all pertinent information for a case, including criminal case status
**Dashboard or other view showing case and test assignment, status, and backlog
 
'''Quality, security and compliance'''
 
*[[Quality assurance]] / [[quality control]] mechanisms
*Standardized terminology via built-in dictionaries and medical classification codes like [[International Statistical Classification of Diseases and Related Health Problems|ICD]]
*National Association of Medical Examiners (NAME) and International Association of Coroners and Medical Examiners (IAC&ME) accreditation support
*Results review and approval
*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 (log-in requirements, account usage rules, account locking, etc.)
*[[Electronic signature]] support
*Data [[encryption]] and secure communication protocols
*Archiving and [[Data retention|retention]] of case and other data and information
*Configurable data [[backup]]s
*Status updates and alerts
 
'''Operations management and reporting'''
 
*Customizable rich-text reporting, with multiple supported output formats
*Custom and industry-specific forms, including body release, autopsy authorization, media release, cremation authorization, subpoena, etc.
*Support for state-specific death certificates
*Industry-compliant labeling
*Email integration
*Instrument interfacing and data management
*Instrument calibration and maintenance tracking
*Inventory and reagent management
*Third-party software and database interfacing
*Integrated (or online) system help
*Hour, mileage, and expense tracking
*Turnaround time calculation
 
 
==Specialty LIMS requirements==
As noted previously, the DOJ points out many disciplines and sub-disciplines that are used in and near the forensics laboratory. It is beyond the scope of this article to address the system functionality for each. However, an attempt has been made to cover the most important sub-disciplines associated with forensics and medical examiners labs in regards to LIMS functionality.
 
 
 
 
 
==Conclusion==
This brief topical article sought to answer "what are the key elements of a LIMS for food and beverage testing??" It notes that ...
 
==References==
{{Reflist|colwidth=30em}}
 
<!---Place all category tags here-->
[[Category:LIMS FAQ articles (added in 2022)]]
[[Category:LIMS FAQ articles (all)]]
[[Category:LIMS FAQ articles on food and beverage]]

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

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