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==Sandbox begins below==
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[[File:Mechanical Testing Lab (5426178692).jpg|right|200px]]
{{raw:wikipedia::Detection limit}}
'''Title''': ''What is the importance of a materials testing laboratory to society?''
 
'''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''': October 2023
 
==Introduction==
 
Before we can answer why a materials testing laboratory is important to society, we have to ask what a material is, and what testing it looks like. The definition of "material" has varied significantly over the years, dependent on the course of study, laboratory, author, etc. A 1974 definition by Richardson and Peterson that has seen some use in academic study defines a material as "any nonliving matter of academic, engineering, or commercial importance."<ref>{{Cite book |last=Richardson |first=James H. |last2=Peterson |first2=Ronald V. |date= |year=1974 |title=Systematic Materials Analysis, Part 1 |url=https://books.google.com/books?id=BNocpYI8gJkC&printsec=frontcover&dq=Systematic+Materials+analysis&hl=en&newbks=1&newbks_redir=0&sa=X&ved=2ahUKEwjB1OeQx-aAAxWnmmoFHSV2BSsQ6AF6BAgMEAI#v=onepage&q=Systematic%20Materials%20analysis&f=false |chapter=Chapter 1: Introduction to Analytical Methods |series=Materials science series |publisher=Academic Press |place=New York |page=2 |isbn=978-0-12-587801-2 |doi=10.1016/B978-0-12-587801-2.X5001-0}}</ref> But recently biomaterials like biopolymers (as replacements for plastics)<ref>{{Cite journal |last=Das |first=Abinash |last2=Ringu |first2=Togam |last3=Ghosh |first3=Sampad |last4=Pramanik |first4=Nabakumar |date=2023-07 |title=A comprehensive review on recent advances in preparation, physicochemical characterization, and bioengineering applications of biopolymers |url=https://link.springer.com/10.1007/s00289-022-04443-4 |journal=Polymer Bulletin |language=en |volume=80 |issue=7 |pages=7247–7312 |doi=10.1007/s00289-022-04443-4 |issn=0170-0839 |pmc=PMC9409625 |pmid=36043186}}</ref> and even natural<ref>{{Cite journal |last=Kurniawan |first=Nicholas A. |last2=Bouten |first2=Carlijn V.C. |date=2018-04 |title=Mechanobiology of the cell–matrix interplay: Catching a glimpse of complexity via minimalistic models |url=https://linkinghub.elsevier.com/retrieve/pii/S2352431617301864 |journal=Extreme Mechanics Letters |language=en |volume=20 |pages=59–64 |doi=10.1016/j.eml.2018.01.004}}</ref> and engineered biological tissues<ref>{{Cite journal |last=Kim |first=Hyun S. |last2=Kumbar |first2=Sangamesh G. |last3=Nukavarapu |first3=Syam P. |date=2021-03 |title=Biomaterial-directed cell behavior for tissue engineering |url=https://linkinghub.elsevier.com/retrieve/pii/S246845112030057X |journal=Current Opinion in Biomedical Engineering |language=en |volume=17 |pages=100260 |doi=10.1016/j.cobme.2020.100260 |pmc=PMC7839921 |pmid=33521410}}</ref> may be referenced as "materials." (And to Richardson and Peterson's credit, they do add in the preface of their 1974 work that "[a]lthough the volumes are directed toward the physical sciences, they can also be of value for the biological scientist with materials problems."<ref>{{Cite book |last=Richardson |first=James H. |last2=Peterson |first2=Ronald V. |date= |year=1974 |title=Systematic Materials Analysis, Part 1 |url=https://books.google.com/books?id=BNocpYI8gJkC&printsec=frontcover&dq=Systematic+Materials+analysis&hl=en&newbks=1&newbks_redir=0&sa=X&ved=2ahUKEwjB1OeQx-aAAxWnmmoFHSV2BSsQ6AF6BAgMEAI#v=onepage&q=Systematic%20Materials%20analysis&f=false |chapter=Preface |series=Materials science series |publisher=Academic Press |place=New York |page=xiii |isbn=978-0-12-587801-2 |doi=10.1016/B978-0-12-587801-2.X5001-0}}</ref> A modern example would be biodegradable materials research for tissue and medical implant engineering.<ref>{{Cite journal |last=Modrák |first=Marcel |last2=Trebuňová |first2=Marianna |last3=Balogová |first3=Alena Findrik |last4=Hudák |first4=Radovan |last5=Živčák |first5=Jozef |date=2023-03-16 |title=Biodegradable Materials for Tissue Engineering: Development, Classification and Current Applications |url=https://www.mdpi.com/2079-4983/14/3/159 |journal=Journal of Functional Biomaterials |language=en |volume=14 |issue=3 |pages=159 |doi=10.3390/jfb14030159 |issn=2079-4983 |pmc=PMC10051288 |pmid=36976083}}</ref>) Yet today more questions arise. What of matter that doesn't have "academic, engineering, or commercial importance"; can it now be called a "material" in 2023? What if a particular matter exists today but hasn't been thoroughly studied to determine its value to researchers and industrialists? Indeed, the definition of "material" today is no easy task.
 
To complicate things further, a material can be defined based upon the context of use, i.e., as either a raw material or as a product. The ISO 10303-45 standard by the [[International Organization for Standardization]] (ISO), which addresses the representation and exchange of material and product manufacturing information in a standardized way, essentially views materials and products as being interchangeable, complicating matters further.<ref name="ISO10303-45">{{cite web |url=https://www.iso.org/standard/78581.html |title=ISO 10303-45:2019 ''Industrial automation systems and integration — Product data representation and exchange — Part 45: Integrated generic resource: Material and other engineering properties'' |publisher=International Organization for Standardization |date=November 2019 |accessdate=20 September 2023}}</ref><ref name=":0">{{Cite journal |last=Swindells |first=Norman |date=2009 |title=The Representation and Exchange of Material and Other Engineering Properties |url=http://datascience.codata.org/articles/abstract/10.2481/dsj.008-007/ |journal=Data Science Journal |language=en |volume=8 |pages=190–200 |doi=10.2481/dsj.008-007 |issn=1683-1470}}</ref>
 
Taking into account the works of various researchers, as well as ISO 10303-45, the concepts of "raw materials" and "chemical elements," and modern trends towards the inclusion of biomaterials (though discussion of biomaterials will be limited here) in materials science, we can land on the following definition:
 
:A material is discrete matter that is elementally raw (e.g., native metallic and non-metallic elements), fundamentally processed (e.g., calcium oxide), or fully manufactured (by human, automation, or both; e.g., a fastener) that has an inherent set of properties that a human or automation-driven solution (e.g., an [[artificial intelligence]] [AI] algorithm) has identified for a potential or realized use environment.
 
First, this definition more clearly defines the types of matter that can be included, recognizing that manufactured products may still be considered materials. Initially this may seem troublesome, however, in the scope of complex manufactured products such as automobiles and satellites; is anyone really referring to those types of products as "materials"? As such, the word "discrete" is included, which in manufacturing parlance refers to distinct components such as brackets and microchips that can be assembled into a greater, more complex finished product. This means that while both a bolt and an automobile are manufactured "products," the bolt, as a discrete type of matter, can be justified as a material, whereas the automobile can't. Second—answering the question of "what if a particular matter exists today but hasn't been thoroughly studied to determine its value to researchers and industrialists?"—the definition recognizes that the material needs at a minimum recognition of a potential use case. This turns out to be OK, because if no use case has been identified, the matter still can be classified as an element, compound, or substance. It also insinuates that that element, compound, or substance with no use case isn't going to be used in the manufacturing of any material or product. Third, the definition also recognizes the recent phenomena of autonomous systems discovering new materials and whether or not those autonomous systems should be credited with inventorship.<ref>{{Cite journal |last=Ishizuki |first=Naoya |last2=Shimizu |first2=Ryota |last3=Hitosugi |first3=Taro |date=2023-12-31 |title=Autonomous experimental systems in materials science |url=https://www.tandfonline.com/doi/full/10.1080/27660400.2023.2197519 |journal=Science and Technology of Advanced Materials: Methods |language=en |volume=3 |issue=1 |pages=2197519 |doi=10.1080/27660400.2023.2197519 |issn=2766-0400}}</ref>
 
Going down this path leads us to the realization that materials, by definition, are inherently linked to the act of intentional human- or automation-driven creation, i.e., manufacturing and construction.
 
 
 
 
==Conclusion==
 
 
==References==
{{Reflist|colwidth=30em}}

Latest revision as of 18:25, 10 January 2024

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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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