External Standard Method
External Standard Method - Related Videos
11:28
Method of Standard Addition
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2023
Source: Laboratory of Dr. Paul Bower - Purdue University The method of standard additions is a quantitative analysis method, which is often used when the sample of interest has multiple components that result in matrix effects, where the additional components may either reduce or enhance the analyte absorbance signal. That results in significant errors in the analysis results. Standard additions are commonly used to eliminate matrix effects from a measurement, since it is assumed that the...
07:22
Standardized Method for Measuring Collection Efficiency from Wipe-sampling of Trace Explosives
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Cited by 7 •
2017
Optimized sampling protocols and the development of new wipe materials can be facilitated by standardized measurements of collection efficiency from wipe-sampling. Our approach for sampling trace explosives uses an automated device to control speed, force, and distance during wipe-sampling followed by extraction of collected explosives.
01:20
Externalities
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2025
Externalities are unintended side effects of economic activities that impact third parties who are not directly involved in the market transaction. They can have positive or negative effects that can influence society and the environment in various ways. Positive Externalities Positive externalities occur when a market activity produces benefits for others without those beneficiaries having to pay for it. Examples include: Education: When individuals receive an education, society benefits...
01:23
Negative Externalities
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2025
A negative externality occurs when an economic transaction imposes unintended costs on third parties who are not directly involved in the market transaction. These external costs are not captured in the market price. Negative Externalities and Market Failure Consider a chemical manufacturing plant that produces industrial chemicals for sale. The plant's private costs include raw materials, labor, equipment, and maintenance, which are reflected in the market price of the chemicals. However,...
01:20
Positive Externalities
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2025
Positive externalities occur when the actions of an individual or business engaging in a market exchange unintentionally benefit third parties who are not involved in the transaction. A common example is education. When people pursue higher education, they not only gain personal knowledge and skills that benefit their future earnings but also society as a whole, which benefits from an educated workforce that leads to increased productivity and innovation across the economy. In economic terms,...
FAQs
The calibration curve establishes the relationship between instrumental signal intensity and analyte concentration across the prepared standards. Linear regression provides a fitted relationship that can be used to locate the concentration corresponding to a sample signal. This interpolation is meaningful only when the sample response is assessed within the concentration behavior represented by the standards.
Matching instrument conditions helps ensure that differences in signal reflect analyte concentration rather than changes in measurement settings. Relevant conditions include the instrument response during analysis and the procedure used for measuring standards and samples. If the response changes between measurements, the calibration relationship may no longer accurately represent the sample concentration.
Matrix differences can reduce accuracy because components surrounding the analyte may influence the measured instrumental response. External standards are prepared separately from samples, so their composition may not fully resemble the sample matrix. Minimizing these differences makes the standards more representative of sample behavior and improves the reliability of concentration estimates.
First, prepare standards with known analyte concentrations and measure their instrumental responses. Next, analyze the biochemical samples under the same conditions. Use the standard responses to construct a calibration curve, commonly with linear regression, and then interpolate each sample signal on that curve to obtain the corresponding analyte concentration.
In biochemistry, the approach can support quantitative assays for metabolites, proteins, nucleic acids, and other biomolecules. It can be paired with instrumental techniques such as spectrophotometry or chromatography, provided the instrument produces a measurable response related to analyte concentration. The resulting calculations convert those responses into quantitative sample information.
Accuracy depends on several linked conditions: the instrument response must remain stable, the standards must span appropriate concentrations, and matrix differences between standards and samples should be minimized. Weakness in any of these areas can distort the calibration relationship or the sample interpolation, leading to less reliable biochemical concentration measurements.