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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...

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Related Experiment Videos

A novel model selection strategy using total error concept.

Zhisheng Wu1, Qun Ma, Zhaozhou Lin

  • 1Beijing University of Chinese Medicine, Beijing 100102, China.

Talanta
|April 20, 2013
PubMed
Summary
This summary is machine-generated.

Accuracy profile theory offers a superior method for validating partial least square (PLS) models in Chinese material medica. This approach assesses multi-model prediction performance at each concentration level for enhanced accuracy.

Related Experiment Videos

Area of Science:

  • Chemometrics
  • Spectroscopy
  • Traditional Chinese Medicine

Background:

  • Previous research validated partial least square (PLS) models using accuracy profile theory in Chinese material medica.
  • Traditional chemometric indicators (R², RMSEP, RPIQ) inadequately assess multi-model performance across all concentration levels.

Purpose of the Study:

  • To propose accuracy profile theory as a decision tool for evaluating multi-model prediction performance at individual concentration levels.
  • To compare the sensitivity of model selection strategies based on concentration-specific versus overall performance assessment.

Main Methods:

  • Construction of visible and near-infrared (vis/NIR) spectroscopy models using PLS, interval PLS (iPLS), backward interval PLS (BiPLS), and moving window PLS (MWPLS).
  • Application of accuracy profile theory to calculate analytical methodology parameters: linearity, relative bias, uncertainty, repeatability, intermediate precision, lower limit of quantification (LLOQ), and risk.
  • Assessment of multi-model predictive performance at each concentration level.

Main Results:

  • Accuracy profile theory provides a more sensitive model selection strategy by evaluating performance at each concentration level.
  • The "total error concept" within accuracy profile theory effectively demonstrates the prediction performance of different PLS models.
  • Analytical methodology parameters were successfully calculated using the accuracy profile theory.

Conclusions:

  • Model selection based on accuracy profile theory at every concentration level is more sensitive than methods assessing all levels collectively.
  • Accuracy profile theory offers a coherent and robust approach for selecting optimal chemometric models in vis/NIR spectroscopy for material medica analysis.