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Related Concept Videos

Instrument Calibration01:12

Instrument Calibration

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Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
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Data Validation01:15

Data Validation

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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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The absorbance of UV and visible (UV–visible) radiations is measured using a UV–visible spectrophotometer. Deuterium lamps, which emit UV radiation, and tungsten lamps, which produce radiation in the visible region, are used as light sources in UV–visible spectrophotometers. A monochromator or prism is used for diffraction grating, i.e., to split the incoming radiation into different wavelengths. A system of slits is used to focus the desired wavelength on the sample cell.
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Spectrophotometry is the quantitative measurement of the absorption, reflection, diffraction, or transmission of electromagnetic radiation through a material as a function of the intensity and wavelength of the radiation. A spectrophotometer is a device used to measure the change in the radiation intensity caused by its interaction with the material.
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Updated: Nov 6, 2025

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
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A sample selection method specific to unknown test samples for calibration and validation sets based on spectra

Yue Sun1, Meng Yuan1, Xiaoyan Liu1

  • 1School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|May 6, 2021
PubMed
Summary

A new method for selecting calibration and validation samples based on spectral similarity to test samples improves measurement accuracy. This approach optimizes calibration set size and enhances predictive performance for near-infrared spectroscopy applications.

Keywords:
NIR spectroscopyPLS regressionSalvia miltiorrhiza analysisSample selectionSpectra similarity

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Area of Science:

  • Chemometrics
  • Spectroscopy
  • Data Science

Background:

  • Effective calibration and validation set construction is crucial for accurate chemometric models.
  • Representative sample selection ensures reliable model evaluation and prediction.
  • Current methods may not optimally select samples for unknown test data.

Purpose of the Study:

  • To propose a novel method for constructing calibration and validation sets based on spectral similarity.
  • To improve measurement accuracy by selecting samples maximally similar to unknown test samples.
  • To optimize calibration set size for enhanced model performance.

Main Methods:

  • Developed a sample selection method using Euclidean and Mahalanobis distances to estimate spectral similarity.
  • Applied the method to Salvia miltiorrhiza and corn datasets using near-infrared spectroscopy (NIR).
  • Compared the proposed method against Kennard-Stone (KS) and SPXY algorithms.

Main Results:

  • The proposed method selects samples more targeted to unknown test samples.
  • Demonstrated superior predictive performance compared to KS and SPXY methods.
  • Optimization of calibration set size reduced the influence of irrelevant samples.

Conclusions:

  • The proposed spectral similarity-based method enhances calibration and validation set construction.
  • This approach leads to improved predictive accuracy in chemometric modeling.
  • The method offers a more specific and practical solution for real-world applications.