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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
An analytical balance measures mass and requires regular calibration to...
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Calibration Curves: Linear Least Squares01:20

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
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In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
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Vector Algebra: Method of Components01:08

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It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
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Linear Approximation in Frequency Domain01:26

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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A Multimodal Wide-Field Fourier-Transform Raman Microscope
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Principal component analysis or kernel principal component analysis based joint spectral subspace method for

Peng Shan1, Yuhui Zhao2, Qiaoyun Wang1

  • 1College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|November 8, 2019
PubMed
Summary
This summary is machine-generated.

Kernel principal component analysis (KPCA) based joint spectral space (JKPCA) offers superior calibration model transfer. This method, along with principal component analysis (PCA) based joint spectral space (JPCA), enables effective spectral data analysis when only master and slave spectra are available.

Keywords:
Calibration transferJoint spectral subspaceKernel principal component analysis (KPCA)Multivariate calibrationPartial least squares (PLS)Principal component analysis (PCA)

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Area of Science:

  • Chemometrics
  • Spectroscopy
  • Data Analysis

Background:

  • Calibration model transfer is crucial for applying models across different instruments or conditions.
  • Existing methods often struggle with limited standardization samples (master and slave spectra).

Purpose of the Study:

  • To propose novel methods for calibration model transfer using joint spectral spaces.
  • To evaluate the performance of these new methods against established techniques.

Main Methods:

  • Development of joint principal component analysis (JPCA) and joint kernel principal component analysis (JKPCA) methods.
  • Utilizing PCA and KPCA to create a shared spectral subspace for master and slave spectra.
  • Estimating a transfer matrix and building partial least squares (PLS) models on extracted low-dimensional features.

Main Results:

  • JKPCA demonstrated the best transfer ability across two datasets.
  • JPCA performed comparably to or better than established methods like GLS and SST.
  • Both JPCA and JKPCA effectively extract shared features for model transfer.

Conclusions:

  • JKPCA is a highly effective method for calibration model transfer, especially when dealing with limited standardization data.
  • JPCA offers a competitive alternative, outperforming several other common calibration transfer techniques.