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Related Concept Videos

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
Mass Spectrometry: Isotope Effect01:13

Mass Spectrometry: Isotope Effect

Most elements exist in nature as a mixture of isotopes. The isotopes differ in weight due to their respective number of neutrons. The molecular weight of a molecule is different depending on the specific isotope of its elements involved. As a result, the mass spectrum of the molecule exhibits peaks from the same fragment at multiple positions. The positions of these mass signals depend on the mass differences between isotopes. Furthermore, the intensity of these signals is dependent on the...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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¹H NMR Signal Integration: Overview00:58

¹H NMR Signal Integration: Overview

The intensity of a signal, which can be represented by the area under the peak, depends on the number of protons contributing to that signal. The area under each peak is shown as a vertical line called an integral, with the integral value listed under it, as seen in the proton NMR spectrum of benzyl acetate. Each integral value is divided by the smallest integral value to obtain the ratio of the number of protons producing each signal. The ratio reveals the relative number of protons and not...
Mass Spectrum01:23

Mass Spectrum

A mass spectrum is the graphical representation of the relative abundance of the charged fragments in an analyte plotted against their mass-to-charge ratio (m/z). The plot's x-axis represents the ratio of the mass of the charged fragment to the number of charges it carries. The y axis of the plot represents the relative abundance of each charged species. The relative abundance is calculated from the signal intensity of each charged species recorded at the detector. The most intense signal (the...

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Related Experiment Video

Updated: Jun 17, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

[Comparison of two spectral mixture analysis models].

Qin-Jun Wang1, Qi-Zhong Lin, Ming-Xiao Li

  • 1Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China. wangqin08262002@yahoo.com.cn

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|December 30, 2009
PubMed
Summary
This summary is machine-generated.

Linear spectral mixture analysis (LSMA) outperformed constrained linear spectral mixture analysis (CLSMA) in spectral unmixing accuracy. Band selection further improved LSMA and CLSMA performance, highlighting its importance for reducing spectral correlation and enhancing unmixing results.

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Last Updated: Jun 17, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

Area of Science:

  • Remote Sensing
  • Spectral Analysis
  • Geospatial Data Processing

Context:

  • Spectral mixture analysis is crucial for decomposing mixed pixel spectra into constituent end-member proportions.
  • Linear Spectral Mixture Analysis (LSMA) and Constrained Linear Spectral Mixture Analysis (CLSMA) are common methods for this task.
  • Understanding the performance differences between these models is vital for accurate geospatial analysis.

Purpose:

  • To experimentally compare the spectral unmixing accuracy of LSMA and CLSMA.
  • To evaluate the impact of band selection on the performance of both LSMA and CLSMA.
  • To determine which model and approach yield superior results in spectral unmixing.

Summary:

  • Experiments using four end members (red, green, blue, yellow) and 39 mixed samples demonstrated LSMA's higher accuracy over CLSMA when using all spectral bands.
  • LSMA achieved a total error of 0.30087 compared to CLSMA's 0.37552.
  • After band selection, LSMA's total error was 0.28095 and CLSMA's was 0.29805, with LSMA still showing better accuracy.
  • Both models benefited from band selection, with LSMA showing a 0.02 error reduction and CLSMA a 0.077 reduction, indicating the advantage of reduced spectral correlation.

Impact:

  • LSMA demonstrates superior spectral unmixing performance compared to CLSMA, particularly when accounting for real-world measurement errors.
  • Band selection is a critical step that significantly enhances the accuracy of spectral unmixing for both LSMA and CLSMA.
  • These findings provide valuable insights for optimizing spectral unmixing techniques in remote sensing and other applications requiring precise material composition analysis.