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Related Concept Videos

Atomic Emission Spectroscopy: Overview01:20

Atomic Emission Spectroscopy: Overview

Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...
Sample Preparation for Analysis: Advanced Techniques01:08

Sample Preparation for Analysis: Advanced Techniques

Accurate analysis of complex samples often requires advanced preparation techniques to achieve reliable and reproducible results. Samples containing inorganic or organic materials can be challenging to dissolve or decompose effectively. Standard sample preparation methods include acid digestion, fusion, dry ashing, and wet digestion.
Acid digestion with strong acids is commonly used to dissolve inorganic materials that are insoluble (do not dissolve) in water. This method can be useful for...
Atomic Emission Spectroscopy: Lab01:29

Atomic Emission Spectroscopy: Lab

AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
Atomic Absorption Spectroscopy: Lab01:21

Atomic Absorption Spectroscopy: Lab

For AAS measurements, samples must be introduced as clear solutions, often requiring extensive preliminary treatment to dissolve materials like soils, animal tissues, and minerals. Common methods for sample preparation include treatment with hot mineral acids, wet ashing, combustion in closed containers, high-temperature ashing, or fusion with reagents.
 Solutions containing organic solvents, such as low-molecular-mass alcohols, esters, or ketones, enhance absorbances by increasing nebulizer...
Inductively Coupled Plasma–Mass Spectrometry (ICP–MS): Overview01:19

Inductively Coupled Plasma–Mass Spectrometry (ICP–MS): Overview

In inductively coupled plasma–mass spectrometry (ICP–MS), an inductively coupled plasma (ICP) torch is used as an atomizer and ionizer. Solid samples are dissolved and volatilized before being introduced into the high-temperature argon plasma, while solution samples are nebulized and passed through the high-temperature argon plasma. Plasma dissociates the analytes and ionizes their component atoms to form a mixture of positive ions and molecular species. The positive ions are then passed on to...
Classifying Matter by Composition03:35

Classifying Matter by Composition

Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
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Related Experiment Video

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Preparation of Food Samples Using Homogenization and Microwave-Assisted Wet Acid Digestion for Multi-Element Determination with ICP-MS
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Compositional data analysis for elemental data in forensic science.

Gareth P Campbell1, James M Curran, Gordon M Miskelly

  • 1Forensic Science Programme, The Department of Chemistry, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand. gcam032@gmail.com

Forensic Science International
|May 5, 2009
PubMed
Summary
This summary is machine-generated.

Forensic science can now objectively discriminate materials using compositional data (CoDa) analysis. This method successfully separated New Zealand nephrite samples with a low error rate, improving accuracy over traditional approaches.

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Area of Science:

  • Geochemistry
  • Forensic Science
  • Data Analysis

Background:

  • Elemental composition analysis is crucial for material discrimination in forensic science.
  • Traditional methods often overlook data constraints inherent in compositional datasets.
  • Compositional Data Analysis (CoDa) offers a robust framework for handling such data.

Purpose of the Study:

  • To apply CoDa analysis for discriminating materials based on elemental composition within a forensic context.
  • To evaluate the effectiveness of CoDa in separating geological samples, specifically New Zealand nephrite.
  • To develop an objective method for material interpretation in forensic investigations.

Main Methods:

  • Utilized a compositional data (CoDa) analysis framework.
  • Applied Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for dimensionality reduction and classification.
  • Reduced the full elemental composition to a descriptive subcomposition for enhanced discrimination.
  • Employed a 10-repeat, three-fold cross-validation technique for model assessment.

Main Results:

  • Achieved successful separation of in situ nephrite outcrops from a defined area.
  • Demonstrated that a descriptive subcomposition, derived through CoDa, was more effective than the full composition for discrimination.
  • The LDA classification model yielded a low mean error rate of 2.9% upon validation.
  • The CoDa framework successfully addressed and managed the constraints of compositional data.

Conclusions:

  • CoDa analysis provides an objective and effective framework for material discrimination in forensic science.
  • The developed methodology offers a significant improvement over subjective pattern-matching approaches.
  • This approach enhances the reliability and accuracy of forensic material analysis through rigorous data handling.