[A genetic algorithm approach to qualitative analysis in inductively coupled plasma-atomic emission spectroscopy]

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Computational Chemistry

Context:

• Qualitative analysis in Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES) traditionally requires standard samples and can be affected by spectroscopic interferences.
• Developing automated methods for elemental analysis is crucial for efficiency and accuracy in various scientific fields.

Purpose:

• To develop and evaluate a computer program utilizing a genetic algorithm (GA) for automated qualitative analysis in ICP-AES.
• To demonstrate that GA can eliminate the need for standard samples and mitigate spectroscopic interferences.

Summary:

• A genetic algorithm (GA) approach was implemented for qualitative analysis using sequential ICP-AES, coupled with a custom computer program.
• The GA successfully identified elements and their concentration ranges in unknown samples without requiring standard samples, effectively addressing spectroscopic interferences.
• Optimized GA parameters (Pr=0.6, Pc=0.4, mutation rate=0) yielded results in strong agreement with reference values.

Impact:

• This study presents a promising, automated method for spectroscopic qualitative analysis, reducing reliance on traditional techniques.
• The GA-enhanced ICP-AES analysis offers a potential pathway to more efficient and accurate elemental identification in complex samples.
• Further development of this GA application could establish it as a standard, effective tool for qualitative analysis in ICP-AES.