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Single-step calibration, prediction and real samples data acquisition for artificial neural network using a CCD

N Maleki¹, A Safavi, F Sedaghatpour

¹Department of Chemistry, College of Sciences, Shiraz University, Shiraz 71454, Iran.

|October 31, 2008

Summary

A new artificial neural network (ANN) model enables simultaneous determination of aluminum (Al(III)) and iron (Fe(III)) in alloys. This method uses chrome azurol S and a CCD camera for accurate and precise metal ion analysis.

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Area of Science:

Analytical Chemistry
Computational Chemistry

Background:

Accurate determination of aluminum (Al(III)) and iron (Fe(III)) is crucial for alloy characterization.
Existing methods for simultaneous analysis of Al(III) and Fe(III) may face challenges with sensitivity and selectivity.

Purpose of the Study:

To develop an artificial neural network (ANN) model for the simultaneous determination of Al(III) and Fe(III) in alloys.
To utilize chrome azurol S (CAS) as a chromogenic reagent and a CCD camera as a detection system for enhanced analysis.

Main Methods:

An ANN model with three layers and a back-propagation learning rule was trained.
Sigmoid transfer functions were employed in hidden and output layers for nonlinear calibration.
Experimental conditions were optimized to minimize interferences and improve sensitivity and selectivity.

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Main Results:

The ANN model achieved simultaneous determination of Al(III) and Fe(III) in the concentration range of 0.25–4 µg/mL.
The method demonstrated satisfactory accuracy and precision for both metal ions.
Data for calibration, prediction, and real samples were obtained from a single image capture.

Conclusions:

The proposed ANN-based method offers a reliable approach for simultaneous Al(III) and Fe(III) determination in alloys.
The method was successfully applied to synthetic alloy samples, indicating its practical applicability.
This technique enhances analytical efficiency by using a single image for comprehensive data acquisition.