A Characteristic Interval Modeling Method for Simultaneous Detection of Multiple Metal Ions.

Area of Science:

• Analytical Chemistry
• Environmental Science

Background:

• Zinc smelting industrial wastewater contains multi-metal spectral signals with significant overlap.
• This overlap leads to low accuracy and large prediction errors in traditional detection methods.

Purpose of the Study:

• To develop a characteristic interval modeling method for accurate simultaneous detection of copper and nickel.
• To address the challenges posed by overlapping spectral signals in complex industrial wastewater.

Main Methods:

• Preliminary screening of characteristic intervals for copper and nickel based on absorption spectra and varying partition lengths.
• Selection of optimal feature sub-intervals using root mean squares error of cross-validation and correlation coefficient as evaluation indicators.
• Application of partial least squares (PLS) modeling on combined optimal sub-intervals for simultaneous detection.

Main Results:

• Achieved linear determination ranges of 0.3-3.0 mg/L for both copper and nickel.
• Obtained high correlation coefficients of 0.9974 for copper and 0.9966 for nickel.
• Demonstrated a reduction in wavelength variable screening complexity and improved model accuracy.

Conclusions:

• The proposed characteristic interval modeling method enhances the accuracy of polymetallic ion analysis in zinc smelting wastewater.
• This approach provides a foundation for precise and reliable monitoring of heavy metals in industrial effluents.
• The method effectively overcomes spectral signal overlap issues, enabling reliable quantification of target metals.