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Sensitivity for Multivariate Calibration Based on Multilayer Perceptron Artificial Neural Networks.

Fabricio A Chiappini1,2, Franco Allegrini3, Héctor C Goicoechea1,2

  • 1Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Cátedra de Química Analítica I, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Ciudad Universitaria, Santa Fe S3000ZAA, Argentina.

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This study introduces a new method for estimating sensitivity in artificial neural network (ANN) calibration models, improving process monitoring. The developed technique enhances analytical figures of merit for multivariate spectroscopic data analysis.

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Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Machine Learning

Background:

  • Multivariate spectroscopic data analysis is crucial for process monitoring, often requiring advanced methods due to observed non-linearities.
  • Artificial neural networks (ANNs), particularly multilayer perceptrons (MLPs), offer flexibility over linear methods for calibration but lack complete statistical characterization of prediction uncertainty.
  • Estimating prediction errors in analytical calibration is vital for calculating key analytical figures of merit (AFOMs).

Purpose of the Study:

  • To deduce and report equations for estimating sensitivity in multilayer perceptron (MLP)-based calibrations for the first time.
  • To assess the reliability of the derived sensitivity parameter using simulated and experimental data.
  • To apply the new sensitivity estimation method to a biopharmaceutical fluorescence calibration model.

Main Methods:

  • Development of novel equations for sensitivity estimation in MLP models.
  • Validation of the derived sensitivity parameter through analysis of simulated datasets.
  • Assessment of the method's performance using experimental spectroscopic data.
  • Application to a previously established MLP fluorescence calibration for biopharmaceutical analysis.

Main Results:

  • Successfully deduced and reported equations for sensitivity estimation in MLP calibrations.
  • Demonstrated the reliability of the derived sensitivity parameter through rigorous testing.
  • Achieved a sensitivity approximately 30 times greater than the univariate reference method in a biopharmaceutical application.
  • Provided a pathway for enhanced statistical characterization of MLP models in analytical calibration.

Conclusions:

  • The developed method provides a reliable way to estimate sensitivity in MLP-based calibrations.
  • This advancement contributes to a more complete statistical characterization of ANNs in analytical chemistry.
  • The enhanced sensitivity achieved has significant implications for process monitoring and analytical method development, particularly in industries like biopharmaceuticals.