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A Multivariate Analysis-Driven Workflow to Tackle Uncertainties in Miniaturized NIR Data.

Giulia Gorla1, Paolo Taborelli1, Barbara Giussani1

  • 1Department of Science and High Technology, University of Insubria, Via Valleggio 9, 22100 Como, Italy.

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Summary
This summary is machine-generated.

This study addresses measurement errors in miniaturized near-infrared (NIR) instruments, crucial for accurate results. It proposes a workflow for analyzing these errors, especially in pharmaceutical samples like pills and tablets.

Keywords:
ASCAdata uncertaintyimage analysisminiaturized NIRmultivariate error

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Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Spectroscopy

Background:

  • Miniaturized near-infrared (NIR) instruments are increasingly used across various fields.
  • Accuracy and reliability of these devices are critical for scientific outcomes but often overlooked.
  • Understanding measurement errors is essential for robust analytical procedures.

Purpose of the Study:

  • To explore and understand measurement errors in analytical procedures using miniaturized NIR instruments.
  • To investigate the impact of sample characteristics and preprocessing techniques on measurement errors.
  • To propose a practical workflow for error analysis in diverse experimental settings.

Main Methods:

  • Multivariate analysis of measurement errors.
  • Application of Analysis of Sampled Components (ASCA) for instrumental factor analysis.
  • Evaluation of preprocessing methods using multivariate error matrices, image histograms, and K index.
  • Investigation of solid pharmaceutical samples (pills and tablets).

Main Results:

  • Multivariate measurement errors are complex and significantly influenced by preprocessing techniques.
  • Sample characteristics, particularly for solid pharmaceutical forms, impact error profiles.
  • ASCA provided insights into critical instrumental factors and method limitations.
  • Joint interpretation of error matrices and summary statistics aids in evaluating preprocessing impacts.

Conclusions:

  • A practical workflow for analyzing measurement errors in miniaturized NIR spectroscopy is presented.
  • The study highlights the importance of considering sample properties and preprocessing choices for accurate NIR analysis.
  • The findings contribute to improving the reliability and accuracy of results obtained from miniaturized NIR instruments.