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Multivariate optimization techniques in food analysis - A review.

Sergio L C Ferreira1, Mario M Silva Junior1, Caio S A Felix1

  • 1Universidade Federal da Bahia, Instituto de Química, Grupo de Pesquisa em Química e Quimiometria, Campus Ondina, 40170-115 Salvador, Bahia, Brazil; Instituto Nacional de Ciência e Tecnologia, INCT, de Energia e Ambiente, Universidade Federal da Bahia, 40170-115 Salvador, Bahia, Brazil.

Food Chemistry
|October 8, 2018
PubMed
Summary

This review compares multivariate techniques for optimizing food analysis methods. It highlights the pros and cons of response surface methodologies and chemometric tools for sample preparation and instrumental conditions.

Keywords:
Box Behnken designCentral composite designDoehlert matrixExperimental designFactorial designFoodRobustness

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Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Food Science

Background:

  • Optimization of analytical methods is crucial in food analysis.
  • Multivariate techniques offer efficient approaches to method development.
  • Response surface methodologies and chemometric tools are widely used.

Purpose of the Study:

  • To critically review multivariate techniques for optimizing food analysis methods.
  • To compare different response surface methodologies, detailing their advantages and disadvantages.
  • To discuss the application of chemometric tools in optimizing sample preparation and instrumental conditions for food analysis.

Main Methods:

  • Critical review of existing literature on multivariate techniques.
  • Comparison of central composite designs, Box Behnken designs, and Doehlert matrix.
  • Discussion on the application of these techniques in food sample preparation and instrumental analysis.

Main Results:

  • Response surface methodologies (RSMs) provide a systematic approach to optimization.
  • Central composite designs, Box Behnken designs, and Doehlert matrix have specific applications and limitations.
  • These chemometric tools are effective for optimizing the determination of organic and inorganic species in food.

Conclusions:

  • Multivariate techniques, particularly RSMs, are powerful tools for optimizing food analysis.
  • The choice of technique depends on the specific analytical challenge and desired outcomes.
  • Consideration of multiple responses and robustness testing enhances the reliability of optimized methods.