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Related Concept Videos

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Related Experiment Video

Updated: Jun 22, 2026

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize (Zea mays L.)
05:55

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize (Zea mays L.)

Published on: June 16, 2018

Maize authentication: quality control methods and multivariate analysis (chemometrics).

Ioannis S Arvanitoyannis1, Antonios Vlachos

  • 1School of Agricultural Sciences, Department of Agriculture Animal Production and Aquatic Environment, University of Thessaly, Hellas, Greece. parmenion@uth.gr

Critical Reviews in Food Science and Nutrition
|June 2, 2009
PubMed
Summary
This summary is machine-generated.

Determining maize authenticity involves analyzing objective factors like fatty acids and subjective sensory analysis. Multivariate statistical methods are crucial for valid conclusions on maize origin and genetic modification status.

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Published on: April 23, 2012

Area of Science:

  • Agricultural Science
  • Food Science
  • Analytical Chemistry

Background:

  • Maize is a vital global cereal crop with widespread use in processed foods.
  • Authenticity concerns include cultivar, geographical origin, and the presence of genetically modified (GM) maize.
  • Accurate identification of maize authenticity is critical for regulatory compliance and consumer trust.

Purpose of the Study:

  • To review and summarize key findings on maize authenticity using objective and subjective evaluation methods.
  • To highlight the importance of multivariate analysis in addressing maize authenticity issues.
  • To provide a comprehensive overview of current methodologies for maize authentication.

Main Methods:

  • Determination of objective parameters: fatty acids, phenolic compounds, pigments, and heavy metals.
  • Inclusion of subjective evaluations: sensory analysis.
  • Application of multivariate statistical techniques: Principal Component Analysis (PCA), Cluster Analysis, Discriminant Analysis, and Canonical Analysis.

Main Results:

  • Objective and subjective data provide complementary information for maize authentication.
  • Multivariate analysis is essential for integrating diverse datasets and drawing robust conclusions.
  • The review synthesizes findings across various analytical approaches, presented in five tables.

Conclusions:

  • A combination of objective measurements and sensory evaluation, coupled with advanced statistical analysis, is effective for verifying maize authenticity.
  • Accurate authentication supports the integrity of the food supply chain and addresses concerns regarding GM maize.
  • This review consolidates critical information for researchers and industry professionals involved in maize quality control and assurance.