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Food Analytical Methods

, Volume 12, Issue 11, pp 2469–2473 | Cite as

Interpreting and Reporting Principal Component Analysis in Food Science Analysis and Beyond

  • D. CozzolinoEmail author
  • A. Power
  • J. Chapman
Article
  • 185 Downloads

Abstract

Principal component analysis (PCA) is one of the most widely used data mining techniques in sciences and applied to a wide type of datasets (e.g. sensory, instrumental methods, chemical data). However, several questions and doubts on how to interpret and report the results are still asked every day from students and researchers. This brief communication is inspired in relation to those questions asked by colleagues and students. Please note that this article is a focus on the practical aspects, use and interpretation of the PCA to analyse multiple or varied data sets. In summary, the application of the PCA provides with two main elements, namely the scores and loadings. The scores provide with a location of the sample where the loadings indicate which variables are the most important to explain the trends in the grouping of samples.

Keywords

Principal components Scores Loadings Data sets 

Notes

Acknowledgements

The authors thank the support of our colleagues and friends that encouraged writing this article.

Compliance with Ethical Standards

Conflict of Interest

Dr. Daniel Cozzolino declares that he has no conflict of interest. Dr. Aoife Power declares that she has no conflict of interest. Dr. James Chapman declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human or animal subjects.

Informed Consent

(In case humans are involved) Informed consent was obtained from all individual participants included in the study. (If not applicable on the study) Not applicable.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of ScienceRMIT UniversityMelbourneAustralia
  2. 2.Centre for Research in Engineering and Surface Technology (CREST), FOCAS InstituteTechnological University DublinDublinIreland

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