Chemometrics in foodomics: Handling data structures from multiple analytical platforms
Tormod Næs has been coauthoring a review on handling data structures from multiple instruments/platforms in food science. The paper is entitle “Chemometrics in foodomics: Handling data structures from multiple analytical platforms” and was recently published in the journal Trends in Analytical Chemistry
- Multiple analytical platforms provide complementary and synergistic information.
- Simple correlation studies ensure sound foodomics data handling and interpretation.
- orrelation studies can provide foodomics researchers with new ways of looking into data.
- Multi-block methods provide additional tools in foodomics.
Foodomics studies are normally concerned with multifactorial problems and it makes good sense to explore and to measure the same samples on complementary, synergistic analytical platforms that comprise multifactorial sensors and separation methods. However, the challenge of exploring, extracting and describing the data increases exponentially. Moreover, the risk of becoming flooded with non-informative data increases concomitantly.
Acquisition of data from different analytical platforms provides opportunities for checking the validity of the data, comparing analytical platforms and ensuring proper data (pre)processing – all in the context of correlation studies. We provide practical and pragmatic tools to validate and to deal advantageously with data from more than one analytical platform. We emphasize the need for complementary correlation studies within and between blocks of data to ensure proper data handling, interpretation and dissemination. Correlation studies are a preliminary step prior to multivariate data analysis or as an introduction to more advanced multi-block methods.
- Correlation studies;
- Data processing;
- Data validity;
- Multi-block chemometrics;
- Multivariate data analysis;
- Pearson correlation
Skov, T.; Honoré, A. H.; Jensen, H. M.; Næs, T.; Engelsen, S. B., Chemometrics in foodomics: Handling data structures from multiple analytical platforms. TrAC Trends in Analytical Chemistry 2014, 60, (0), 71-79