Abstract
Multivariate statistical methods involve the simultaneous analysis of more than one outcome variable. In applied use, this definition is sometimes relaxed, but it typically includes methods such as principal component analysis, factor analysis, cluster analysis, and partial least-squares regression. Methods based on principal component analysis are frequently encountered in food science, and it is therefore compared with factor analysis, commonly used in biostatistics. The interpretation of results, especially in relation to the original variables, might be challenging, which could represent these methods’ weakest link from an applied point of view.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bro R, Smilde AK (2003) Centering and scaling in component analysis. J Chemometr 17:16–33. doi:10.1002/cem.773
Esbensen KH (2001) Multivariate data analysis – in practice. An introduction to multivariate data analysis and experimental design, 5th edn. CAMO ASA, Oslo
Fabrigar LR, Wegener DT, MacCallum RC, Strahan EJ (1999) Evaluating the use of exploratory factor analysis in psychological research. Psychol Methods 4:272–299. doi:10.1037/1082-989X.4.3.272
Fahey MT, Thane CW, Bramwell GD, Coward WA (2007) Conditional gaussian mixture modeling for dietary patterns. J R Stat Soc Ser A-G 170:149–166. doi:10.1111/j.1467-985X.2006.00452.x
Johnson RA, Wichern DW (2007) Applied multivariate statistical analysis, 6th edn. Pearson Prentice Hall, Upper Saddle River
Kahn JH (2006) Factor analysis in counseling psychology research, training and practice: principles, advances, and applications. Couns Psychol 34:684–718. doi:10.1177/0011000006286347
Karoui R, Baerdemaeker JD (2007) A review of the analytical methods coupled with chemometric tools for the determination of the quality and identity of dairy products. Food Chem 102:621–640. doi:10.1016/j.foodchem.2006.05.042
Kozak M, Scaman CH (2008) Unsupervised classification methods in food sciences: discussion and outlook. J Sci Food Agric 88:1115–1127. doi:10.1002/jsfa.3215
Pripp AH, Isaksson T, Stepaniak L, Sørhaug T, Ardö Y (2005) Quantitative structure activity relationship modelling of peptides and proteins as a tool in food science. Trends Food Sci Technol 16:484–494. doi:10.1016/j.tifs.2005.07.003
Siebert KJ (2003) Modeling protein functional properties from amino acid composition. J Agric Food Chem 51:7792–7797. doi:10.1021/jf0342775
Suhr D (2009) Principal component analysis vs. exploratory factor analysis. SUGI 30 proceedings. http://www2.sas.com/proceedings/sugi30/203-30.pdf. Accessed 5 Jun 2012
Tzouros NE, Arvanitoyannis IS (2001) Agricultural produces: synopsis of employed quality control methods for the authentication of foods and application of chemometrics for the classification of foods according to their variety of geographical origin. Crit Rev Food Sci Nutr 41:287–319. doi:10.1080/20014091091823
Widaman KF (1993) Common factor-analysis versus principal component analysis – differential bias in representing model parameters. Multivar Behav Res 28:263–311. doi:10.1207/s15327906mbr2803_1
Wold S (1976) Pattern-recognition by means of disjoint principal components models. Pattern Recognit 8:127–139. doi:10.1016/0031-3203(76)90014-5
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this chapter
Cite this chapter
Pripp, A.H. (2013). Application of Multivariate Analysis: Benefits and Pitfalls. In: Statistics in Food Science and Nutrition. SpringerBriefs in Food, Health, and Nutrition. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5010-8_5
Download citation
DOI: https://doi.org/10.1007/978-1-4614-5010-8_5
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-5009-2
Online ISBN: 978-1-4614-5010-8
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)