Multivariate data analysis applied to spectroscopy: Potential application to juice and fruit quality

Cozzolino, D, Cynkar, W, Shah, N and Smith, P 2011, 'Multivariate data analysis applied to spectroscopy: Potential application to juice and fruit quality', Food Research International, vol. 44, no. 7, pp. 1888-1896.


Document type: Journal Article
Collection: Journal Articles

Title Multivariate data analysis applied to spectroscopy: Potential application to juice and fruit quality
Author(s) Cozzolino, D
Cynkar, W
Shah, N
Smith, P
Year 2011
Journal name Food Research International
Volume number 44
Issue number 7
Start page 1888
End page 1896
Total pages 9
Publisher Pergamon Press
Abstract The goal of building a multivariate calibration model is to predict a chemical or physical property from a set of predictor variables, for example the analysis of sugar concentration in fruits using near infrared (NIR) spectroscopy. Effective multivariate calibration models combined with a rapid analytical method should be able to replace laborious and costly reference methods. The quality of a calibration model primarily depends on its predictive ability. In order to build, interpret and apply NIR calibrations not only the quality of spectral data but also other properties such as effect of reference method, sample selection and interpretation of the model coefficients are also important. The objective of this short review is to highlight the different steps, methods and issues to consider when calibrations based on NIR spectra are developed for the measurement of chemical parameters in both fruits and fruit juices. The same principles described in this paper can be applied to other rapid methods like electronic noses, electronic tongues, and fluorescence spectroscopy.
Subject Food Sciences not elsewhere classified
Sensor Technology (Chemical aspects)
Keyword(s) Calibration
Fruits
Multivariate data analysis
Near infrared
Partial least squares
Spectroscopy
DOI - identifier 10.1016/j.foodres.2011.01.041
Copyright notice © 2011 Elsevier
ISSN 0963-9969
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