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

Danial Cozzolino, Wieslawa Cynkar, N Shah, Paul Smith

    Research output: Contribution to journalArticlepeer-review

    170 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)1888-1896
    Number of pages9
    JournalFood Research International
    Issue number7
    Publication statusPublished - Aug 2011


    • Calibration
    • Fruits
    • Multivariate data analysis
    • Near infrared
    • Partial least squares
    • Spectroscopy


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