Characterisation of olive fruit for the milling process by using visible/near infrared spectroscopy

  • Roberto Beghi Department of Agricultural and Environmental Sciences – Production, Landscape, Agroenergy, University of Milan, Italy.
  • Valentina Giovenzana | valentina.giovenzana@unimi.it Department of Agricultural and Environmental Sciences – Production, Landscape, Agroenergy, University of Milan, Italy.
  • Raffaele Civelli Department of Agricultural and Environmental Sciences – Production, Landscape, Agroenergy, University of Milan, Italy.
  • Enrico Cini Department of Economics, Engineering, Science and Technology Agriculture and Forestry, University of Florence, Italy.
  • Riccardo Guidetti Department of Agricultural and Environmental Sciences – Production, Landscape, Agroenergy, University of Milan, Italy.

Abstract

Increasing consumption of olive oil and table olives has recently determined an expansion of olive tree cultivation in the world. This trend is supported by the documented nutritional value of the Mediterranean diet. The aim of this work was to test a portable visible/ near infrared (vis/NIR) system (400-1000 nm) for the analysis of physical-chemical parameters, such as olive soluble solid content (SSC) and texture before the olive oil extraction process. The final goal is to provide the sector with post-harvest methods and sorting systems for a quick evaluation of important properties of olive fruit. In the present study, a total of 109 olives for oil production were analysed. Olive spectra registered with the optical device and values obtained with destructive analysis in the laboratory were analysed. Specific statistical models were elaborated to study correlations between optical and laboratory analysis, and to evaluate predictions of reference parameters obtained through the analysis of the visible-near infrared range. Statistical models were processed using chemometric techniques to extract maximum data information. Principal component analysis (PCA) was performed on vis/NIR spectra to examine sample groupings and identify outliers, while partial least square (PLS) regression algorithm was used to correlate samples spectra and physical- chemical properties. Results are encouraging. PCA showed a significant sample grouping among different ranges of SSC and texture. PLS models gave fairly good predictive capabilities in validation for SSC (R2=0.67 and RMSECV%=7.5%) and texture (R2=0.68 and RMSECV%=8.2%).

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Published
2013-10-04
Section
Original Articles
Keywords:
olives, visible/near infrared spectroscopy, ripening parameters, chemometrics, post-harvest.
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How to Cite
Beghi, R., Giovenzana, V., Civelli, R., Cini, E., & Guidetti, R. (2013). Characterisation of olive fruit for the milling process by using visible/near infrared spectroscopy. Journal of Agricultural Engineering, 44(2), e8. https://doi.org/10.4081/jae.2013.e8