Olive fruit ripening evaluation and quality assessment by hyperspectral sensing devices

S. Serranti; G. Bonifazi; R. Gasbarrone

doi:10.1117/12.2297352

15 May 2018 Olive fruit ripening evaluation and quality assessment by hyperspectral sensing devices

S. Serranti, G. Bonifazi, R. Gasbarrone

Proceedings Volume 10665, Sensing for Agriculture and Food Quality and Safety X; 106650R (2018) https://doi.org/10.1117/12.2297352
Event: SPIE Commercial + Scientific Sensing and Imaging, 2018, Orlando, FL, United States

Abstract

The present work explores the possible utilization of hyperspectral devices, to evaluate olive fruit ripening in order to define optimal harvesting strategies and/or to perform an in depth characterization of the product addressed to “canned olive” productions, whose conservation characteristics, and related organoleptic attributes, strongly condition market price and producers revenue. A comparison was performed between two different hyperspectral sensing devices: the 1st one working at laboratory scale (Specim SisuCHEMA XL™: 1000-2500 nm) acquiring hyperspectral images and the 2nd one based on a portable architecture (ASD FieldSpec 4™ Standard-Res: 350-2500 nm) acquiring spectra on “spot” bases. Olive fruits collected spectra, acquired with the different sensing architectures, have been correlated with the maturity index and the harvesting time. To reach these goals a chemiometric approach, finalized to set up Partial Least Square (PLS) regression models able to predict olive fruits ripening and quality, was applied. Results have been compared in a proximity sensing perspective and in a “on-line” quality control logic, both finalized to maximize olive fruit derived product quality (i.e. olive oils and/or canned olives) in a costs/benefits perspective taking into account the different sensing architectures and their integrated utilization.

Citation Download Citation

S. Serranti, G. Bonifazi, and R. Gasbarrone "Olive fruit ripening evaluation and quality assessment by hyperspectral sensing devices", Proc. SPIE 10665, Sensing for Agriculture and Food Quality and Safety X, 106650R (15 May 2018); https://doi.org/10.1117/12.2297352

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