On-field monitoring of fruit ripening evolution and quality parameters in olive mutants using a portable NIR-AOTF device

Marco Cirilli; Andrea Bellincontro; Stefania Urbani; Maurizio Servili; Sonia Esposto; Fabio Mencarelli; Rosario Muleo

doi:10.1016/j.foodchem.2015.11.129

On-field monitoring of fruit ripening evolution and quality parameters in olive mutants using a portable NIR-AOTF device

Food Chem. 2016 May 15:199:96-104. doi: 10.1016/j.foodchem.2015.11.129. Epub 2015 Nov 30.

Authors

Marco Cirilli¹, Andrea Bellincontro², Stefania Urbani³, Maurizio Servili³, Sonia Esposto³, Fabio Mencarelli⁴, Rosario Muleo⁵

Affiliations

¹ Department of Agriculture, Forestry, Nature and Energy (DAFNE), Molecular Ecophysiology of Woody Plant Laboratory, University of Tuscia, Viterbo, Via San Camillo de Lellis snc, 01100 Viterbo, Italy.
² Department for Innovation in Biological Agro-food and Forest systems (DIBAF), Postharvest Laboratory, University of Tuscia, Viterbo, Via San Camillo de Lellis snc, 01100 Viterbo, Italy. Electronic address: bellin@unitus.it.
³ Department of Agricultural, Food and Environmental Sciences, University of Perugia, Via S. Costanzo, 06126 Perugia, Italy.
⁴ Department for Innovation in Biological Agro-food and Forest systems (DIBAF), Postharvest Laboratory, University of Tuscia, Viterbo, Via San Camillo de Lellis snc, 01100 Viterbo, Italy.
⁵ Department of Agriculture, Forestry, Nature and Energy (DAFNE), Molecular Ecophysiology of Woody Plant Laboratory, University of Tuscia, Viterbo, Via San Camillo de Lellis snc, 01100 Viterbo, Italy. Electronic address: muleo@unitus.it.

PMID: 26775949
DOI: 10.1016/j.foodchem.2015.11.129

Abstract

This study optimizes the application of portable Near Infrared-Acousto Optically Tunable Filter (NIR) device to meet the increasing demand for cost-effective, non-invasive and easy-to-use methods for measuring physical and chemical properties during olive fruit development. Fruits from different phenotypically cultivars were sampled for firmness, total and specific phenols detection by HPLC, total anthocyanins, chlorophyll and carotenoids detection by spectrophotometry. On the same fruits, a portable NIR device in diffuse reflectance mode was employed for spectral detections. Predictive models for firmness, chlorophyll, anthocyanins, carotenoids and rutin were developed by Partial Least Square analysis. Oleuropein, verbascoside, 3,4-DHPEA-EDA, and total phenols were used to develop a validation model. Internal cross-validation was applied for calibration and predictive models. The standard errors for calibration, cross-validation, prediction, and RPD ratios (SD/SECV) were calculated as references for the model effectiveness. The determination of the optimal harvesting time facilitates the production of high quality extra virgin olive oil and table olives.

Keywords: Chlorophyll a (PubChem CID: 6449992); Chlorophyll b (PubChem CID: 46173742); Firmness; NIR-AOTF spectroscopy; Oleuropein; Oleuropein (PubChem CID: 5281544); Oleuropein-aglycone di-aldehyde (3,4-DHPEA-EDA) (PubChem CID: 56842347); Olive fruits; Partial Least Square regression (PLSR); Quercetin-3-O-rutinoside (rutin) (PubChem CID: 5280805); Rutin; Total phenols; Verbascoside; Verbascoside (PubChem CID: 5281800).

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Calibration
Fruit / chemistry
Least-Squares Analysis
Mutation
Olea / chemistry*
Olive Oil / standards*
Phenols / analysis
Spectroscopy, Near-Infrared

Substances

Olive Oil
Phenols