Superior EVOO Quality Production: An RGB Sorting Machine for Olive Classification
Author(s)
Date Issued
2022
Type
article
Volume
11
Issue
18
Journal
Abstract
Extra virgin olive oil (EVOO) is a commercial product of high quality, thanks to its nutritional and organoleptic characteristics. The olives ripeness and the choice of harvest time according to their color and size, strongly influences the quality of the EVOO. The physical sorting of olives with machines performing rapid and objective optical selection, impossible by hand, can improve the quality of the final product. The aim of this study concerns the classification of olives into two qualitative classes, based on the maturity stage and the presence of external defects, through an industrial RGB optical sorting prototype, evaluating its performance and comparing the results with those obtained visually by trained operators. EVOOs obtained from classified olives were characterized through chemical, physical-chemical analysis and sensory profile. For the first time, the optoelectronic technologies in an industrial system was tested on olives to produce superior quality EVOO. The selection allows late harvest, obtaining oils with good characteristics from fully ripe and unripe fruits together, separating defective olives with appropriate calibration and training. Optoelectronic selection creates the opportunity to blend the obtained oils destined to different applications according to the needs of the consumer or producer, using a vanguard technology at low cost.
Additional information
Author Contributions
Conceptualization, F.P., C.C. and S.V. (Simona Violino); methodology, F.P., S.V. (Simona Violino), F.T. and P.T.; software, S.V. (Simone Vasta), F.T. and C.C.; validation, F.P. and C.C.; formal analysis, S.V. (Simone Vasta), L.M., R.M., P.T., L.G., P.D.R. and L.O.; investigation, F.P. and C.C.; resources, L.M. and S.V. (Simona Violino); data curation, C.C, F.P, S.V. (Simona Violino), L.M., L.G. and P.D.R.; writing—original draft preparation, S.V. (Simona Violino) and L.M.; writing—review and editing, S.V. (Simone Vasta), L.M, L.G., C.C., P.D.R. and P.T.; visualization, F.P. and C.C.; supervision, F.P. and C.C.; project administration, F.P. and C.C.; funding acquisition, F.P. and C.C. All authors have read and agreed to the published version of the manuscript.
Conceptualization, F.P., C.C. and S.V. (Simona Violino); methodology, F.P., S.V. (Simona Violino), F.T. and P.T.; software, S.V. (Simone Vasta), F.T. and C.C.; validation, F.P. and C.C.; formal analysis, S.V. (Simone Vasta), L.M., R.M., P.T., L.G., P.D.R. and L.O.; investigation, F.P. and C.C.; resources, L.M. and S.V. (Simona Violino); data curation, C.C, F.P, S.V. (Simona Violino), L.M., L.G. and P.D.R.; writing—original draft preparation, S.V. (Simona Violino) and L.M.; writing—review and editing, S.V. (Simone Vasta), L.M, L.G., C.C., P.D.R. and P.T.; visualization, F.P. and C.C.; supervision, F.P. and C.C.; project administration, F.P. and C.C.; funding acquisition, F.P. and C.C. All authors have read and agreed to the published version of the manuscript.
Subjects
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Name
foods-11-02917.pdf
Size
1.74 MB
Format
Adobe PDF
Checksum (MD5)
c542fac68bd1f5aa8a5a1d0e2af05ea1
Project(s)
MiPAAF, grant number INNOLITEC, D.M. 37067/2018; AGRIDIGIT, DM 36503.7305/2018 of 20 December 2018