Superior EVOO Quality Production: An RGB Sorting Machine for Olive Classification

Area of Science:

• Agricultural Science
• Food Science
• Technology Innovation

Background:

• Extra virgin olive oil (EVOO) quality is significantly influenced by olive ripeness and harvest timing.
• Manual sorting of olives is labor-intensive and less objective than automated methods.
• Optical sorting offers a potential solution for objective and efficient olive classification.

Purpose of the Study:

• To evaluate an industrial RGB optical sorting prototype for classifying olives into two qualitative classes based on maturity and defects.
• To compare the performance of optical sorting with visual inspection by trained operators.
• To characterize the chemical, physical-chemical, and sensory profiles of EVOOs produced from optically sorted olives.

Main Methods:

• Utilized an industrial RGB optical sorting prototype for automated olive classification.
• Compared optical sorting results with visual assessments performed by experienced operators.
• Conducted comprehensive chemical, physical-chemical, and sensory analyses on EVOOs derived from sorted olives.

Main Results:

• The optical sorting system successfully classified olives based on maturity and external defects.
• Optoelectronic selection enabled the blending of oils from olives of varying ripeness, including late harvests.
• EVOO produced from sorted olives demonstrated good characteristics, with potential for tailored applications.

Conclusions:

• Industrial optical sorting technology is a viable, low-cost method for enhancing EVOO quality.
• This technology allows for precise separation of olives, improving oil characteristics and enabling customized blending.
• Optoelectronic selection offers a vanguard approach to producing superior quality EVOO for specific market demands.