MAISOR Project Results: Testing and Final Benefits

Revolutionizing Olive Oil Quality Control: The Power of Automatic Classification Systems

In this instance, the project has been developed collaboratively by EZAKO and Mobility ION Technologies SL. and monitored by Pole SCS.

In the realm of olive oil quality control, advancements in technology are paving the way for more accurate, efficient, and cost-effective testing methods. The process of ensuring the purity and quality of olive oil has been divided into three key stages: Classification Method Development, Method Training, and Final Testing. Through meticulous analysis and innovation, the industry is witnessing a transformative shift towards automatic classification systems, offering numerous advantages over traditional testing procedures.

The Three Stages of Testing

  1. Classification Method Development: The initial phase involves the development of a robust classification method. This stage is crucial as it sets the foundation for accurate analysis. Researchers focus on designing algorithms and selecting analytical techniques that can effectively differentiate between various qualities of olive oil.
  2. Method Training: In the second stage, the developed method undergoes rigorous training. This involves analyzing injections of 45 different samples of olive oil. The objective is to fine-tune the system, ensuring it can consistently identify and classify the samples accurately.
  3. Final Testing (Blind Test): The final stage is the ultimate test of the system's reliability. A blind test is conducted using 5 additional samples that were not part of the training dataset. This step is critical in validating the system's performance in real-world scenarios.

Exceptional Results: A 93% Hit Rate

The outcome of this comprehensive testing process is remarkable. The automatic classification system achieved a classification hit rate of 93% on the full dataset. This high level of accuracy underscores the potential of automated systems in enhancing the quality control process in the olive oil industry.

Benefits of Automatic Classification Systems

Implementing an automatic classification system brings a plethora of benefits compared to traditional testing procedures:

  1. Higher and Consistent Accuracy: The hit rate of 93% demonstrates the system's superior accuracy and consistency. Traditional methods often suffer from variability and human error, which can compromise the reliability of results. Automated systems provide a more dependable solution, ensuring each test is as precise as the last.
  2. Scalability: With automatic classification, scaling testing operations becomes feasible. The consistent accuracy of automated systems allows for the testing of larger batches of samples without a drop in performance, thereby meeting the demands of a growing market.
  3. Cost Reduction: Financial benefits are significant with the use of analytical instruments. Automated systems reduce the need for extensive human labor and minimize the chances of costly errors, thereby lowering overall analysis costs.
  4. Objectivity: One of the most critical advantages is the objectivity of automated systems. Unlike traditional methods, which can be influenced by human biases and inconsistencies, automatic classification ensures that every analysis is impartial and based solely on data.


The integration of automatic classification systems in olive oil quality control represents a significant leap forward for the industry. The impressive classification hit rate of 93% highlights the efficacy of these systems in delivering accurate and reliable results. Beyond accuracy, the benefits of scalability, cost reduction, and objectivity make automated systems an indispensable tool for modernizing quality control processes. As the industry continues to embrace technological advancements, the promise of superior quality and efficiency in olive oil testing is becoming a reality.


About Silicon Eurocluster

The Silicon Eurocluster project aims to achieve greater European self-sufficiency, with increased competitiveness and resilience in the electronics value chain, with specific attention to SMEs.

“Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.”