Fast and Reliable Determination of Virgin Olive Oil Quality by Fruit Inspection Using Computer Vision

Influence of the Degree of Fruitiness on the Quality Assessment of Virgin Olive Oils Using Electronic Nose Technology

The electronic nose is a non-invasive technology suitable for the analysis of edible oils. One of the practical applications in the olive oil industry is the classification of virgin oils based on their sensory characteristics. Notwithstanding that this technology, at this stage, cannot realistically replace the currently used methods, it is fruitful for a preliminary analysis of the oil quality. This work makes use of this technology to develop a methodology for the detection of the threshold by which an extra-virgin olive oil (EVOO) drops into the virgin olive oil (VOO) category. With this aim, two features were studied: the level of fruitiness level and the type of defect. The results showed a greater influence of the level of fruitiness than the type of defect in the determination of the detection threshold. Furthermore, three of the sensors (S2, S7 and S9) of the commercial e-nose PEN3 were identified as the most discriminating in the classification between EVOO and VOO oils.

Zero Defect Manufacturing in the Food Industry: Virgin Olive Oil Production

This paper provides a zero defect manufacturing (ZDM) approach designed for the virgin olive oil (VOO) industry, with the objective of producing the best possible product using sustainable methods. A deep analysis of related work for ZDM and the current state-of-the-art technology in the VOO elaboration process is presented, along with the implications of the well-known trade-off between quality and extraction yield and the importance of having the right information on the state of the fruits and the main technological variables of the process. Currently available new technologies, such as smart devices with cloud connectivity, enable having the required amount of data and information in real-time, thus making the concept of ZDM possible. Together with the proposed ZDM approach and strategies, the basic requirements and the first steps towards the implementation of ZDM in this productive sector are identified.

A Portable Battery-Operated Sensor System for Simple and Rapid Assessment of Virgin Olive Oil Quality Grade

Virgin olive oil quality is assessed by chemical as well as sensory analysis. Two of the most important parameters that define the quality of virgin olive oils are the free acidity and the peroxide index. These chemical parameters are usually determined by manual titration procedures that must be carried out in a laboratory by trained personnel. In this paper, a portable sensor system to support the quality grade assessment of virgin olive oil is presented. The system is battery operated and characterized by small dimensions, light weight and quick measurement response (about 30 s). The working principle is based on the measurement of the electrical conductance of an emulsion between a chemical reagent and the olive oil sample. Two different chemical reagents have been investigated: (1) a hydro-alcoholic solution (HAS), made of 60% ethanol and 40% distilled water; (2) 100% distilled water (DW). Tests have been carried out on a set of 40 olive oil samples. The results have shown how, for most of the fresh virgin olive oil samples (31 samples out of 40), the free acidity can be estimated with good accuracy from the electrical conductance of the emulsion using HAS as the reagent. In the case of the full set of samples, the emulsion electrical conductance, using HAS as the reagent, is a function of both the sample free acidity as well as the compounds produced by oil oxidation, and a compensation method based on the measured electrical conductance, using DW as the reagent, has been introduced to improve the accuracy in the estimated free acidity. Tests have also been carried out on the full set of samples, using a k-nearest neighbors algorithm, to demonstrate the feasibility of olive oil classification according to the quality grade. The results have shown how measurements carried out using only the HAS reagent provide better classification accuracy than measurements carried out using both the HAS and DW reagents. The proposed system can be a low-cost alternative to standard laboratory analyses to evaluate the quality grade of virgin olive oil.

Effects of an integrated harvest system on the quality of olive fruit for small producers

Small producers confront specific challenges when they opt to produce high-quality olive fruit. Limited resources for investing in harvest machinery and manpower are the main reasons for continuing a traditional harvest method that puts the final product and its economic value at risk. This paper discusses the efficiency of an integrated harvest system as a possible solution to these specific challenges. The system is formed by a newly designed manual harvesting device and the use of a cooling room near the olive grove. Both systems were evaluated to assess their feasibility for optimum conditions before processing. The combined effect of the harvesting method and cold storage on the fruit characteristics (incidence of decay, skin color, weight loss, firmness, respiration, and ethylene production) was evaluated on three different varieties (‘Arbequina’, ‘Picual’ and ‘Verdial’) and four different storage times (0, 4, 8, and 14 days). The results indicate that the proposed harvesting method in combination with an appropriate cooling system offers an affordable alternative for obtaining fruit with the best physiological characteristics.

Automatic System for the Detection of Defects on Olive Fruits in an Oil Mill

The ripeness and sanitary state of olive fruits are key factors in the final quality of the virgin olive oil (VOO) obtained. Since even a small number of damaged fruits may significantly impact the final quality of the produced VOO, the olive inspection in the oil mill reception area or in the first stages of the productive process is of great interest. This paper proposes and validates an automatic defect detection system that utilizes infrared images, acquired under regular operating conditions of an olive oil mill, for the detection of defects on individual fruits. First, the image processing algorithm extracts the fruits based on the iterative application of the active contour technique assisted with mathematical morphology operations. Second, the defect detection is performed on the segmented olives using a decision tree based on region descriptors. The final assessment of the algorithm suggests that it works effectively with a high detection rate, which makes it suitable for the VOO industry.