Search Results (9)

Sewage sludge, characterized by its high organic matter and nutrient content, as well as the presence of microbial pathogens and other contaminants, requires proper management due to its significant generation rate. The table olive sector, which is highly significant in Spain as a global leader in production and export, generates various waste streams such the Organic Solid By-Products from Table Olive Processing (OSBTOP), which are mainly derived from the olive pit after the pitting process. The main aim of this study was to enhance the methane production performance of sewage sludge through co-digestion with OSBTOP as a co-substrate. Batch assays demonstrated that employing OSBTOP as a co-substrate increased methane content by 35–41% across all tested mixtures. While the highest methane yield was produced at a 40:60 (sludge:OSBTOP) ratio, a 60:40 mixture proved to be a more advantageous option for scale-up and practical application. This is attributed to factors such as the higher availability of sludge and its inherent buffering capacity, which counteracts the accumulation of volatile fatty acids and promotes process stability, thereby contributing to the study’s objective of significantly enhancing methane production from sewage sludge through co-digestion. In semi-continuous operation, methane yields in the co-digestion scenario exceeded those of mixed sludge digestion, showing a yield of 180 versus 120 LCH₄⁻¹ · kgVS_added⁻¹, representing a 50% improvement. This study highlights the potential of anaerobic digestion as a strategy for valorizing OSBTOP, a by-product with no prior studies, while demonstrating that its co-digestion with sewage sludge enhances methane generation, offering a sustainable approach to organic waste treatment. Full article

Computer Vision (CV) has proven to be a powerful tool for automation in agri-food industrial processes, offering high-precision solutions tailored to specific working conditions. Recent advancements in Artificial Neural Networks (ANNs) have revolutionized CV applications, enabling systems to autonomously learn and optimize tasks. However, ANN-based approaches often require complex development and lengthy training periods, making their implementation a challenge. In this study, we explore the use of the Segment Anything Model (SAM), a pre-trained neural network developed by META AI in 2023, as an alternative for industrial segmentation tasks in the table olive (Olea europaea L.) processing industry. SAM’s ability to segment objects regardless of scene composition makes it a promising tool to improve the efficiency of olive pitting machines (DRRs). These machines, widely employed in industrial processing, frequently experience mechanical inefficiencies, including the “boat error,” which arises when olives are improperly oriented, leading to defective pitting and pit splinter contamination. Our approach integrates SAM into n CV workflow to diagnose and quantify boat errors without designing or training an additional task-specific ANN. By analyzing the segmented images, we can determine both the percentage of boat errors and the size distribution of olives during transport. The results validate SAM as a feasible option for industrial segmentation, offering a simpler and more accessible solution compared to traditional ANN-based methods. Moreover, our statistical analysis reveals that improper calibration—manifested as size deviations from the nominal value—does not significantly increase boat error rates. This finding supports the adoption of complementary CV technologies to enhance olive pitting efficiency. Future work could investigate real-time integration and the combination of CV with electromechanical correction systems to fully automate and optimize the pitting process. Full article

Table olive pitting machines (DRRs) are essential in the agri-food industry but face significant limitations that constrain their performance and compromise process reliability. The main defect, known as the “boat error”, results from improper olive orientation during pitting, leading to bone fragmentation, pulp damage, and potential risks to consumer safety. Traditional quality control methods, such as the use of flotation tanks and expert sensory evaluation, rely on destructive sampling, are time-consuming, and reduce overall productivity. To address these challenges, this study presents a novel computer vision (CV) system integrated into a commercial DRR machine. The system captures high-speed images of Gordal olives (Olea europaea regalis) just before pitting; these are later analyzed offline using a custom MATLAB application that applies HSV-based segmentation and morphological analysis to quantify the olive size and orientation. The method accurately identifies boat error cases based on angular thresholds, without interrupting the production flow or damaging the product. The results show that 97% of olives were correctly aligned, with only 1.1% presenting critical misorientation. Additionally, for the first time, the system allowed a detailed evaluation of the olive size distribution at the machine inlet, revealing an unexpected proportion of off-caliber olives. This contamination in sizing suggests a possible link between calibration deviations and the occurrence of boat errors, introducing a new hypothesis for future investigation. While the current implementation is limited to offline analysis, it represents a non-destructive, low-cost, and highly precise diagnostic tool. This work lays the foundation for a deeper understanding of DRR machine behavior and provides a framework for future developments aimed at optimizing their performance through targeted correction strategies. Full article

The olive oil industry recently introduced a novel multi-phase decanter with the “Leopard DMF” series, which gives a by-product called pâté, made up of pulp and olive wastewater with a high content of phenolic substances and without pits. This study aims to create a new culture medium, the Olive Juice Broth (OJB), from DMF pâté, and apply it to select bacteria strains able to survive and degrade the bitter substances normally present in the olive fruit. Thirty-five different bacterial strains of Lactiplantibacillus plantarum from the CREA-IT.PE Collection of Microorganisms were tested. Seven strains characterized by ≥50% growth in OJB (B31, B137, B28, B39, B124, B130, and B51) showed a degradation of the total phenolic content of OJB ≥ 30%. From this set, L. plantarum B51 strain was selected as a starter for table olive production vs. spontaneous fermentation. The selected inoculant effectively reduced the debittering time compared to spontaneous fermentation. Hydroxytyrosol, derived from oleuropein and verbascoside degradation, and tyrosol, derived from ligstroside degradation, were produced faster than during spontaneous fermentation. The OJB medium is confirmed to be useful in selecting bacterial strains resistant to the complex phenolic environment of the olive fruit. Full article

Olive paté is a traditional Italian food typical of the Mediterranean diet, derived from debittered, pitted, and crushed table olives. Currently, there is no sensory standard for the quality of the product. In this research, a specific lexicon and an innovative profile sheet for sensory analysis of olive paté have been developed and tested to fill the gap. The list of descriptors was set in terms of negative sensations, olfactory/gustatory sensations, and kinaesthetic attributes. The assessors of a professional panel were first trained with and then used the profile sheet to characterize 40 olive paté commercial samples from five different Olea europaea L. cultivars. From a sensorial point of view, Nocellara del Belice paté was perceived as the saltiest, Nocellara etnea as the bitterest, and Hojiblanca and Bella di Cerignola were the most balanced and characterized on the olfactory/gustatory level. In terms of texture, Hojiblanca paté is the most consistent and grainiest. To support and verify kinaesthetic descriptors, textural analysis by back extrusion was performed. Through the agglomerative hierarchical analysis, the various paté showed a clear clusterization into two clusters: Nocellara del Belice-Itrana and Hojblanca-Nocellara etnea-Bella di Cerignola. The results obtained showed the validity of the profile sheet to correctly assess the different products. Full article

A prospecting campaign in the Maltese Islands has ensured the survival of several ancient olive trees (Olea europaea L.), genetically distant from known cultivars. Most of these plants were abandoned or partially cultivated. A two-year evaluation of fruit characteristics and compositions was performed on samples collected from the main representatives of these indigenous genotypes. Analyses were carried out using Gas Chromatography, High-Performance Liquid Chromatography and Near Infrared Spectrometry. Among the fruit samples, a wide range of variations was observed. Some of the genotypes showed fruit traits suitable for table olive production. This is the case of samples with a pulp/pit ratio higher than four, such as 1Wardija, 1Caritas, 1Plattini, 1Bingemma Malta and 3Loretu, whilst 1Bidni, 1Mellieha, 2Qnotta, 3Loretu, 1Bingemma Malta and 1Caritas were suitable for dual purpose. The total phenol content ranged from 6.3 (1Wardija) to 117.9 (2Mtarfa) g/kg of fresh pulp. The average percentage of MUFA was quite low for most of the varieties. These genotypes, which presumably originated in the Maltese Islands and are well adapted to the local pedo-climatic conditions, are being propagated for the following evaluation of their bio-agronomical performance (production, suitability to intensive cultivation, environmental sustainability, product quality, etc.). The purpose is to select, among these local genotypes, the most outstanding varieties, in terms of phenolic and FA profile and agronomical potential, to spread into cultivation, thereby contributing to an increase in the quality of the local table and olive oil production, strongly linked to the territory. Full article

This paper presents the results of the transformation into table olives of drupes belonging to three Italian cultivars: Carolea, Leccino and Nocellara Messinese, widely used for virgin olive oil production, by using the two most common methods to produce fermented table olives: the Spanish-style method (SS) and the natural fermentation (NF). The most suitable drupes as table olives due to their flesh-to-pit ratio and high-weight fruits were Nocellara Messinese olives. From the results obtained, it was highlighted that fermentation must be improved by using a selected starter culture that can drive the fermentation by rapid acidification. In fact, the long time required by NF results in a lower pH close to the hygienic safety limit but not low enough to be considered as a stable product, while the fast fermentation obtained by treating the olives with lye solution resulted in pH values that were too high. The sugar content in all table olives was almost null, and the sensory evaluation showed that SS olives were less bitter than NF olives. Moreover, NF olive-flesh samples showed a higher amount of healthy phenolic compounds than SS olives, whose phenolic content was drastically affected by the alkaline treatment and the successive washing steps. Full article

Acrylamide, a compound identified as a probable carcinogen, is generated during the sterilization phase employed during the processing of Californian-style green ripe olives. It is possible to reduce the content of this toxic compound by applying different strategies during the processing of green ripe olives. The influence of different processing conditions on acrylamide content was studied in three olives varieties (“Manzanilla de Sevilla”, “Hojiblanca”, and “Manzanilla Cacereña”). Olives harvested during the yellow–green stage presented higher acrylamide concentrations than green olives. A significant reduction in acrylamide content was observed when olives were washed with water at 25 °C for 45 min (25% reduction) and for 2 h (45% reduction) prior to lye treatment. Stone olives had 21–26% higher acrylamide levels than pitted olives and 42–50% higher levels than sliced olives in the three studied varieties. When calcium chloride (CaCl₂) was added to the brine and brine sodium chloride (NaCl) increased from 2% to 4%, olives presented higher concentrations of this contaminant. The addition of additives did not affect acrylamide levels when olives were canned without brine. Results from this study are very useful for the table olive industry to identify critical points in the production of Californian-style green ripe olives, thus, helping to control acrylamide formation in this foodstuff. Full article

Olive pitting, slicing and stuffing machines (DRR in Spanish) are characterized by the fact that their optimal functioning is based on appropriate adjustments. Traditional systems are not completely reliable because their minimum error rate is 1–2%, which can result in fruit loss, since the pitting process is not infallible, and food safety issues can arise. Such minimum errors are impossible to remove through mechanical adjustments. In order to achieve this objective, an innovative solution must be provided in order to remove errors at operating speed rates over 2500 olives/min. This work analyzes the appropriate placement of olives in the pockets of the feed chain by using the following items: (1) An IoT System to control the DRR machine and the data analysis. (2) A computer vision system with an external shot camera and a LED lighting system, which takes a picture of every pocket passing in front of the camera. (3) A chip with a neural network for classification that, once trained, classifies between four possible pocket cases: empty, normal, incorrectly de-stoned olives at any angles (also known as a “boat”), and an anomalous case (foreign elements such as leafs, small branches or stones, two olives or small parts of olives in the same pocket). The main objective of this paper is to illustrate how with the use of a system based on IoT and a physical chip (NeuroMem CM1K, General Vision Inc.) with neural networks for sorting purposes, it is possible to optimize the functionality of this type of machine by remotely analyzing the data obtained. The use of classifying hardware allows it to work at the nominal operating speed for these machines. This would be limited if other classifying techniques based on software were used. Full article