Application of Digital Olfaction for Table Olive Industry

Burn Defect and Phenol Prediction for Flavoured Californian-Style Black Olives Using Digital Sensors

Californian-style black olives can undergo different chemical changes during the sterilization process that can affect their sensory and phenol characteristics. Thus, these olives were stuffed with flavoured hydrocolloids and submitted to different thermal sterilization treatments to assess sensory categories. The triangular test indicated that the panellists were able to discriminate between samples from different categories according to their aromas with more than 85% success. The results indicated that the negative aroma detected by tasters was related to burn defects. The highest level of defects was found in standard olives, while the lowest was identified in the extra category. Furthermore, olives submitted to the lowest thermal sterilization treatment (extra) presented significantly higher phenol profile content, such as for hydroxytyrosol, tyrosol, oleuropein and procyanidin B1. The electronic nose (E-nose) discriminated between samples from different categories according to the specific aroma (PC1 = 82.1% and PC2 = 15.1%). The PLS-DA classified the samples with 90.9% accuracy. Furthermore, the volatile organic compounds responsible for this discrimination were creosol, copaene, benzaldehyde and diallyl disulphide. Finally, the models established by the PLS analysis indicated that the E-nose could predict olives according to their aroma and total phenol profile (RCV2 values were 0.89 and 0.92, respectively). Thus, this device could be used at the industrial level to discriminate between olives with different sensory aromas to determine those with the highest quality.

Use of Different Nutrients to Improve the Fermentation Performances of Lactiplantibacillus pentosus OM13 during the Production of Sevillian Style Green Table Olives

The aim of this study was to evaluate the fermentation performance of the commercial starter Lactiplantibacillus pentosus OM13 with four nutrients (A, B, C, and D) that differed in the following ingredients: starch, sugars, maltodextrin, inactivated yeast, inactivated yeast rich in amino acids, inactivated yeast rich in mannoproteins, and salt (NaCl). For this purpose, six different experimental productions of Nocellara del Belice table olives were carried out. During transformation, the fermentation process was monitored by measuring pH and plate counts for lactic acid bacteria (LAB), yeasts, Enterobacteriaceae, Staphylococcaceae, and Pseudodomondaceae populations. At the end of the production process, each trial was subjected to volatile organic compound analysis and sensory evaluation. The addition of the different nutrients resulted in a significant reduction in pH (around 2.5 points) after 3 days of fermentation. At the same time, a significant increase in the number of LAB populations (> 6.6 log CFU/mL) was observed for all trials. Volatile organic compound (VOC) analysis revealed the presence of 39 compounds. In this study, nutrient C was optimal for improving the fermentation activity of L. pentosus OM13. These results provide elements for the implementation of experimental protocols to reduce product losses and improve sensory characteristics.

RustOnt: An Ontology to Explain Weather Favorable Conditions of the Coffee Rust

Crop disease management in smart agriculture involves applying and using new technologies to reduce the impact of diseases on the quality of products. Coffee rust is a disease that factors such as poor agronomic management activities and climate conditions may favor. Therefore, it is crucial to identify the relationships between these factors and this disease to learn how to face its consequences and build intelligent systems to provide appropriate management or help farmers and experts make decisions accordingly. Nevertheless, there are no studies in the literature that propose ontologies to model these factors and coffee rust. This paper presents a new ontology called RustOnt to help experts more accurately model data, expressions, and samples related to coffee rust and apply it whilst taking into account the geographical location where the ontology is adopted. Consequently, this ontology is crucial for coffee rust monitoring and management by means of smart agriculture systems. RustOnt was successfully evaluated considering quality criteria such as clarity, consistency, modularity, and competence against a set of initial requirements for which it was built.