Assessment of Tunisian virgin olive oils via synchronized analysis of sterols, phenolic acids, and fatty acids in combination with multivariate chemometrics

Combining different biomarkers to distinguish Chemlali virgin olive oils from different geographical areas of Tunisia

BACKGROUND

Despite their high potential, Tunisian virgin olive oils (VOOs) are mainly exported in bulk or blended with VOOs of other origin, hindering their international market placement. To face this situation, their valorization is needed by highlighting their unique features and by developing tools to guarantee their geographical authenticity. Compositional features of Chemlali VOOs produced in three Tunisian regions were assessed to identify suitable authenticity markers.

RESULTS

Quality indices ensured the quality of the VOOs studied. Volatile compounds, total phenols, fatty acid (FA) and chlorophylls are significantly influenced by the region of origin, which was justified by the differences found in soil and climatic conditions of the three geographical regions. To explore the capabilities of these markers for the geographical authentication of Tunisian Chemlali VOOs, classification models based on partial least squares-discriminant analysis (PLS-DA) were developed by grouping the minimum number of variables allowing the highest discrimination power, minimizing in this way the analytical procedure. The PLS-DA authentication model based on combining volatile compounds with FA or with total phenols achieved a correct classification of 95.7% of the VOOs according to their origin, as assessed by 10%-out cross-validation. Sidi Bouzid Chemlali VOOs achieved 100% of correct classification, while the misclassification between Sfax and Enfidha ones did not exceed 10%.

CONCLUSIONS

These results allowed to establish the most promising and affordable combination of markers for the geographical authentication of Tunisian Chemlali VOOs from distinct production regions and provide the basis to further develop authentication models based on wider datasets. © 2023 Society of Chemical Industry.

Detection of Urban and Environmental Changes via Remote Sensing

Rapid climate and environmental change at limited, regional, and general scales have been a major concern for researchers in a number of fields, such as topography, economy, environment, and sustainable development. Changes in land cover and land use have taken into account due to potential impacts on soil depletion, amplified run-off, water balance, and climate change. A detailed understanding of the characteristics of land exploitation and land structure is indispensable for the study of their influences on life and nature. In addition, urban extension is a major form of land extraction and land transformation, as it relates to the rise in population and the availability of financial services. Remote sensing records have been shown to be important for reporting and perceiving urban development and transition, and for providing critical information for future growth. Transformation and shift identification are the tools used to recognise distinctions in a land cover by tracking them at various times. In addition, various change identification and detection approaches are routinely tested with the goal of providing the greatest change detection deductions for a particular appliance. This review would aim to establish a practical plan that combines remote sensing techniques, on the one hand, and modelling approaches, on the other, to track land use, to cover changes, and to predict future trends.

Usage of Artificial Intelligence and Remote Sensing as Efficient Devices to Increase Agricultural System Yields

Artificial Intelligence is an emerging technology in the field of agriculture. Artificial Intelligence-based tools and equipment have actually taken the agriculture sector to a different level. This new technology has improved crop production and enhanced instantaneous monitoring, processing, and collection. The most recent computerized structures using remote sensing and drones have made a significant contribution to the agro-based domain. Moreover, remote sensing has the capability to support the development of farming applications with the aim of facing this main defy, via giving cyclic records on yield status during studied periods at diverse degrees and for diverse parameters. Various hi-tech, computer-supported structures are created to determine different central factors such as plant detection, yield recognition, crop quality, and several other methods. This paper includes the techniques employed for the analysis of collected information in order to enhance the productivity, forecast eventual threats, and reduce the task load on cultivators.