José Miguel Madueño Luna’s research while affiliated with University of Seville and other places

This study focuses on characterizing the temporal evolution of the surface affected by industrial treatment with NaOH within the processing tanks during the lye treatment stage of Manzanilla table olives. The lye treatment process is affected by multiple variables, such as ambient temperature, the initial temperature of the olives before lye treatment, the temperature of the NaOH solution, the concentration of the solution, the variety of olives, and their size, which are determinants of the speed of the lye treatment process. Traditionally, an expert, relaying on their subjective judgement, manages the cooking process empirically, leading to variability in the termination timing of the cook. In this study, we introduce a system that, by using an artificial vision system, allows us to know in a deterministic way the percentage of lye treatment achieved at each moment along the cooking process; furthermore, with an interpolator that accumulates values during the lye treatment, it is possible to anticipate the completion of the cooking by indicating the moment when two-thirds, three-fourths, or some other value of the interior surface will be reached with an error of less than 10% relative to the optimal moment. Knowing this moment is crucial for proper processing, as it will affect subsequent stages of the manufacturing process and the quality of the final product.

Iapetus, a Saturn moon, shows the most differentiated albedo dichotomy of the Solar System. The dark leading side has a lower albedo than the bright trailing side. Spectral data on the visible light reveal the existence of two types of materials on the surface. The darkening in the leading side is thought to be originated by the presence of organic material and carbonaceous compounds on surface, while the trailing side is covered by water ice due to migration processes from the dark side. On airless bodies like Iapetus, the surface escape speed is greater than the speed of water molecules, resulting in the retention of a H2O atmosphere that allows some species to get diffused through it. Here, there were performed simulations of the evolution of the surface temperature for each of the two hemispheres of Iapetus. The results showed a slow yet steady increment of temperatures for both sides, with a steeper slope for the dark hemisphere. It was also simulated how much energy budget can be accumulated in both sides and its consequences. Finally, we calculated the diffusion coefficients for ammonia, methane, and water ice. The results let us infer how these compounds could evolve over time.

Many drylands around the world have seen both soil and vegetation degradation around watering points. It can be seen in spaceborne imagery as radial brightness belts that fade with distance from the water areas. The study's primary goal was to characterize spatio-temporal land degradation/rehabilitation in the drylands of the southeast Iberian Peninsula. The brightness index of Tasseled Cap was discovered to be the best spectral transformation for enhancing the contrast between the bright-degraded areas near the points and the darker surrounding areas far from and in-between these areas. To comprehend the spatial structure present in spaceborne imagery of two desert sites and three key time periods, semi-variograms were created (mid-late 2000s, around 2015, and 2020). In order to assess spatio-temporal land-cover patterns, a geostatistical model (kriging) was used to smooth brightness index values extracted from 30 m spatial resolution images. To assess the direction and intensity of changes between study periods, a change detection analysis based on kriging prediction maps was performed. These findings were linked to the socioeconomic situation prior to and following the EU economic crisis. The study discovered that degradation occurred in some areas as a result of the region's agricultural activities being exploited.

The study of electrical impedance applied to food has become a method with great potential for use in the food industry, which allows the monitoring and control of quality processes in a safe and non-invasive way. Recent research has shown that this technique can be an alternative method to determine the floral origin of the honey bee (Apis mellifera L.) and acquire information on chemical and physical properties such as conductivity, ash content and acidity. In this work, the electrical impedance of six monofloral honey samples from diverse origins and one commercial multi-floral honey were measured using a low-cost impedance meter, obtaining 101 samples (reactance (X) versus resistance (R)), with a frequency sweep between 1 Hz and 25 MHz in all the honeys analyzed. This shows that it is possible, by using a multilayer neural network trained from these data, to classify with 100% accuracy between these honeys and, thereby, quickly and easily determine the floral origin of the honey. This is without the need to use the chemical data or equivalent electrical models.