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Measuring lycopene in tomatoes is fundamental to the agrifood industry because of its health benefits. It is one of the leading quality criteria for consuming this fruit. Traditionally, the amount determination of this carotenoid is performed using the high-performance liquid chromatography (HPLC) technique. This is a very reliable and accurate method, but it has several disadvantages, such as long analysis time, high cost, and destruction of the sample. In this sense, this work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp. A Raspberry Pi 4 programmed with Python language was used to develop the lycopene prediction model. Various regression models were evaluated using neural networks, fuzzy logic, and linear regression. The best model was the fuzzy nonlinear regression as the RGB input, with a correlation of R 2 = 0.99 and a mean error of 1.9 × 10 -5 . This work was able to demonstrate that it is possible to determine the lycopene content using a digital camera and a low-cost integrated system in a non-invasive way.

The biomass of microalgae and the compounds that can be obtained from their processing are of great interest for various economic sectors. Chlorophyll from green microalgae has biotechnological applications of great potential in different industrial areas such as food, animal feed, pharmaceuticals, cosmetics, and agriculture. In this paper, the experimental, technical and economic performance of biomass production from a microalgal consortium ( Scenedesmus sp., Chlorella sp., Schroderia sp., Spirulina sp., Pediastrum sp., and Chlamydomonas sp.) was investigated in three cultivation systems (phototrophic, heterotrophic and mixotrophic) in combination with the extraction of chlorophyll ( a and b ) on a large scale using simulation; 1 ha was established as the area for cultivation. In the laboratory-scale experimental stage, biomass and chlorophyll concentrations were determined for 12 days. In the simulation stage, two retention times in the photobioreactor were considered, which generated six case studies for the culture stage. Subsequently, a simulation proposal for the chlorophyll extraction process was evaluated. The highest microalgae biomass concentration was 2.06 g/L in heterotrophic culture, followed by mixotrophic (1.98 g/L). Phototrophic and mixotrophic cultures showed the highest chlorophyll concentrations of 20.5 µg/mL and 13.5 µg/mL, respectively. The simulation shows that higher biomass and chlorophyll production is attained when using the mixotrophic culture with 72 h of retention that we considered to evaluate chlorophyll production ( a and b ). The operating cost of the entire process is very high; the cultivation stage has the highest operating cost (78%), mainly due to the high energy consumption of the photobioreactors.

A comparative single-evaluation cross-sectional study was performed to evaluate cognitive damage in post-COVID-19 patients. The psychophysics tests of Two-Alternative Forced Choice (2AFC) and Simple Reaction Time (SRT), under a designed virtual environment, were used to evaluate the cognitive processes of decision-making, visual attention, and information processing speed. The population under study consisted of 147 individuals, 38 controls, and 109 post-COVID patients. During the 2AFC test, an Emotiv EPOC+® headset was used to obtain EEG signals to evaluate their Focus, Interest, and Engagement metrics. Results indicate that compared to healthy patients or recovered patients from mild-moderate COVID-19 infection, patients who recovered from a severe-critical COVID infection showed a poor performance in different cognitive tests: decision-making tasks required higher visual sensitivity (p = 0.002), Focus (p = 0.01) and information processing speed (p < 0.001). These results signal that the damage caused by the coronavirus on the central nervous and visual systems significantly reduces the cognitive processes capabilities, resulting in a prevalent deficit of 42.42% in information processing speed for mild-moderate cases, 46.15% for decision-making based on visual sensitivity, and 62.16% in information processing speed for severe-critical cases. A psychological follow-up for patients recovering from COVID-19 is recommended based on our findings.