Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2020Scopus SJRQ1Portable infrared sensing technology for phenotyping chemical traits in fresh market tomatoes
Akpolat, Hacer; Barineau, Mark; Jackson, Keith A.; Aykas, Didem P.; Rodriguez-Saona, Luis E.
Our objective was to develop predictive regression algorithms based on infrared spectroscopy to screen for selected quality traits directed at optimizing the selection capabilities of fresh market tomatoes. Fresh tomato (681) samples were harvested from multiple locations (Florida, Virginia, California and South Carolina) during the 2016 and 2018 seasons at various ripening stages. Spectra were collected by transmittance and attenuated total reflectance (ATR) either from the tomato surface or juice. Reference methods included soluble solid content, titratable acidity, sugars, organic acids, and lycopene. Partial least squares regression using surface spectra showed good correlation for lycopene and ascorbic acid (rcv>0.9) but modest correlation coefficients (rcv 0.54–0.80) for all other traits, while juice spectra gave high correlation coefficients (rcv>0.94) and excellent predictive performance (RPD range 3–10) for all quality traits except ascorbic acid (rcv>0.79). Multiple quality traits were simultaneously determined by using a single drop of sample providing fast (<1 min) measurements and minimal sample preparation based on unique spectral fingerprints.
AtıfScopus17WoS15OpenAlex19
İncele - MakaleAçık Erişim2016Scopus SJRQ1A New Energy Prediction Algorithm for Energy-Harvesting Wireless Sensor Networks With Q-Learning
Kosunalp, Selahattin
Traditional wireless sensor networks (WSNs) face the problem of a limited-energy source, typically batteries, resulting in the need for careful and effective utilization of the energy source. However, inevitable energy depletion will eventually disturb the operation of a WSN. Energy harvesting (EH) technology is acquiring particular interest, because it has the potential to provide a continuous energy supply in battery-powered WSNs. Solar energy is the most effective environmental energy for EH-WSNs because of its high energy intensity, which comes from a non-controllable source. Therefore, the prediction of future energy availability is a critical issue, as the amount of the harvestable energy may vary over time. In this paper, a novel solar energy prediction algorithm with Q-learning, called Q-learning-based solar energy prediction (QL-SEP), is proposed. Q-learning is an effective way of predicting future actions based on past observations. The distinctive feature of QL-SEP is that not only past days' observations but also the current weather conditions are considered for prediction. The performance of QL-SEP is simulated in this paper using real-world measurements obtained from a solar panel in comparison with the state-of-art approaches.
AtıfScopus128WoS96OpenAlex135İncele