Freeze-Damage Detection in Lemons Using Electrochemical Impedance Spectroscopy.

Lemon is the most sensitive citrus fruit to cold. Therefore, it is of capital importance to detect and avoid temperatures that could damage the fruit both when it is still in the tree and in its subsequent commercialization. In order to rapidly identify frost damage in this fruit, a system based on the electrochemical impedance spectroscopy technique (EIS) was used. This system consists of a signal generator device associated with a personal computer (PC) to control the system and a double-needle stainless steel electrode. Tests with a set of fruits both natural and subsequently frozen-thawed allowed us to differentiate the behavior of the impedance value depending on whether the sample had been previously frozen or not by means of a single principal components analysis (PCA) and a partial least squares discriminant analysis (PLS-DA). Artificial neural networks (ANNs) were used to generate a prediction model able to identify the damaged fruits just 24 hours after the cold phenomenon occurred, with sufficient robustness and reliability (CCR = 100%).

Early Detection of Freeze Damage in Navelate Oranges with Electrochemical Impedance Spectroscopy.

The early detection of freeze damage in Navelate oranges (Citrus sinensis L. Osbeck) was studied using electrochemical impedance spectroscopy (EIS), which is associated with a specific double-needle sensor. The objective was to identify this problem early in order to help to determine when a freeze phenomenon occurs. Thus, we selected a set of Navelate oranges without external defects, belonging to the same batch. Next, an intense cold process was simulated to analyze the oranges before and after freezing. The results of the spectroscopy analysis revealed different signals for oranges depending on whether they had experienced freezing or not. Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) of the obtained data demonstrated that it is possible to discriminate the samples, explaining 88.5% of the total variability (PCA) and being able to design a mathematical model with a prediction sensitivity of 80% (PLS-DA). Additionally, a designed artificial neural network (ANN) prediction model managed to correctly classify 100% of the studied samples. Therefore, EIS together with ANN-based data treatment is proposed as a viable alternative to the traditional techniques for the early detection of freeze damage in oranges.

Finite Element Models of Gold Nanoparticles and Their Suspensions for Photothermal Effect Calculation.

(1) Background: The ability of metal nanoparticles to carry other molecules and their electromagnetic interactions can be used for localized drug release or to heat malignant tissue, as in the case of photothermal treatments. Plasmonics can be used to calculate their absorption and electric field enhancement, which can be further used to predict the outcome of photothermal experiments. In this study, we model the nanoparticle geometry in a Finite Element Model calculus environment to calculate the effects that occur as a response to placing it in an optical, electromagnetic field, and also a model of the experimental procedure to measure the temperature rise while irradiating a suspension of nanoparticles. (2) Methods: Finite Element Method numerical models using the COMSOL interface for geometry and mesh generation and iterative solving discretized Maxwell's equations; (3) Results: Absorption and scattering cross-section spectrums were obtained for NanoRods and NanoStars, also varying their geometry as a parameter, along with electric field enhancement in their surroundings; temperature curves were calculated and measured as an outcome of the irradiation of different concentration suspensions; (4) Conclusions: The results obtained are comparable with the bibliography and experimental measurements.

Tailored cancer therapy by magnetic nanoparticle hyperthermia: A virtual scenario simulation method.

BACKGROUND AND OBJECTIVE: Hyperthermia is a cancer treatment aiming to induce cell death by directly warming cancerous tissues above 40 °C. This technique can be applied both individually and together with other cancer therapies. The main challenge for researchers and medics is to heat only tumoral cells avoiding global or localized heating of sane tissues. The objective in this study is to provide a realistic virtual scenario to develop an optimized multi-site injection plan for tailored magnetic nanoparticle-mediated hyperthermia applications. METHODS: A three-dimensional model of a cat's back was tested in three different simulation scenarios, showing the impact of magnetic nanoparticles in each specific environment configuration. RESULTS: As a result of this study. This simulation method can, minimising the affection to healthy tissue. CONCLUSIONS: This virtual method will help real and personalized therapy planning and tailor the dose and distribution of magnetic nanoparticles for an enhanced hyperthermia cancer treatment.