Ultrasound as pre-treatment for microwave drying of Myrtus communis fruits: Influence on phenolic compounds and antioxidant activity

Background: Drying constitutes the most common method of food preservation that may degrade nutrients compounds in fruits due to high temperatures and long drying times. Myrtus communis is one of the important aromatic and medicinal species, owing to these reasons, the development of new methods of drying is essential for the preservation and valorization of myrtle fruits. Aims: The aim of the present study was to investigate the effect of ultrasound as a pre-treatment (USP) at 10 min to 90 min in microwave-drying (MD) on the dehydration of myrtle Myrtus communis fruits, on phytochemical content, and on antioxidant activity. Methods: ultrasound drying as pretreatment in microwave drying, extraction yield efficiency and antioxidant activity were measured using radical scavenging assay (DPPH•) and reducing power in addition the PCA analysis was investigated to detect the relationships between variables. Results: The ultrasound pretreatment reduced notably the microwave drying time. A pretreatment of 90 min provided the most rapid drying kinetics (6 min and 5.5 min for 500 w and 700 w respectively) compared to the microwave drying alone (18 min and 11 min for 500 w and 700 w respectively). A higher phytochemical content; 219.90 ± 0.69 mg GAE/g for total phenol content (TPC) was obtained compared to those from MD and conventional drying (CD); 193.79 ± 0.99 mg GAE/g and 148.16 ± 0.95 mg GAE/g for TPC respectively. Indeed, the antioxidant activity tests revealed that ultrasound pretreatment is one of the most efficient methods to preserve the quality and the hydrogen and/or electron-donating ability of antioxidants for neutralizing DPPH radicals (98.63 %) test and reducing ferric ions to ferrous ones. Effectively, the results of PCA analysis show a higher positive correlation between antioxidant activity and flavonoids, anthocyanins, and tannins contents. Conclusions: Ultrasound pretreatment is expected to be a potential alternative to preserve fruit quality during microwave drying because it can reduce drying time at ambient temperatures while preserving natural heat-sensitive nutritive components, flavor, and color

Ziziphus lotus (L.) Lam. plant treatment by ultrasounds and microwaves to improve antioxidants yield and quality: An overview

The purpose of this review is to compile the literature published about different aspects of microwave-assisted extraction (MAE) use and ultrasound-assisted extraction (UAE) applied on jujube worldwide and to compare the results on the antioxidant activity obtained for each extraction method. As a result of the increased consumers demand for natural products, as well as for those of agro-food, nutraceutical, cosmetic industries, and green extraction techniques are nowadays trending to be potential alternatives that can improve antioxidant yield and its quality from an economical and environmental point of view by reducing time, energy, and solvent consumption. Ultrasounds and microwaves are widely used methods in the extraction of active principles due to their cavitation and dipolar rotation effect, respectively. These two techniques provide efficiency of extraction while minimizing the time and preserving the quality of the food matrix, overcoming the disadvantages of conventional techniques characterized by their consumption of large quantities of solvents and providing a sparse quantity of extraction. Jujube, a shrub with a high antioxidant potential, which can be affected by various extraction conditions can be the target of UAE and MAE to increase the antioxidant extraction yield. Exploiting the beneficial properties such as the antioxidant activity can lead to an industrialization process, replacing therefor synthetic antioxidants with natural compounds. These can also help in the development of new nutraceuticals and can be used, for instance, in agro-food industries as preservatives

Performance indicators and analytic hierarchy process to evaluate water supply services management in Algeria

The provision of an efficient water supply service (WSS) is crucial for the well-being of citizens and the sustainability of cities. This study aims to evaluate the performance of WSS using the results of a household survey and the ranking of performance indicators (PIs) by the analytic hierarchy process method. The methodology developed was tested for the case of the city of Taoura (Algeria). A survey was carried out among 340 residents of the city. The survey results showed that the majority of respondents (70%) were relatively dissatisfied with the quantity of water provided and 67% of households surveyed rated the quality of service as poor. Then, the performance was evaluated according to 5 decision criteria and 20 PIs. The results of the evaluation of the relative weights of the criteria are as follows: the ‘Financial and economic’ criterion plays the most important role, with a relative weight of 38.61%, followed by the ‘Operational’ criterion (24.7%) and the criterion ‘Physics’ (17.32%). The methodology used in this study can be a reliable tool for evaluating the performance of WSS in developing countries. HIGHLIGHTS Development of a methodological framework to identify and classify performance indicators.; Water supply services are facing issues in achieving sustainability objectives.; Five criteria and 20 performance indicators were identified for assessment.; Weights were generated using the analytic hierarchy process.; Calculate the benefits for households and water companies with the new water tariff estimated by the willingness to pay (WTP).

Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach

The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are critical to efficiently managing their integration in smart grids, delivery, and storage. This paper intends to provide efficient short-term forecasting of solar power production using Variational AutoEncoder (VAE) model. Adopting the VAE-driven deep learning model is expected to improve forecasting accuracy because of its suitable performance in time-series modeling and flexible nonlinear approximation. Both single- and multi-step-ahead forecasts are investigated in this work. Data from two grid-connected plants (a 243 kW parking lot canopy array in the US and a 9 MW PV system in Algeria) are employed to show the investigated deep learning models’ performance. Specifically, the forecasting outputs of the proposed VAE-based forecasting method have been compared with seven deep learning methods, namely recurrent neural network, Long short-term memory (LSTM), Bidirectional LSTM, Convolutional LSTM network, Gated recurrent units, stacked autoencoder, and restricted Boltzmann machine, and two commonly used machine learning methods, namely logistic regression and support vector regression. The results of this investigation demonstrate the satisfying performance of deep learning techniques to forecast solar power and point out that the VAE consistently performed better than the other methods. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models

Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems

Over the past few years, there has been a significant increase in the interest in and adoption of solar energy all over the world. However, despite ongoing efforts to protect photovoltaic (PV) plants, they are continuously exposed to numerous anomalies. If not detected accurately and in a timely manner, anomalies in PV plants may degrade the desired performance and result in severe consequences. Hence, developing effective and flexible methods capable of early detection of anomalies in PV plants is essential for enhancing their management. This paper proposes flexible data-driven techniques to accurately detect anomalies in the DC side of the PV plants. Essentially, this approach amalgamates the desirable characteristics of ensemble learning approaches (i.e., the boosting (BS) and bagging (BG)) and the sensitivity of the Double Exponentially Weighted Moving Average (DEWMA) chart. Here, we employ ensemble learning techniques to exploit their capability to enhance the modeling accuracy and the sensitivity of the DEWMA monitoring chart to uncover potential anomalies. In the ensemble models, the values of parameters are selected with the assistance of the Bayesian optimization algorithm. Here, BS and BG are adopted to obtain residuals, which are then monitored by the DEWMA chart. Kernel density estimation is utilized to define the decision thresholds of the proposed ensemble learning-based charts. The proposed monitoring schemes are illustrated via actual measurements from a 9.54 kW PV plant. Results showed the superior detection performance of the BS and BG-based DEWMA charts with non-parametric threshold in uncovering different types of anomalies, including circuit breaker faults, inverter disconnections, and short-circuit faults. In addition, the performance of the proposed schemes is compared to that of BG and BS-based DEWMA and EWMA charts with parametric thresholds

Antioxidant properties of 3-deoxyanthocyanidins and polyphenolic extracts from Côte d’Ivoire’s red and white sorghums assessed by ORAC and in vitro LDL oxidisability tests

International audienceRed sorghum is a source of phenolic compounds (PCs), including 3-deoxyanthocyanidins that may protect against oxidative stress related disease such as atherosclerosis. HPLC was used to characterise and quantify PCs extracted from red or white sorghum whole grain flour. Antioxidant activity was measured by an oxygen radical absorbance capacity assay and against LDL-oxidisability, and further compared to that of synthesised 3-deoxyanthocyanidins (i.e., luteolinidin and apigeninidin). Phenolic content of red and white sorghums was evaluated as 3.90 ± 0.01 and 0.07 ± 0.01 mmol gallic acid equivalents L- 1, respectively. Luteolinidin and apigeninidin were mainly found in red sorghum. Red sorghum had almost 3 and 10 times greater specific antioxidant activity compared to luteolinidin and apigeninidin, respectively. Red sorghum PCs and the two 3-deoxyanthocyanidins were also effective at preventing LDL vitamin E depletion and conjugated diene production. Red sorghum flour exhibits antioxidant capacity suggesting that it may be a valuable health-promoting food

Antioxidant effects of extra virgin olive oil enriched by myrtle phenolic extracts on iron-mediated lipid peroxidation under intestinal conditions model

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Real-Time Analysis of Polyphenol-Protein Interactions by Surface Plasmon Resonance Using Surface-Bound Polyphenols

International audienceA selection of bioactive polyphenols of different structural classes, such as the ellagitannins vescalagin and vescalin, the flavanoids catechin, epicatechin, epigallocatechin gallate (EGCG), and procyanidin B2, and the stilbenoids resveratrol and piceatannol, were chemically modified to bear a biotin unit for enabling their immobilization on streptavidin-coated sensor chips. These sensor chips were used to evaluate in real time by surface plasmon resonance (SPR) the interactions of three different surface-bound polyphenolic ligands per sensor chip with various protein analytes, including human DNA topoisomerase IIα, flavonoid leucoanthocyanidin dioxygenase, B-cell lymphoma 2 apoptosis regulator protein, and bovine serum albumin. The types and levels of SPR responses unveiled major differences in the association, or lack thereof, and dissociation between a given protein analyte and different polyphenolic ligands. Thus, this multi-analysis SPR technique is a valuable methodology to rapidly screen and qualitatively compare various polyphenol-protein interactions