Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in Olea europaea Leaves.

The study of the phenolic compounds present in olive leaves (Olea europaea) is of great interest due to their health benefits. In this research, different machine learning algorithms such as RF, SVM, and ANN, with temperature, time, and volume as input variables, were developed to model the extract yield and the total phenolic content (TPC) from experimental data reported in the literature. In terms of extract yield, the neural network-based ANNZ-L model presents the lowest root mean square error (RMSE) value in the validation phase (9.44 mg/g DL), which corresponds with a mean absolute percentage error (MAPE) of 3.7%. On the other hand, the best model to determine the TPC value was the neural network-based model ANNR, with an RMSE of 0.89 mg GAE/g DL in the validation phase (MAPE of 2.9%). Both models obtain, for the test phase, MAPE values of 4.9 and 3.5%, respectively. This affirms that ANN models would be good modelling tools to determine the extract yield and TPC value of the ultrasound-assisted extraction (UAE) process of olive leaves under different temperatures, times, and solvents.

Machine Learning to Predict the Adsorption Capacity of Microplastics.

Nowadays, there is an extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through their own degradation processes, can contaminate ecosystems with micro- and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) were developed to predict different microplastic/water partition coefficients (log Kd) using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients above 0.92 in the query phase, which indicates that these types of models could be used for the rapid estimation of the absorption of organic contaminants on microplastics.

Benefits, toxicity and current market of cannabidiol in edibles.

The commercialization of products with cannabidiol (CBD) has undergone a significant increase. These products can be presented in different forms such as baked goods, gummies or beverages (such as kombucha, beer or teas, among others) using wide concentrations ranges. The use of CBD in edibles favors its consumption, for medicinal users, during the work week, avoid its possible social stigma and facilitates its transport. These products can be purchased on store shelves and online. There is a large number of specialized studies, in which the possible advantages of CBD consumption are described in the preclinical and clinical trials. It is also necessary to recognize the existence of other works revealing that the excessive consumption of CBD could have some repercussions on health. In this review, it is analyzed the composition and properties of Cannabis sativa L., the health benefits of cannabinoids (focusing on CBD), its consumption, its possible toxicological effects, a brief exposition of the extraction process, and a collection of different products that contain CBD in its composition.

Cyclodextrins inclusion complex: Preparation methods, analytical techniques and food industry applications.

This review offers a vision of the chemical behaviour of natural ingredients, synthetic drugs and other related compounds complexed using cyclodextrins. The review takes care of different sections related to i) the inclusion complexes formation with cyclodextrins, ii) the determination of the inclusion formation constant, iii) the most used methods to prepare host inclusion in the non-polar cavity of cyclodextrins and iv) the analytical techniques to evidence host inclusion. The review provides different literature that shows the application of cyclodextrins to improve physical, chemical, and biological characteristics of food compounds including solubility, stability and their elimination/masking. Moreover, the review also offers examples of commercial food/supplement products of cyclodextrins to indicate that cyclodextrins can be used to generate biotechnological substances with innovative properties and improve the development of food products.

Essential Oils as Antimicrobials in Crop Protection.

At present, organic crops have reached an important boom in a society increasingly interested in the conservation of the environment and sustainability. It is evident that a part of the population in the Western world focuses their concern on how to obtain our food and on doing it in a way that is as respectful as possible with the environment. In this review, we present a compilation of the work carried out with the use of essential oils as an alternative in the fight against different bacteria and fungi that attack crops and related products. Given the collected works, the efficacy of essential oils for their use as pesticides for agricultural use is evident.

Valorization of by-products from olive oil industry and added-value applications for innovative functional foods.

BACKGROUND: In the last years, the consumption of olive oil has experienced a sharp rise due to its organoleptic and healthy properties and with this the wastes and by-products derived from the olive production and the olive oil industry have also increased causing important environmental and economic issues. However, the high content in bioactive compounds of these wastes and by-products makes that its recovery is both a great challenge and an excellent opportunity for the olive oil sector. AIM OF THE REVIEW: This review encompasses the more outstanding aspects related to the advances achieved until date in the olive oil by-products valorisation and added-value applications for innovative functional foods. CONCLUSION: Taking into account the information reported in this manuscript, the development of a multiproduct biorefinery in cascade using eco-friendly technologies interchangable seems a suitable stratety to obtaining high added value compounds from olive oil by-products with applications in the field of innovative functional foods. In addition, this would allow an integral valorization of these residues enhancing the profitability of the olive oil industry. On the other hand, the biocompounds fom olive oil by-products have been described by their interesting bioactivities with beneficial properties for the consumers' health; therefore, their incorporation into the formulation of functional foods opens new possibilities in the field of innovative foods. Future perspective: Despite the studies descibed in the literature, more research on the healthy properties of the recovered compounds and their interactions with food components is key to allow their reintegration in the food chain and therefore, the removal of the olive oil by-products.

Value-Added Compound Recovery from Invasive Forest for Biofunctional Applications: Eucalyptus Species as a Case Study.

From ancient times, the medicinal properties of the different Eucalyptus species are well known. In fact, plants from this family have been used in folk medicine as antiseptics, and to treat different ailments of the upper respiratory tract such as sinus congestion, common cold, or influenza. Moreover, other biological activities were described for Eucalyptus species such as antioxidant and antimicrobial properties. In the last few decades, numerous investigations revealed that the compounds responsible for these properties are secondary metabolites that belonging to the group of phenolic compounds and are present in different parts of the plants such as leaves, bark, wood, fruits, and stumps. The increasing demand for natural compounds that can substitute synthetic antioxidants and the increase in resistance to traditional antibiotics have boosted the intense search for renewable natural sources containing substances with such bioactivities, as well as greener extraction technologies and avant-garde analytical methods for the identification of the target molecules. The literature data used in this paper were collected via Scopus (2001-2020) using the following search terms: Eucalyptus, extraction methods, phenolic compounds, and biological activities. This review collects the main studies related to the recovery of value-added compounds from different Eucalyptus species, as well as their biofunctional applications.

Pomegranate Peel as Suitable Source of High-Added Value Bioactives: Tailored Functionalized Meat Products.

In the last few years, the consumer's concern with the relationship between health and diet has led to the search of foods with functional properties beyond the nutritional. In this framework, the consumption of pomegranate has increased due to their sensorial attributes and remarkable amounts of bioactive compounds, which generate, at the same time, huge amounts of by-products. A search in the Scopus database for the last 10 years has revealed the rising interest in pomegranate peel (PP), the main residue from this fruit. The meat industry is a food sector that has had to search for new alternatives to substitute the use of synthetic preservatives by new natural additives, to extend the self-life and keep the quality attributes of their processed products. This review sets out the main bioactivities of PP extracts, and their incorporation in meat products is elaborated. PP is a good source of bioactive compounds, including phenolic acids, flavonoids and hydrolyzable tannins, which have beneficial health effects. It can be concluded that the reformulation of meat products with PP extracts is a suitable strategy for enhancing their technological characteristics, in addition to conferring functional properties that make them healthier and potentially more acceptable for the consumer.

Prediction Models to Control Aging Time in Red Wine.

A combination of physical-chemical analysis has been used to monitor the aging of red wines from D.O. Toro (Spain). The changes in the chemical composition of wines that occur over the aging time can be used to distinguish between wine samples collected after one, four, seven and ten months of aging. Different computational models were used to develop a good authenticity tool to certify wines. In this research, different models have been developed: Artificial Neural Network models (ANNs), Support Vector Machine (SVM) and Random Forest (RF) models. The results obtained for the ANN model developed with sigmoidal function in the output neuron and the RF model permit us to determine the aging time, with an average absolute percentage deviation below 1%, so it can be concluded that these two models have demonstrated their capacity to predict the age of wine.

Esters flash point prediction using artificial neural networks.

In this article, an artificial neural network to predict the flash point of 95 esters was implemented. Four variables were used for its development. A neural network with 4-5-8-5-1 topology was encountered to gain the best agreement of the experimental results with those predicted (square correlation coefficient (R(2)) and root mean square error were 0.99 and 5.46 K for the training phase and 0.96 and 13.02 K for the testing set).

The use of artificial neural networks to forecast biological atmospheric allergens or pathogens only as Alternaria spores.

The monitoring of atmospheric Alternaria spores is of major importance due to their adverse effects on crops and their role as human allergens. Most species act as plant pathogens, prompting considerable economic losses worldwide on important crops such as potato, tomato or wheat. Fungal spores can also have serious detrimental effects on human health, triggering respiratory diseases and allergenic processes. The aim of this study was not only to examine the relationship between the atmospheric Alternaria spore content and the prevailing meteorological parameters, but also to predict the atmospheric Alternaria spore content in the Northwest Spain using a novel data analysis technique, ANNs (Artificial Neural Networks). A Hirst-type LANZONI VPPS 2000 volumetric 7-day recording sampler was used to collect the airborne spores from 1997 to 2008. Neural networks provided us with a good tool for forecasting Alternaria airborne spore concentration, and thus could help the automation of the prediction system in the aerobiological information diffusion to the population suffering from allergic problems or the prevention of considerable economic worldwide losses on important crops. Our proposed model would be applied to different geographical areas; nevertheless, the adjustment of the model, by using the available and adequate variables, would be realised in each case.