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Alpha-phellandrene is a very common cyclic monoterpene found in several EOs, which shows extensive biological activities. Therefore, the main focus of the present systematic review was to provide a comprehensive and critical analysis of the state of the art regarding its biological activities and pharmaceutical and food applications. In addition, the study identified essential oils rich in alpha-phellandrene and summarized their main biological activities as a preliminary screening to encourage subsequent studies on their single components. With this review, we selected and critically analyzed 99 papers, using the following bibliographic databases: PubMed, SciELO, Wiley and WOS, on 8 July, 2022. Data were independently extracted by four authors of this work, selecting those studies which reported the keyword “alpha-phellandrene” in the title and/or the abstract, and avoiding those in which there was not a clear correlation between the molecule and its biological activities and/or a specific concentration from its source. Duplication data were removed in the final article. Many essential oils have significant amounts of alpha-phellandrene, and the species Anethum graveolens and Foeniculum vulgare are frequently cited. Some studies on the above-mentioned species show high alpha-phellandrene amounts up to 82.1%. There were 12 studies on alpha-phellandrene as a pure molecule showed promising biological functions, including antitumoral, antinociceptive, larvicidal and insecticidal activities. There were 87 research works on EOs rich in alpha-phellandrene, which were summarized with a focus on additional data concerning potential biological activities. We believe this data is a useful starting point to start new research on the pure molecule, and, in particular, to distinguish between the synergistic effects of the different components of the OEs and those due to alpha-phellandrene itself. Toxicological data are still lacking, requiring further investigation on the threshold values to distinguish the boundary between beneficial and toxic effects, i.e., mutagenic, carcinogenic and allergenic. All these findings offer inspiration for potential applications of alpha-phellandrene as a new biopesticide, antimicrobial and antitumoral agent. In particular, we believe our work is of interest as a starting point for further studies on the food application of alpha-phellandrene.

Coumarins are a group of phytochemicals that may be beneficial or harmful to health depending on their type and dosage and the matrix that contains them. Some of these compounds have been proven to display pro-oxidant and clastogenic activities. Therefore, in the current work, we have studied the coumarins that are present in food sources extracted from the Phenol-Explorer database in order to predict their clastogenic activity and identify the structure-activity relationships and genotoxic structural alerts using alternative methods in the field of computational toxicology. It was necessary to compile information on the type and amount of coumarins in different food sources through the analysis of databases of food composition available online. A virtual screening using a clastogenic model and different software, such as MODESLAB, ChemDraw and STATISTIC, was performed. As a result, a table of food composition was prepared and qualitative information from this data was extracted. The virtual screening showed that the esterified substituents inactivate molecules, while the methoxyl and hydroxyl substituents contribute to their activity and constitute, together with the basic structures of the studied subclasses, clastogenic structural alerts. Chemical subclasses of simple coumarins and furocoumarins were classified as active (xanthotoxin, isopimpinellin, esculin, scopoletin, scopolin and bergapten). In silico genotoxicity was mainly predicted for coumarins found in beer, sherry, dried parsley, fresh parsley and raw celery stalks. The results obtained can be interesting for the future design of functional foods and dietary supplements. These studies constitute a reference for the genotoxic chemoinformatic analysis of bioactive compounds present in databases of food composition.

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Problems of variability in the design of equipment, the availability of equipment, the changes in the environment and in future changes which have been extensively investigated in order to determine the chances of success in environmental, technological and economic matters. This research considers the variability of the operational parameters in the quality of the final product, as an element in the process design, this approach is not an usual activity. Some previous studies on the production of sugarcane syrup were related to sensory attributes (viscosity, flavor and presence of crystals) with operational parameters (pH and °Brix). It was generated the pH and °Brix using different probability distributions and the results were plotted by control charts. It was determined the influence of the variability of the °Brix in the sensorial acceptance of the final product.

Increasing interest in constituents and dietary supplements has created the need for more efficient use of this information in nutrition-related fields. The present work aims to obtain optimal models to predict the total antioxidant properties of food matrices, using available information on the amount and class of flavonoids present in vegetables. A new dataset using databases that collect the flavonoid content of selected foods has been created. Structural information was obtained using a structural-topological approach called TOPological Sub-Structural Molecular (TOPSMODE). Different artificial intelligence algorithms were applied, including Machine Learning (ML) methods. The study allowed us to demonstrate the effectiveness of the models using structural-topological characteristics of dietary flavonoids. The proposed models can be considered, without overfitting, effective in predicting new values of Oxygen Radical Absorption capacity (ORAC), except in the Multi-Layer Perceptron (MLP) algorithm. The best optimal model was obtained by the Random Forest (RF) algorithm. The in silico methodology we developed allows us to confirm the effectiveness of the obtained models, by introducing the new structural-topological attributes, as well as selecting those that most influence the class variable.