Production method and varietal source influence the volatile profiles of spirits prepared from fig fruits (Ficus carica L.)

Fig fruits (Ficus carica L.) are used in several Mediterranean countries to produce alcoholic spirits, with either plurivarietal dried figs or, monovarietal fresh figs as the raw material. To determine the influence of different varietal attributes and production methods on the quality of fig spirits, we analyzed the volatile compounds that contribute to the aroma and organoleptic characteristics of spirits derived from eight Portuguese varieties of fresh figs, as well as mixtures of dried figs processed in the laboratory and plurivarietal fig spirits already in the market. The quantification of major and minor volatiles by GC-FID revealed that the plurivarietal dried fig spirits contained greater quantities of short-chain fatty acid esters and higher alcohols (associated with poor-quality spirits) and compounds with a negative influence on aroma and flavor (such as ethyl lactate, ethyl acetate and diethyl succinate) than the fresh fig spirits. HS-SPME/GC-MS analysis detected 130 volatile compounds, among which the esters ethyl decanoate, ethyl octanoate and ethyl dodecanoate, the aldehydes benzaldehyde and furfural, the monoterpene limonene and the norisoprenoide beta-damascenone were common constituents in most of the spirits. The volatile profile of all dried fig distillates was similar and diverse, reflecting the plurivarietal origin, whereas the monovarietal fresh fig spirits produced distinct profiles (sufficient for varietal chemical differentiation), with Burjassote branco distillates containing the greatest number of volatile compounds. This volatile analysis provides a way to determine the quality of fig spirits objectively and to develop spirits with novel characteristics for the market

Toxicity prediction based on artificial intelligence: A multidisciplinary overview

The use and production of chemical compounds are subjected to strong legislative pressure. Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory aspects for a multitude of industries, such as chemical, pharmaceutical, or food, due to direct harm to humans, animals, plants, or the environment. Simultaneously, there are growing demands on the authorities to replace traditional in vivo toxicity tests carried out on laboratory animals (e.g., European Union REACH/3R principles, Tox21 and ToxCast by the U.S. government, etc.) with in silica computational models. This is not only for ethical aspects, but also because of its greater economic and time efficiency, as well as more recently because of their superior reliability and robustness than in vivo tests, mainly since the entry into the scene of artificial intelligence (AI)-based models, promoting and setting the necessary requirements that these new in silico methodologies must meet. This review offers a multidisciplinary overview of the state of the art in the application of AI-based methodologies for the fulfillment of regulatory-related toxicological issues. This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning