Machine learning approach for predicting the antifungal effect of gilaburu (Viburnum opulus) fruit extracts on Fusarium spp. isolated from diseased potato tubers

A supervised machine learning approach for the prediction of antioxidant activities of Amaranthus viridis seed

This research aimed at modelling and predicting the antioxidant activities of Amaranthus viridis seed extract using four (4) data-driven models. Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest Neighbour (k-NN), and Decision Tree (DT) were used as modelling algorithms for the construction of a non-linear empirical model to predict the antioxidant properties of Amaranthus viridis seed extract. Datasets for the modelling operation were obtained from a Box Behnken design while the hyperparameters of the ANN, SVM, k-NN and DT were determined using a 10-fold cross-validation technique. Among the Machine Learning algorithms, DT was observed to exhibit excellent performance and outperformed other Machine Learning algorithms in predicting the antioxidant activities of the seed extract, with a sensitivity of 0.867, precision of 0.928, area under the curve of 0.979, root mean square error of 0.184 and correlation coefficient of 0.9878. It was closely followed by ANN which was used to analyze and explain in detail the effect of the independent variables on the antioxidant activities of the seed extracts. This result affirmed the suitability of DT in predicting the antioxidant activities of Amaranthus viridis.

Predicting the Growth of F. proliferatum and F. culmorum and the Growth of Mycotoxin Using Machine Learning Approach

In distinct parts of the food web, Fusarium culmorum and Fusarium preserving the relationship can germinate and grow zearalenone (ZEA) and fumonisins (FUM), accordingly. Antimicrobial drugs used to combat these fungi and toxic metabolites raise the risk of hazardous residue in food products, as well as the development of fungus tolerance. For modeling fungal growth and pathogenicity under separate water action (a q ) (0.96 and 0.99) and surface temp (20 and 28°C) tyrannies, several machine learning (ML) methodologies (artificial neural, regression trees, and extreme rise enhanced trees) and multiple regression model (MLR) were used also especially in comparison. GR and mycotoxin levels inside the environment often reduced as EOC concentrations grew, although some treatment in association with specific a q and temperature values caused ZEA production. In terms of predicting the growth rate of F. culmorum and F. maintaining the relationship and the production of ZEA and FUM, random forest techniques outperformed neural network models and extreme gradient boosted trees. The MLR option was the most inefficient. It is the first research to look at the ML potential of bio EVOH products containing EOCs and ambient variables of F. culmorum and F. proliferatum development, as well as the generation of zearalenone and fumonisins. The findings show that these entire novel wrapping technologies, in tandem using machine learning techniques, could be useful in predicting and controlling the dangers connected with fungal species or biotoxins in foodstuff.

Effects of Non-Thermal Treatment on Gilaburu Vinegar (Viburnum opulus L.): Polyphenols, Amino Acid, Antimicrobial, and Anticancer Properties

Simple Summary

In this study, traditionally produced vinegar made from gilaburu (C-GV) and thermally pasteurized gilaburu vinegar (P-GV), and (ultrasound-treated gilaburu vinegar (UT-GV) were evaluated. At the same time, ultrasound treatment enriched 11 phenolic compounds (gallic acid, protocatechuic acid, hydroxybenzoic acid, vanillic acid, p-coumaric acid, rutin, ferulic acid, o-coumaric acid, neohesperidin, quercetin, trans-cinnamic). Ultrasound showed different effects on free amino acids and volatile profiles. In general, ultrasound showed more positive results than thermal pasteurization. Six important minerals (Ca, Fe, K, Mg, Mn, and Zn) were detected in gilaburu vinegar, and ultrasound treatment increased the Fe content. Gilaburu vinegar, prepared by different methods, had potential antibacterial and anti-cancer activity.

Abstract

Gilaburu (Viburnum opulus L.) is an important fruit that has been studied in recent years due to its phytochemicals and health benefits. In this study, traditionally produced vinegar made from gilaburu fruit (C-GV) was evaluated. Vinegar with higher levels of bioactive components optimized by response surface methodology (RSM) was also produced using ultrasound (UT-GV). The maximum optimization result for the bioactive components was achieved at 14 min and 61.2 amplitude. The effectiveness of thermal pasteurization (P-GV) on gilaburu vinegar was evaluated. An increase was detected for every organic acid with ultrasound treatment. In the UT-GV and C-GV samples, arabinose was present, which is useful for stimulating the immune system. Gilaburu vinegar samples contained 29–31 volatile compounds. The smallest amount of volatile compounds was found in P-GV (1280.9 µg/kg), and the largest amounts of volatile compounds were found in C-GV (1566.9 µg/kg) and UT-GV (1244.10 µg/kg). In the UT-GV sample, Fe was increased, but Ca, K, Mg, and Mn were decreased. A total of 15 polyphenols were detected in C-GV, P-GV, and UT-GV samples, and gallic acid was the most common. A total of 17 free amino acids were detected in gilaburu vinegar samples. Ultrasound provided enrichment in total phenolic compounds and total free amino acids. All three vinegar samples had good antimicrobial activity against pathogens. The efficacy of C-GV, P-GV, and UT-GV samples against colon and stomach cancer was determined, but there were no significant differences between them. As a result, ultrasound treatment is notable due to its antimicrobial and anticancer activity, especially for the enrichment of phenolic compounds and amino acids in gilaburu vinegar.