Effects on quality characteristics of ultrasound-treated gilaburu juice using RSM and ANFIS modeling with machine learning algorithm

Gilaburu (Viburnum opulus L.) is a red-colored fruit with a sour taste that grows in Anatolia. It is rich in various antioxidant and bioactive compounds. In this study, bioactive compounds and ultrasound parameters of ultrasound-treated gilaburu water were optimized by response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). As a result of RSM optimization, the independent ultrasound parameters were determined as an ultrasound duration of 10.7 min and an ultrasound amplitude of 53.3, respectively. The R2 values of the RSM modeling level were 99.93%, 98.54%, and 99.80%, respectively, and the R2 values of the ANFIS modeling level were 99.99%, 98.89%, and 99.87%, respectively. Some quality parameters of gilaburu juice were compared between ultrasound-treated gilaburu juice (UT-GJ), thermal pasteurized gilaburu juice (TP-GJ), and control group (C-GJ). The quality parameters include bioactive compounds, phenolic compounds, minerals, and sensory evaluation. Bioactive compounds in the samples increased after ultrasound application compared to C-GJ and TP-GJ samples. The content of 15 different phenolic compounds was determined in Gilaburu juice samples, and the phenolic compound of UT-GJ samples increased compared to TP-GJ and C-GJ samples, except for gentisic acid. Ultrasound treatment applied to gilaburu juice enabled its bioactive compounds to hold more in the juice.

Optimization of sensory properties of ultrasound-treated strawberry vinegar

Vinegar is renowned for its benefits to human health due to the presence of antioxidants and bioactive components. Firstly, this study optimized the production conditions of ultrasound-treated strawberry vinegar (UT-SV), known for its high consumer appeal. The sensory properties of UT-SV were optimized by response surface methodology (RSM) to create the most appreciated strawberry vinegar. Secondly, various quality parameters of conventional strawberry vinegar (C-SV), UT-SV, and thermally pasteurized strawberry vinegar (P-SV) samples were compared. RSM was employed to craft the best strawberry vinegar based on consumers ratings of UT-SV. Sensory characteristics, bioactive values, phenolic contents, and organic acid contents of C-SV, UT-SV, and P-SV samples were assessed. Through optimization, the ultrasound parameters of the independent variables were determined as 5.3 min and 65.5 % amplitude. The RSM modeling levels exhibited high agreement with pungent sensation at 98.06 %, aromatic intensity at 98.98 %, gustatory impression at 99.17 %, and general appreciation at 99.26 %, respectively. Bioactive components in UT-SV samples increased after ultrasound treatment compared to C-SV and P-SV samples. Additionally, the amount of malic acid, lactic acid, and oxalic acid increased after ultrasound treatment compared to C-SV samples. Ultimately, UT-SV with high organoleptic properties was achieved. The ultrasound treatment positively impacted the bioactive values, phenolic and organic acid content, leading to the development of a new and healthy product.

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.

Deciphering the mechanism of jujube vinegar on hyperlipoidemia through gut microbiome based on 16S rRNA, BugBase analysis, and the stamp analysis of KEEG

Background

Growing data indicate that the gut microbiome may contribute to the rising incidence of hyperlipoidemia. Jujube vinegar lowers lipids, protects the liver, and reduces oxidant capacity, however, it is unknown whether this is due to the gut flora. To further research the role of the gut microbiome in treating hyperlipidemia with jujube vinegar, we looked into whether the action of jujube vinegar is related to the regulation of the gut microbiome.

Method

Thirty male ICR mice were used. The control group (CON), the high-fat diet (HFD) group, and the vinegar group (VIN) each consisted of ten female ICR mice fed consistently for eight weeks. For each treatment, we kept track of body mass, liver index, blood lipid levels, and oxidative stress state. We also analyzed mouse feces using high-throughput 16srRNA sequencing to examine the relationship between jujube vinegar's hypolipidemic effect and antioxidant activity and how it affects the gut microbiome.

Results

Jujube vinegar reduced body weight by 19.92%, serum TC, TG, and LDL-C by 25.09%, 26.83%, and 11.66%, and increased HDL-C by 1.44 times, serum AST and ALT decreased by 26.36% and 34.87% respectively, the blood levels of SOD and GSH-Px increased 1.35-fold and 1.60-fold, respectively. While blood MDA decreased 33.21%, the liver's SOD and GSH-Px increased 1.32-fold and 1.60-fold, respectively, and the liver's MDA decreased 48.96% in HFD mice. The gut microbiome analysis revealed that jujube vinegar increased the intestinal microbial ASV count by 13.46%, and the F/B (Firmicutes/Bacteroidota) ratio by 2.08-fold in high-fat diet mice, and the proportion was significantly inversely correlated with TC, TG, and LDL-C and positively correlated with HDL-C. Biomarker bacteria in the vinegar group included Lactobacillaceae and Lactobacillus, which correlated favorably with HDL-C, SOD, and GSH-Px and negatively with LDL-C, TC, and TG. Jujube vinegar increased the abundance of the Aerobic, Contains Mobile Elements, and Facultative Aerobic by 2.84 times, 1.45 times, and 2.40 times, while decreased the abundance of Potential pathogens by 44.72%, according to the BugBase study. The KEGG analysis showed that jujube vinegar was predominantly reflected in the biological process of gene function and related to signal transduction pathways, including glucagon signaling system, HIF-1 signaling pathway, adipocytokine signaling pathway, amino sugar, and nucleotide sugar metabolism, and so forth.

Conclusion

Based on these findings, jujube vinegar may reduce hyperlipoidemia by controlling the gut microbiome and enhancing antioxidant capacity.

Determination of Changes in Volatile Aroma Components, Antioxidant Activity and Bioactive Compounds in the Production Process of Jujube (Ziziphus jujuba Mill.) Vinegar Produced by Traditional Methods

Jujube has anticancer, diabetic, antimicrobial, anti-inflammatory, cardiovascular, gastrointestinal and immune system effects. In this study, jujube juice, jujube wine and jujube vinegar were investigated in terms of chemical composition, phenolic contents, organic acid contents, volatile compound contents, and antioxidant activity. Antioxidant activity and total phenolic content of jujube vinegar produced by traditional methods were found to be higher than those of jujube juice and wine. Protocatechic acid, chlorogenic acid, phydroxybenzoic acid, caffeic acid, epicatechin, and syringic acid were detected in jujube vinegar. Moreover, oxalic acid, malic acid, tartaric acid, formic acid, ascorbic acid, lactic acid, acetic acid and some other organic acid components were determined in jujube vinegar. Volatile aroma compounds such as ester, aldehyde, alcohol, terpene, acid, and ketone were determined in jujube samples. It was seen that the antioxidant activity and bioactive compounds of jujube vinegar were very rich, and jujube vinegar, which is an alternative product with a high potential produced from jujube fruit, is an important product for the food sector due to its long shelf life. This research is the first detailed study in which the antioxidant activity and bioactive compounds determined during the production stages of jujube vinegar (jujube juice, wine, and vinegar) were evaluated in detail.