Development of Potato bread by enrichment of Dietary fibre and Analysis Jamun (Syzygium cumini) Chemical Extract composition

Development of Traditional potato bread by enrichment of dietary fibre content and addition of Jamun as Medicinal herb from india which is used for curing the diabetes. The main purpose of development of this bread is to prepare healthy food products for European people.MSc/MAInstitute of Food Technolog

Ultrasound assisted phytochemical extraction of persimmon fruit peel: Integrating ANN modeling and genetic algorithm optimization

In the present study, ultrasound assisted extraction (UAE) of phytochemicals from persimmon fruit peel (PFP) was modeled using an artificial neural network (ANN) and optimized by integrating with genetic algorithm (GA). The range of process parameters selected for conducting the experiments was ultrasonication power (XU) 150–––350 W, extraction temperatures (XT) 30–––70 °C, solid to solvent ratio (XS) 1:15–––1:35 g/ml, and ethanol concentration (XC) 40–––80 %. The range of responses total phenolic content (YP), antioxidant activity (YA), total beta carotenoid (YB) and total flavonoid content (YF) at various independent variables combinations were found to be 7.72–––24.62 mg GAE/g d.w., 51.44–––85.58 %DPPH inhibition, 24.78–––56.56 µg/g d.w. and 0.29–––1.97 mg QE/g d.w. respectively. The modelling utilised an ANN architecture with a configuration of 4–12-4. The training process employed the Levenberg–Marquardt method, whereas the activation function chosen for the layers was the log sigmoid. The optimum condition predicted by the hybrid ANN-GA model for the independent variables, XU, XT, XS and XC was found to be 230.18 W, 50.66 °C, 28.27 g/ml, and 62.75 % respectively. The extraction process was carried out for 25 min, with 5-minute intervals, at various temperatures between 30 and 60 °C, to investigate the kinetic and thermodynamic characteristics of the process, under the optimal conditions of XU, XS and XC. The UAE of phytochemicals from persimmon peel followed pseudo second order kinetic model and the extraction process was endothermic in nature

Modelling of ultrasonic assisted osmotic dehydration of cape gooseberry using adaptive neuro-fuzzy inference system (ANFIS)

In the present investigation, the cape gooseberry (Physalis peruviana L.) was preserved by the application of osmotic dehydration (sugar solution) with ultrasonication. The experiments were planned based on central composite circumscribed design with four independent variables and four dependent variables, which yielded 30 experimental runs. The four independent variables used were ultrasonication power (XP) with a range of 100–500 W, immersion time (XT) in the range of 30–55 min, solvent concentration (XC) of 45–65 % and solid to solvent ratio (XS) with range 1:6–1:14 w/w. The effect of these process parameters on the responses weight loss (YW), solid gain (YS), change in color (YC) and water activity (YA) of ultrasound assisted osmotic dehydration (UOD) cape gooseberry was studied by using response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). The second order polynomial equation successfully modeled the data with an average coefficient of determination (R2) was found to be 0.964 for RSM. While for the ANFIS modeling, Gaussian type membership function (MF) and linear type MF was used for the input and output, respectively. The ANFIS model formed after 500 epochs and trained by hybrid model was found to have average R2 value of 0.998. On comparing the R2 value the ANFIS model found to be superior over RSM in predicting the responses of the UOD cape gooseberry process. So, the ANFIS was integrated with a genetic algorithm (GA) for optimization with the aim of maximum YW and minimum YS, YC and YA. Depending on the higher fitness value of 3.4, the integrated ANFIS-GA picked the ideal combination of independent variables and was found to be XP of 282.434 W, XT of 50.280 min, XC of 55.836 % and XS of 9.250 w/w. The predicted and experimental values of response at optimum condition predicted by integrated ANN-GA were in close agreement, which was evident by the relative deviation less than 7%

Ultrasound assisted phytochemical extraction of red cabbage by using deep eutectic solvent: Modelling using ANFIS and optimization by genetic algorithms

The present investigation studied the effect of process parameters on the extraction of phytochemicals from red cabbage by the application of ultrasonication and temperature. The solvent selected for the study was deep eutectic solvent (DES) prepared by choline chloride and citric acid. The ultrasound assisted extraction process was modeled using adaptive neuro-fuzzy inference system (ANFIS) algorithm and integrated with the genetic algorithm for optimization purposes. The independent variables that influenced the responses (total phenolic content, antioxidant activity, total anthocyanin activity, and total flavonoid content) were ultrasonication power, temperature, molar ratio of DES, and water content of DES. Each ANFIS model was formed by the training of three Gaussian-type membership functions (MF) for each input, trained by a hybrid algorithm with 500 epochs and linear type MF for output MF. The ANFIS model predicted each response close to the experimental data which is evident by the statistical parameters (R2>0.953 and RMSE <1.165). The integrated hybrid ANFIS-GA algorithm predicted the optimized condition for the process parameters of ultrasound assisted extraction of phytochemicals from red cabbage was found to be 252.114 W for ultrasonication power, 52.715 °C of temperature, 2.0677:1 of molar ratio of DES and 25.947 % of water content in DES solvent with maximum extraction content of responses, with fitness value 3.352. The relative deviation between the experimental and ANFIS predicted values for total phenolic content, antioxidant activity, total anthocyanin activity, and total flavonoid content was found to be 1.849 %, 3.495 %, 2.801 %, and 4.661 % respectively

Machine Learning Algorithms and Fundamentals as Emerging Safety Tools in Preservation of Fruits and Vegetables: A Review

Machine learning assists with food process optimization techniques by developing a model to predict the optimal solution for given input data. Machine learning includes unsupervised and supervised learning, data pre-processing, feature engineering, model selection, assessment, and optimization methods. Various problems with food processing optimization could be resolved using these techniques. Machine learning is increasingly being used in the food industry to improve production efficiency, reduce waste, and create personalized customer experiences. Machine learning may be used to improve ingredient utilization and save costs, automate operations such as packing and labeling, and even forecast consumer preferences to develop personalized products. Machine learning is also being used to identify food safety hazards before they reach the consumer, such as contaminants or spoiled food. The usage of machine learning in the food sector is predicted to rise in the near future as more businesses understand the potential of this technology to enhance customer experience and boost productivity. Machine learning may be utilized to enhance nano-technological operations and fruit and vegetable preservation. Machine learning algorithms may find trends regarding various factors that impact the quality of the product being preserved by examining data from prior tests. Furthermore, machine learning may be utilized to determine optimal parameter combinations that result in maximal produce preservation. The review discusses the relevance of machine learning in ready-to-eat foods and its use as a safety tool for preservation were highlighted. The application of machine learning in agriculture, food packaging, food processing, and food safety is reviewed. The working principle and methodology, as well as the principles of machine learning, were discussed

Phytochemical and pharmacological characteristics of phalsa (Grewia asiatica L.): A comprehensive review

Phalsa is a tropical and subtropical fruit that is high in nutritional value and is primarily cultivated for its fruit. As, Phalsa fruit contain high number of vitamins (A and C), minerals (calcium, phosphorus, and iron), and fibre while being low in calories and fat. The fruit and seed of Phalsa contain 18 amino acids, the majority of which are aspartic acid, glutamic acid, and leucine. Based on in vivo and in vitro studies phalsa plant possess high antioxidant, anti-inflammatory, anticancer, antimicrobial, antidiabetic properties. However, antioxidant properties are found in the form of vitamin C, total phenolic, anthocyanin, flavonoid, and tannin. The phalsa plant's fruits and leaves have substantial anticancer action against cancer cell lines. Because of the presence of a broad range of physiologically active chemicals, investigations on phalsa plants revealed that some plant parts have radioprotective qualities. The anti-glycosidase and anti-amylase activity of aqueous fresh fruit extract was shown to be substantial. The phalsa plant contains an abundance of biologically active chemicals, allowing it to control microorganisms through a variety of processes. Phalsa methanolic leaf extract was revealed to have antimalarial and antiemetic effects. The hot and cold polysaccharide fractions extracted from the phalsa plant have potent hepatoprotective and therapeutic properties. Therefore, this review is based on the nutritional, bioactive, phytochemicals, and potential pharmacological uses of phalsa. The potential health benefits and economic potential of the phalsa berry's phytochemicals are promising areas for further study

Ultrasound-assisted extraction of phytochemicals from green coconut shell: Optimization by integrated artificial neural network and particle swarm technique

This study employs artificial neural network (ANN) and particle swarm optimization (PSO) to maximize antioxidant and antimicrobial activity from green coconut shells. Phytochemical analysis was carried out on the extract obtained from ultrasound-assisted extraction performed at different combinations of time (10, 20, and 30 min), temperature (30, 35, and 40 °C), and the ratio of solid-solvent (1:10, 1:20, and 1:30 g/ml). The presence of these bioactive compounds exhibits antimicrobial and antioxidant activities. Quantitative analysis showed that the total phenolic compounds ranged from 7.08 to 33.46 mg GAE/g, flavonoids ranged from 2.09 to 28.46 mg QE/g, tannins ranged from 70.5 to 141.09 mg TAE/g, and antioxidant activity of 49.98–66.1 %. The FTIR analysis detected the presence of CO, O–H, and C–H bonds. The optimized condition of ultrasound-assisted extraction (UAE) was compared with the optimized condition of the microwave. The result of ultrasound-assisted extraction was observed to be better than microwave-assisted extraction

Extraction and Encapsulation of Phytocompounds of Poniol Fruit via Co-Crystallization: Physicochemical Properties and Characterization

Poniol (Flacourtia jangomas) has beneficial health effects due to its high polyphenolic and good antioxidant activity content. This study aimed to encapsulate the Poniol fruit ethanolic extract to the sucrose matrix using the co-crystallization process and analyze the physicochemical properties of the co-crystalized product. The physicochemical property characterization of the sucrose co-crystallized with the Poniol extract (CC-PE) and the recrystallized sucrose (RC) samples was carried out through analyzing the total phenolic content (TPC), antioxidant activity, loading capacity, entrapment yield, bulk and traped densities, hygroscopicity, solubilization time, flowability, DSC, XRD, FTIR, and SEM. The result revealed that the CC-PE product had a good entrapment yield (76.38%) and could retain the TPC (29.25 mg GAE/100 g) and antioxidant properties (65.10%) even after the co-crystallization process. Compared to the RC sample, the results also showed that the CC-PE had relatively higher flowability and bulk density, lower hygroscopicity, and solubilization time, which are desirable properties for a powder product. The SEM analysis showed that the CC-PE sample has cavities or pores in the sucrose cubic crystals, which proposed that the entrapment was better. The XRD, DSC, and FTIR analyses also showed no changes in the sucrose crystal structure, thermal properties, and functional group bonding structure, respectively. From the results, we can conclude that co-crystallization increased sucrose’s functional properties, and the co-crystallized product can be used as a carrier for phytochemical compounds. The CC-PE product with improved properties can also be utilized to develop nutraceuticals, functional foods, and pharmaceuticals

Nanophytosomal Gel of <i>Heydotis corymbosa</i> (L.) Extract against Psoriasis: Characterisation, In Vitro and In Vivo Biological Activity

The current study was conducted to examine the possible advantages of Heydotis corymbosa (L.) Lam. extract nanogel as a perspective for enhanced permeation and extended skin deposition in psoriasis-like dermatitis. Optimised nanophytosomes (NPs) were embedded in a pluronic gel base to obtain nanogel and tested ex vivo (skin penetration and dermatokinetics) and in vivo. The optimised NPs had a spherical form and entrapment efficiency of 73.05 ± 1.45% with a nanosized and zeta potential of 86.11 nm and −10.40 mV, respectively. Structural evaluations confirmed encapsulation of the drug in the NPs. Topical administration of prepared nanogel to a rat model of psoriasis-like dermatitis revealed its specific in vivo anti-psoriatic efficacy in terms of drug activity compared to the control and other formulations. Nanogel had improved skin integrity and downregulation of inflammatory cytokines. These findings suggest that developed phytoconstituent-based nanogel has the potential to alleviate psoriasis-like dermatitis with better skin retention and effectiveness