Modeling of rheological behavior of honey using genetic algorithm–artificial neural network and adaptive neuro-fuzzy inference system

Mathematical Description of Changes of Dried Apple Characteristics during Their Rehydration

The mathematical description of changes of dried apples characteristics (mass gain, volume increase, dry matter loss, rehydration indices, and colour) during their rehydration was performed. The effect of conditions of both processes on model parameters were also considered. Apple slices (3 and 10 mm) and cubes (10 mm) were dried in natural convection (drying air velocity 0.01 m/s), forced convection (0.5 and 2 m/s), and fluidisation (6 m/s). Drying air temperatures (Td) were equal to 50, 60, and 70 °C. The rehydration process was carried out in distilled water at the temperatures (Tr) of 20, 45, 70, and 95 °C. Mass gain, volume increase, and dry matter loss were modelled using the following empirical models: Peleg, Pilosof–Boquet–Batholomai, Singh and Kulshrestha, Lewis (Newton), Henderson–Pabis, Page, and modified Page. Colour changes were described through applying the first-order model. Artificial neural networks (feedforward multilayer perceptron) were applied to make the rehydration indices and colour variations (ΔE) dependent on characteristic dimension, Td, drying air velocity, and Tr. The Page and the modified Page models can be considered to be the most appropriate in order to characterise the mass gain (RMSE = 0.0143–0.0619) and the volume increase (RMSE = 0.0142–0.1130), whereas the Peleg, Pilosof–Bouquet–Batholomai, and Singh and Kulshrestha models were found to be the most appropriate to characterise dry matter loss (RMSE = 0.0116–0.0454). The ANNs described rehydration indices and ΔE satisfactorily (RMSE = 0.0567–0.0802). Both considered process conditions influenced (although in different degree) the changes of the considered dried apple characteristics during their rehydration.

Artificial neural network for optimizing the formulation of curcumin-loaded liposomes from statistically designed experiments

Curcumin is a primary polyphenol of the rhizomatous perennial plant called Curcuma Longa. Curcumin interferes favorably with the cellular events that take place in the inflammatory and proliferative stages of wound healing, hence its importance in skin regeneration and wound healing. Curcumin is however lipophilic, and this must be considered in the choice of its drug delivery system. Liposomes are spherical vesicles with bi-lipid layers. Liposomes can encapsulate both lipophilic and hydrophilic drugs, hence their suitability as an ideal drug delivery system for curcumin. There is, nevertheless, a tendency for liposomes to be unstable and have low encapsulation efficiency if it is not formulated properly. Formulation optimization of curcumin-loaded liposomes was studied by the application of artificial neural network (ANN) to improve encapsulation efficiency and flux of the liposomes. The input factors selected for optimization of the formulation were sonication time, hydration volume, and lipid/curcumin ratio. The response variables were encapsulation efficiency and flux. The maximum encapsulation efficiency and flux were obtained using lipid/curcumin ratio of 4.35, sonicator time of 15 min, and hydration volume of 25 mL. The maximum encapsulation efficiency and flux predicted were 100% and 51.23 µg/cm²/h, respectively. The experimental values were 99.934% and 51.229 µg/cm²/h, respectively. Curcumin-loaded liposome formulation is a promising drug delivery system in the pharmaceutical industry when formulated using optimized parameters derived from ANN statistically designed models.

Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling

Two different drying methods were applied for dehydration of apple, i.e., convective drying (CD) and microwave drying (MD). The process of convective drying through divergent temperatures; 50, 60 and 70 °C at 1.0 m/s air velocity and three different levels of microwave power (90, 180, and 360 W) were studied. In the analysis of the performance of our approach on moisture ratio (MR) of apple slices, artificial neural networks (ANNs) was used to provide with a background for further discussion and evaluation. In order to evaluate the models mentioned in the literature, the Midilli et al. model was proper for dehydrating of apple slices in both MD and CD. The MD drying technology enhanced the drying rate when compared with CD drying significantly. Effective diffusivity (D_eff) of moisture in CD drying (1.95 × 10^-7-4.09 × 10^-7 m²/s) was found to be lower than that observed in MD (2.94 × 10^-7-8.21 × 10^-7 m²/s). The activation energy (Ea) values of CD drying and MD drying were 122.28-125 kJ/mol and 14.01-15.03 W/g respectively. The MD had the lowest specific energy consumption (SEC) as compared to CD drying methods. According to ANN results, the best R² values for prediction of MR in CD and MD were 0.9993 and 0.9991, respectively.

Comprehensive study on applications of artificial neural network in food process modeling

Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to food process engineering is relatively novel. ANN had been employed in diverse applications like food safety and quality analyses, food image analysis, and modeling of various thermal and non-thermal food-processing operations. ANN has the ability to map nonlinear relationships without any prior knowledge and predicts responses even with incomplete information. Every neural network possesses data in the form of connection weights interconnecting lines between the input to hidden layer neurons and weights of hidden to output layer neurons, which has a significant role in predicting the output data. The applications of ANN in different unit operations in food processing were described that includes theoretical developments using intelligent characteristics for adaptability, automatic learning, classification, and prediction. The parallel architecture of ANN resulted in a fast response and low computational time making it suitable for application in real-time systems of different food process operations. The predicted responses obtained by the ANN model exhibited high accuracy due to lower relative deviation and root mean squared error and higher correlation coefficient. This paper presented the various applications of ANN for modeling nonlinear food engineering problems. The application of ANN in the modeling of the processes such as extraction, extrusion, drying, filtration, canning, fermentation, baking, dairy processing, and quality evaluation was reviewed.HIGHLIGHTS1. This paper discusses application of ANN in different emerging trends in food process.2. Application of ANN to develop non-linear multivariate modeling is illustrated.3. ANNs have been shown to be useful tool for prediction of outcomes with high accuracy.4. ANN resulted in fast response making it suitable for application in real time systems.

Development of trigger sensitive hyaluronic acid/palm oil-based organogel for in vitro release of HIV/AIDS microbicides using artificial neural networks

Background

Efficient and effective chemotherapeutic methods designed to prevent the continuous spread of HIV/AIDS is essential to break the cycle of new infections. The use of condoms has been seen to be effective in prevention of HIV and STIs but its lack of use especially in vulnerable population is a deterrent to its overall success as a control method. Utilization of topical microbicide to curb the spread of HIV follows the current paradigm for HIV prevention in at risk individuals. The objective of this study was to develop and evaluate hyaluronic acid/palm oil-based organogel loaded with maraviroc (MRV) which would be released using hyaluronidase as the trigger for pre-exposure prophylaxis of HIV.

Results

The organogels had average globules size 581.8 ± 3.9 nm, and were stable after three freeze thaw cycles; the thermosensitive and HA sensitivity was achieved via incorporation of hyaluronic acid and dicaprylate esters in the organogel with thermogelation occurring at 34.1 °C. Artificial neural network was used to model and optimize mucin absorption and flux. These responses were predicted using the multilayer full feed forward (MFFF) and the multilayer normal feed forward (MNFF) neural networks. Optimized organogel showed the mucin adsorption and flux was 70.84% and 4.962 μg/cm2/min1/2, hence MRV was adequately released via triggers of temperature and HA. The MRV organogel showed inhibition HIV − 1 via TZM-bl indicator cells. Compared to control HeLa cells without any treatment, MRV organogel was not cytotoxic for 14 days in vitro.

Conclusion

These data highlight the potential use of hyaluronic acid/palm oil-based organogel for vaginal delivery of anti-HIV microbicides. This can serve as a template for more studies on such formulations in the area of HIV prevention.

Modeling and estimating aboveground biomass of Dacrydium pierrei in China using machine learning with climate change

Accurate estimations of the aboveground biomass (AGB) of rare and endangered species are particularly important for protecting forest ecosystems and endangered species and for providing useful information to analyze the influence of past and future climate change on forest AGB. We investigated the feasibility of using three developed and two widely used models, including a generalized regression neural network (GRNN), a group method of data handling (GMDH), an adaptive neuro-fuzzy inference system (ANFIS), an artificial neural network (ANN) and a support vector machine (SVM), to estimate the AGB of Dacrydium pierrei (D. pierrei) in natural forests of China. The results showed that these models could explain the changes in the AGB of the D. pierrei using a limited amount of meteorological data. The GRNN and ANN models are superior to the other models for estimating the AGB of D. pierrei. The GMDH model consistently produced comparatively poor estimates of the AGB. Three climate scenarios, including the representative concentration pathway (RCP) 2.6, RCP 4.5, and RCP 8.5, were compared with the climate situation of 2013-2017. Under these scenarios, the AGB of D. pierrei females with the same diameter at breast height (DBH) would increase by 13.0 ± 31.4% (mean ± standard deviation), 16.6 ± 30.7%, and 18.5 ± 30.9% during 2041-2060 and 15.6 ± 32.1%, 21.2 ± 33.2%, and 24.8 ± 32.7% during 2061-2080; the AGB of males would increase by 16.3 ± 32.3%, 21.7 ± 32.5%, and 22.9 ± 32.6% during 2041-2060 and 22.3 ± 30.8%, 27.2 ± 31.8%, and 30.1 ± 34.4% during 2061-2080. The R² values of all models range from 0.82 to 0.95. In conclusion, this study suggests that these advanced models are recommended to estimate the AGB of forests, and the AGB of forests would increase in 2041-2080 under future climate scenarios.

Honey authentication using rheological and physicochemical properties

The aim of this study was to evaluate the influence of honey botanical origins on rheological parameters. In order to achieve the correlation, fifty-one honey samples, of different botanical origins (acacia, polyfloral, sunflower, honeydew, and tilia), were investigated. The honey samples were analysed from physicochemical (moisture content, fructose, glucose and sucrose content) and rheological point of view (dynamic viscosity-loss modulus G″, elastic modulus G', complex viscosity η*, shear storage compliance-J' and shear loss compliance J″). The rheological properties were predicted using the Artificial Neural Networks based on moisture content, glucose, fructose and sucrose. The models which predict better the rheological parameters in function of fructose, glucose, sucrose and moisture content are: MLP-1 hidden layer is predicting the G″, η* and J″, respectively, MLP-2 hidden layers the J', while MLP-3 hidden layers the G', respectively. The physicochemical and rheological parameters were submitted to statistical analysis as follows: Principal component analysis (PCA), Linear discriminant analysis (LDA) and Artificial neural network (ANN) in order to evaluate the usefulness of the parameters studied for honey authentication. The LDA was found the suitable method for honey botanical authentication, reaching a correct cross validation of 94.12% of the samples.

Chemical composition and temperature influence on honey texture properties

The aim of this study is to evaluate the chemical composition and temperatures (20, 30, 40, 50 and 60 °C) influence on the honey texture parameters (hardness, viscosity, adhesion, cohesiveness, springiness, gumminess and chewiness). The honeys analyzed respect the European regulation in terms of moisture content and inverted sugar concentration. The texture parameters are influenced negatively by the moisture content, and positively by the °Brix concentration. The texture parameters modelling have been made using the artificial neural network and the polynomial model. The polynomial model predicted better the texture parameters than the artificial neural network.