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.

Ultrasound-assisted extraction of phytocompounds from dragon fruit peel: Optimization, kinetics and thermodynamic studies.

Ultrasound-assisted extraction method (UAE) was applied to recover phytocompounds from dragon fruit peel and the process was modelled and optimized using the combination of artificial neural network (ANN) and genetic algorithm (GA). The influence of ultrasonic temperature (30-70 °C), solvent to solid ratio (10:1-30:1 mL/g), solvent concentration (30-60%), and ultrasonic treatment time (5-25 min) on total polyphenolic content (ZT), antioxidant activity (ZD) and betacyanin content (ZB) was investigated. The ANN model successfully fitted to the experimental data and the output of ANN model was applied for genetic algorithm optimization. The optimal UAE conditions were obtained at ultrasonic temperature of 60 °C, solvent to solid ratio 25:1 mL/g, solvent concentration 60%, and ultrasonic treatment time of 20 min. The extraction kinetics and thermodynamic study for phytochemical compounds extracted from dragon fruit peel using UAE process was carried out at different combinations of temperature and time of extraction. The effective diffusion coefficient for total polyphenol content, antioxidant activity and betacyanin content were ranged from 2.99×10-11to4.84×10-11m2/s, 1.89×10-11to4.51×10-11m2/s and 2.55×10-11to5.40×10-11m2/s respectively and the corresponding mass transfer coefficient were varied between 2.00×10-06-2.81×10-06m/s, 1.53×10-06-2.66×10-06m/s and 1.81×10-06-3.05×10-06m/s respectively. The obtained information on effective diffusivity and mass transfer coefficient during extraction would allow the prediction of extraction rate and for estimation of operation conditions for industrial implementation.