Optimization of cocoa butter analog synthesis variables using neural networks and genetic algorithm

Neuroprotective Effect of 1,3-dipalmitoyl-2-oleoylglycerol Derived from Rice Bran Oil against Cerebral Ischemia-Reperfusion Injury in Rats

1,3-Dipalmitoyl-2-oleoylglycerol (POP) is a triacylglyceride found in oils from various natural sources, including palm kernels, sunflower seeds, and rice bran. In the current study, the neuroprotective effects and the specific mechanism of POP derived from rice bran oil were investigated for the first time using the middle cerebral artery occlusion/reperfusion (MCAO/R) model in rats. Orally administered POP at 1, 3, or 5 mg/kg (three times: 0.5 h before MCAO, after 1 h of MCAO, and after 1 h of reperfusion) markedly reduced the MCAO/R-induced infarct/edema volume and neurobehavioral deficits. Glutathione depletion and the oxidative degradation of lipids in the rat brain induced by MCAO/R were prevented by POP administration. The upregulation of phosphorylated p38 MAPKs, inflammatory factors (inducible nitric oxide synthase (i-NOS) and cyclooxygenase-2 (COX-2)), and pro-apoptotic proteins (B-cell lymphoma-2 (Bcl-2) associated X protein (Bax) and cleaved caspase-3) and the downregulation of the anti-apoptotic protein (Bcl-2) in the ischemic brain were significantly inhibited by POP administration. In addition, downregulation of phosphatidylinositol 3′-kinase (PI3K), phosphorylated protein kinase B (Akt), and phosphorylated cyclic (adenosine monophosphate) AMP responsive element-binding protein (CREB) expression in the ischemic brain was inhibited by POP administration. These results suggest that POP might exert neuroprotective effects by inhibition of p38 MAPK and activation of PI3K/Akt/CREB pathway, which is associated with anti-oxidant, anti-apoptotic, and anti-inflammatory action. From the above results, the present study provides evidence that POP might be effectively applied for the management of cerebral ischemia-related diseases.

Application of bio-inspired optimization algorithms in food processing

Bio-inspired optimization techniques (BOT) are part of intelligent computing techniques. There are several BOTs available and many new BOTs are evolving in this era of industrial revolution 4.0. Genetic algorithm, particle swarm optimization, artificial bee colony, and grey wolf optimization are the techniques explored by researchers in the field of food processing technology. Although, there are other potential methods that may efficiently solve the optimum related problem in food industries. In this review, the mathematical background of the techniques, their application and the potential microbial-based optimization methods with higher precision has been surveyed for a complete and comprehensive understanding of BOTs along with their mechanism of functioning. These techniques can simulate the process efficiently and able to find the near-to-optimal value expeditiously.

Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS

This study aimed to predict the drying kinetics, energy utilization (Eu ), energy utilization ratio (EUR), exergy loss, and exergy efficiency of quince slice in a hot air (HA) dryer using artificial neural networks and ANFIS. The experiments were performed at air temperatures of 50, 60, and 70°C and air velocities of 0.6, 1.2, and 1.8 m/s. The thermal parameters were determined using thermodynamic relations. Increasing air temperature and air velocity increased the effective moisture diffusivity (Deff ), Eu , EUR, exergy efficiency, and exergy loss. The value of the Deff was varied from 4.19 × 10-10 to 1.18 × 10-9 m2/s. The highest value Eu , EUR, and exergy loss and exergy efficiency were calculated 0.0694 kJ/s, 0.882, 0.044 kJ/s, and 0.879, respectively. Midilli et al. model, ANNs, and ANFIS model, with a determination coefficient (R 2) of .9992, .9993, and .9997, provided the best performance for predicting the moisture ratio of quince fruit. Also, the ANFIS model, in comparison with the artificial neural networks model, was better able to predict Eu , EUR, exergy efficiency, and exergy loss, with R 2 of .9989, .9988, .9986, and .9978, respectively.