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Keywords: Hot compressed water extraction, palm oil, artificial neural network Hot compressed water extraction (HCWE) is a promising green alternative of the screw press in the palm oil processing. In this study, steady-state characteristic of the HCWE was modeled by using the artificial neural network (ANN). The overall oil yield and other outputs; β-carotene, α-tocopherol and α-tocotrienol concentration were described by the pressure and temperature in the HCWE. The results show that the predicted yield and concentrations agree well with experimental data. These models were used to estimate the optimum conditions of the HCWE process.

Rice bran is a by-product of the rice milling process. It contains a high concentration of protein. Rice brans are frequently utilized as feed cattle, fertilizer, and fuel. However, their application as human nutrition supplements is uncommon, and the necessary process for this purpose is yet to be established, including the drying process. This study aims to evaluate the effect of the spray-drying parameters, the inlet temperature, inlet flowrate, and inlet air flowrate, on rice bran protein (RBP) powder and optimize it using response surface methodology (RSM). A thermal water-based extraction method was utilized prior to the drying process. The correlation between the spray-drying parameters, i.e., the inlet temperature (120 to 210 °C), feed flowrate (5 to 55%), and air flowrate (246 to 670 L/h), and the RBP yield were investigated. The quality of the RBP powder was evaluated based on acid amino profiling in the mixture through de novo peptide sequencing. The optimized operating conditions for the maximum yield of RBP powder (25.7 g RBP/100 g RRB) are 178 °C, feed flowrate of 25%, and air flowrate of 450 L/h. The main peptides that contribute to RBP powder protein are globulin and glutelin; meanwhile, prolamin is believed to degrade during the drying process. The process also produced protein sugar, helping to produce fine particles powder without the drying agent.