A natural clinoptilolite nanostructure (CNS) along with ultrasonic irradiation was used to remove Cu2+ ions from polluted water. In the first part of this work, natural clinoptilolite was converted to CNS by ball milling. The natural clinoptilolite and prepared CNS samples were characterized by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, X-ray diffraction, N2 adsorption/desorption and pH at the point of zero charge analyses. The SEM images showed the development of CNS from natural clinoptilolite by ball milling. The N2 adsorption/desorption and FTIR spectroscopy confirmed the greater specific surface area, pore volume and number of surface groups of the CNS compared to the natural clinoptilolite. In addition, the crystalline phase of the CNS was the same as the natural clinoptilolite. In the second part of this work, the ultrasonic-assisted sorption of Cu2+ ions from polluted water by CNS was investigated. These experiments were optimized with response surface methodology based on central composite designs. The effects of initial pH of solution, CNS dosage, sonication time and temperature on Cu2+ ion-removal efficiency were investigated. By using a CNS dosage of 500 mg L−1, an initial pH of 6, a sonication time of 12 min and a sonication temperature of 45°C as optimal conditions, 97% of Cu2+ ions were removed from contaminated water. The initial pH was the most effective variable. Ultrasonic-assisted sorption of Cu2+ was more effective than sorption alone, onto the CNS.

The experiments reported in this research paper describe the effects of β-galactosidase enzyme dose and cheese whey amount, on the maximum concentration and yield of galacto-oligosaccahride (GOS) and reaction time. The experimental plan was based on central composite rotational design (CCRD) and modelled by response surface methodology (RSM). The results indicate that the proposed mathematical models could adequately describe the concentration and yield of GOS and the reaction time within the limits of the factors that are being investigated. The variance analysis shows high values of coefficients of determination (>0·97) while no significant lack of fit was evident. Hence, the models could be employed to select reaction conditions applied in the manufacture of products enriched in bioactive compounds with high value-added.

The recovery of milk constituents from cheese whey is affected by various processing conditions followed during production of Ricotta cheese. The objective of the present investigation was to optimize the temperature (60–90 °C), pH (3–7) and CaCl2 concentration (2·0–6·0 mm) for maximum yield/recovery of milk constituents. The research work was carried out in two phases. In 1st phase, the influence of these processing conditions was evaluated through 20 experiments formulated by central composite design (CCD) keeping the yield as response factor. The results obtained from these experiments were used to optimize processing conditions for maximum yield using response surface methodology (RSM). The three best combinations of processing conditions (90 °C, pH 7, CaCl2 6 mm), (100 °C, pH 5, CaCl2 4 mm) and (75 °C, pH 8·4, CaCl2 4 mm) were exploited in the next phase for Ricotta cheese production from a mixture of Buffalo cheese whey and skim milk (9 : 1) to determine the influence of optimized conditions on the cheese composition. Ricotta cheeses were analyzed for various physicochemical (moisture, fat, protein, lactose, total solids, pH and acidity indicated) parameters during storage of 60 d at 4 ± 2 °C after every 15 d interval. Ricotta cheese prepared at 90 °C, pH 7 and CaCl2 6 mm exhibited the highest cheese yield, proteins and total solids, while high fat content was recorded for cheese processed at 100 °C, pH 5 and 4 mm CaCl2 concentration. A significant storage-related increase in acidity and NPN was recorded for all cheese samples.