Polyphenol oxidase (PPO) catalyses the browning reaction during fruit processing and storage. It is considered a threat to clean labels and minimally processed fruit products. Unwanted changes in fruits’ appearance and quality represent a cost to the industry. High pressure and ultrasound, in addition to thermal treatment, are effective in reducing PPO activity and producing high-quality products. PPO from different fruit cultivars behaves differently when submitted to different treatments. A systematic review was conducted, where treatment parameters, PPO inactivation data (≥80% inactivation), and kinetic inactivation parameters (rate constant (k), activation energy (Ea), D-value, and z-value) by different treatments were collected. Additionally, the estimated energy requirements for the inactivation of PPO (≥80%) by different treatments were calculated and compared. Resistance to various treatments varies between fruit cultivars. For the same temperature, the inactivation of PPO by ultrasound combined with heat is more effective than thermal treatment alone, and the high pressure combined thermal process. The majority of the thermal, HPP, and ultrasound inactivation of PPO in fruits followed first-order behaviour. Some fruit cultivars, however, showed biphasic inactivation behaviour. The estimated specific energy requirements calculated based on the mass of processed fruit sample to inactivate ≥80% polyphenol oxidase for the thermal process was 87 to 255 kJ/kg, while for high pressure processing it was 139 to 269 kJ/kg and for ultrasound it was 780 to 10,814 kJ/kg.

A conceptual model was developed to relate oral processing parameters and aroma release of cooked white rice. The conceptual model indicates that aroma release is dependent on the increase of particle surface area, the dilution effect of saliva and the diffusion of aroma from food residues that can be trapped in the buccal-pouches in the mouth. The model was validated against in vivo retro-nasal aroma release data during the consumption of rice flavoured with two aroma compounds (2-nonanone and ethyl propanoate) by five panellists. The oral processing behaviour of each subject was characterised at four different stages during oral processing by measuring bolus particle size, saliva content and the amount of residue particles that could be washed from the mouth after bolus expectoration. The results showed that aroma release for all subjects were dependent on the particle breakdown pathways used in oral processing. Subjects who reduced the rice to smaller particle sizes, higher pasted fraction and higher bolus residues had higher aroma release as expected from the conceptual model. Accounting for the physiological variables of subjects, the physicochemical parameters of aroma compounds and using a larger number of subjects in future studies will improve the model reliability.

Spray drying techniques are one of the methods to preserve and extend the shelf-life of coconut milk. The objective of this research was to create a particle swarm optimization–enhanced artificial neural network (PSO–ANN) that could predict the coconut milk spray drying process. The parameters for PSO tuning were selected as the number of particles and acceleration constant, respectively, for both global and personal best using a 2k factorial design. The optimal PSO settings were recorded as global best, C1 = 4.0; personal best, C2 = 0; and number of particles = 100. When comparing different types of spray drying models, PSO–ANN had an MSE value of 0.077, GA–ANN had an MSE of 0.033, while ANN had an MSE of 0.082. Sensitivity analysis was conducted on all three models to evaluate the significance level of each parameter on the model, and it was discovered that inlet temperature had the most significant influence on the model performance. In conclusion, the PSO–ANN was found to be more effective than ANN but less effective than GA–ANN in predicting the quality of coconut milk powder.