The requirement for easily adoptable technology for fruit preservation in developing countries is paramount. This study investigated the effect of pre-treatment (warm water blanching time—3, 5 and 10 min at 60 °C) and drying temperature (50, 60 and 70 °C) on drying mechanisms of convectively dried Synsepalum dulcificum (miracle berry fruit—MBF) fruit. Refined Adaptive Neuro Fuzzy Inference System (ANFIS) was utilized to model the effect and establish the sensitivity of drying factors on the moisture ratio variability of MBF. Unblanched MBF had the longest drying time, lowest effective moisture diffusivity (EMD), highest total and specific energy consumption of 530 min, 5.1052 E−09 m²/s, 22.73 kWh and 113.64 kWh/kg, respectively at 50 °C drying time, with lowest activation energy of 28.8589 kJ/mol. The 3 min blanched MBF had the lowest drying time, highest EMD, lowest total and specific energy consumption of 130 min, 2.5607 E−08 m²/s, 7.47 kWh and 37 kWh/kg, respectively at 70 °C drying temperature. The 5 min blanched MBF had the highest activation energy of 37.4808 kJ/mol. Amongst others, 3—gbellmf—38 epoch ANFIS structure had the highest modeling and prediction efficiency (R² = 0.9931). The moisture ratio variability was most sensitive to drying time at individual factor level, and drying time cum pretreatment at interactive factors level. In conclusion, pretreatment significantly reduced the drying time and energy consumption of MBF. Refined ANFIS structure modeled and predicted the drying process efficiently, and drying time contributed most significantly to the moisture ratio variability of MBF.

The conventional method of mechanism control which requires physical contact is being replaced with remote control, especially with the advent of the Internet of things (IoT). Facial recognition is used to identify and authenticate faces from images or videos. It has many applications, including granting or restricting access to a facility, or secured areas, or preventing unauthorized users, including selective access. The accuracy of such a recognition and control system is very important and depends on how the system was modeled and adopted. In this study, facial images and gesture images were collated and modeled in a python neural network, then optimized using TensorFlow. The algorithm was compiled unto a raspberry pi for testing on a developed automatic gate. An effective method of achieving accuracy using the epoch is presented in this work. Five (0, 5, 10, 15, 20) batch number and epoch respectively were modeled to achieve accuracy for the system. The model was trained with five gestures; fist, palm, thumb up, thumb, and last finger. The gesture recognition accuracy achieved through the epoch was maximum at 99.97 when both batch number and epoch were set to 20. However, when no epoch was set, the accuracy was below 10.2, whereas there was no accuracy below 98% when the epoch was introduced. This depicts the importance of epoch in achieving accuracy in image recognition. It was also discovered that the higher the epoch and the batch number the greater the accuracy, but the processing would require a very high processing unit.

The feasibility of recovering bioactive antioxidants from Phyllantus emblica leaves using ethanol -water mixture (0 – 100%) and heat-assisted extraction technology (HAE-T) was investigated in this present study. To this effect, the process parameter of operating temperature (OT: 30 -50 oC), solid -to-liquid ratio (S:L: 1:20 - 1:60 g/mL) and extraction time (ET: 45 - 180 min) were investigated on the extract total phenolic content (TPC, mg GAE/g d.w), yield (EY, %) and antioxidant activity (μM AAE/g d.w) by employing the Box-Behnken experimental design (BBD) available in response surface methodology (RSM). Multi-objective process optimization using the desirability function algorithm to determine set of process variables that simultaneously optimize process responses of TPC, EY and AA was also investigated. The recovery process was thereafter modeled with BBD-RSM and multi gene genetic programming (MGGP) algorithm and model reliability assessment via Monte Carlo simulation were conducted for the best performing set of models for predicting TPC, EY and AA values. The HPLC characterization of the recovered extract was carried out to ascertain the phenolic compound constituents of the Phyllantus emblica leaf extract. The 50% ethanol solution was found to be the best for the optimal extraction of antioxidants from Phyllantus emblica leaves. The HAE-T was suitable for recovering bioactive extract from Phyllantus emblica leaves and investigated process parameters have impacts on the recovery process. The process parameters that simultaneously gave the optimum EY of 21.6565%, TPC of 67.116 mg GAE/g and AA of 3.68583 µM AAE/g were OT of 41.61 oC, S:L of 1:60 g/mL and ET of 180 min. The HPLC extract profiling revealed that the bioactive constituents present in the recovered extract were betulinic acid, gallic acid, chlorogenic acid, caffeic acid, ellagic acid, and ferulic acid. Both the developed BBD-RSM and evolved MGGP-based models predicted the observed process response of TPC, EY and AA satisfactorily with BBD-RSM models marginally better in their predictions. The reliability results showed that the BBD-RSM models predicted TPC, EY and AA values with high certainty of 99.985%, 97.569% and 98.661%, respectively.

The viscoelastic property of packaging material affects foods’ postharvest management and biophysical quality preservation of food. This study investigated the creep viscoelastic property of neat polyvinyl alcohol film and polyvinyl alcohol film reinforced Momordica augustisepala nanofiber composite. The applicability and comparison of Burger phenomenological and Adaptive neuron model to analyze the creep data were investigated. The instantaneous modulus and creeping speed of neat and reinforced polyvinyl alcohol were 7.53710 MPa, 10.27580 MPa and 3.00590 MPa.s, 2.85391 MPa.s, respectively. The coefficient of determination of Burger model prediction was 0.99889 and 0.99972 for neat and composite polyvinyl alcohol film, while the coefficient of determination of Adaptive neuron model was 0.99986 for both neat and reinforced polyvinyl alcohol film. The Momordica augustisepala nanofiber improved the creep viscoelastic property of polyvinyl alcohol film and Adaptive neuron model showed superiority over Burger model in prediction of experimental data. The results are vita in material design, life prediction and usage prescription.