In the present study, influence of two types packaging (inflated polypropylene (IPP) and laminated aluminium foil (ALU)) and storage conditions (with/without 1 g of silica desiccant packets (SDPs)) on quality and shelf life of chewy santol candies were studied. After storage at 25 degrees Celsius for 30 days, it was found that the combination of ALU with SDPs presented the best treatment to maintain the quality of colour, water activity (aw), moisture content, total acidity, pH value, sensory evaluation i.e. (colour, odour, flavour and overall acceptance). Shelf-life prediction by using accelerated Q10 method based on moisture factors as an indicator of deterioration of the samples during storage. The samples were incubated at 25, 35 and 45°C and sampled every 5 days for estimated on physical, chemical quality and microbiological change. The predicted shelf life of chewy santol candies at 25°C of IPP and ALU packaging with SDPs using Q10 method were 25 and 27 days, respectively. However, the IPP and ALU packaging without SDPs, the products can be stored less than 25 days. SDPs provides a cheaper and easy method to keeping quality of the chewy santol candies. Therefore, the products with SDPs packaging can maintain the product quality during storage and has an acceptable quality to consumers.

The color, texture and rancidity of dried fruit are critical parameters to control for consumer acceptance. The goal of this research was to investigate the kinetics of color parameter changes and texture in terms of the crispness and peroxide value (PV) of dried coconut chips by using zero-, first- and second-order kinetic reactions during storage at different temperatures, as well as shelf life prediction using the accelerated method. The outcomes demonstrated that the zero-order kinetic reaction was appropriate to describe the change in color, crispness and PV of dried coconut chips during storage (R2 = 0.9690–0.9899). The rancidity had a higher correlation than the texture and color changes used to assign the quality essence. The activation energy (Ea) for the PV change was 11.83 kJ/mol. Therefore, the shelf life expectancy of the dried coconut chips was estimated to be 144, 128 and 115 days at 35, 45 and 55 ∘C, respectively. Meanwhile, the shelf life of products stored at ambient temperature was 159 days, and those products were stored in the refrigerator for 194 days. The findings provide retailers and consumers the ability to choose the ideal temperature and storage time for dried coconut chips in order to ensure the product’s quality.

The optimal process conditions when examining the antioxidant potential, total polyphenol content, and attribute liking in roasted rice germ flavored herbal tea were investigated using response surface methodology (RSM). The influence upon the extraction process of time and temperature was assessed using a full factorial design on three levels with two variables (32), involving five central point replicates. Extraction temperature (70 °C, 80 °C, and 90 °C) and extraction time (3 min, 4.5 min, and 6 min) served as independent variables, while the dependent variables were allocated to the regression equation to determine antioxidant activity (R2 = 0.941) along with total polyphenol content (R2 = 0.849), flavor liking score (R2 = 0.758), and overall liking score (R2 = 0.816). Following experimentation, it was determined that the optimal time and temperature conditions to maximize total polyphenol content, antioxidant activity, flavor, and overall liking score were in a range of 86 °C to 90 °C for 3.4 min to 5.9 min. When these conditions were imposed, the antioxidant potential, total polyphenol content, flavor, and overall liking score were >70% for DPPH scavenging activity, >75 mgGAE/g, >6.7 (like moderately), and >6.5 (like moderately), respectively.

The objectives of this research are to generate models that can effectively forecast the supply and demand of four cassava products. The appropriate forecasting models for cassava production volume is Back Propagation Neural Network (BPN) 4-14-1, cassava starch is BPN 7-12-1, cassava chip is BPN 7-14-1, cassava pellets is Multiple Linear Regression (MLP), and sago is BPN 7-13-1. Then, Linear Programming is used to calculate the optimization of cassava products to obtain the maximum profit and for cassava plant areas to obtain the maximum yield per area. The benefits of this research can support management planning for farmers and manufacturers.