Prediction of the Phytochemical Properties of Luffa Cylindrica Seed Oil Using Artificial Neural Network

The research used an artificial neural network (ANN) to examine optimum extraction conditions and phytochemical contents of Luffa cylindrica seed oil. The oil yield was predicted using an artificial neural network. The performance of the ANN and response surface methodology models was compared. The optimum extraction yielded 7.567% oil yield, 185.676 mg/l phenol, and 45.087 mg/l terpineol at 75.57 °C extraction temperature, 5.77 h extraction time, and 10.68 g/mol n-hexane concentration, respectively. These data show that the oil output is poor but has a significant phenol and terpenoid content that may be employed in pharmaceutical sectors. The FT-IR analysis of Luffa cylindrica seed oil revealed a high level of unsaturated hydrocarbons and esters, making the oil appropriate for using in the paint industry and creating cosmetics

Biosorption of Chromium (II) Ion from Textile Effluent Using Watermelon Shell-Activated Carbon

Watermelon Shell, an agricultural waste, was employed for the adsorptive removal of chromium (II) ion Cr2+ from textile effluent. This study analysed the adsorbent's active sites and morphological structures using FT-IR, SEM, XRD, and XRF. The independent variables' effect, contact time, adsorbent dosage, and pH were predicted using Response Surface Methodology (RSM) for chromium adsorption onto Watermelon Shell Activated Carbon (WSAC). The experimental results indicated that NaOH activation effectively improved WSAC's adsorption capacity. The maximum adsorption capacity was 54.53 %, with an adsorbent dosage of 0.6 g/l, pH of 6.0, and agitation time of 40 min. The high correlation coefficient (R2=0.978) between the model and the experimental data showed that the model predicted the removal of Cr2+ from textile effluent using Watermelon Shell Activated Carbon efficiently

Synthetic Modification of Sunflower Oil

This report is based on the synthesis of thermoset resins from sunflower oil. Sunflower oil with an iodine value of 120 g I / 100 g oil containing 30 % oleic acid and 59 % linoleic acid was epoxidized by reaction with a peroxy acid (formed in-situ by the reaction between hydrogen peroxide and formic acid). The ratio of ethylenic unsaturation to hydrogen peroxide to formic acid used was 1:1.5:0.5. The maximum conversion of iodine generated was 82.45 % for seven h of epoxidation at 65 °C, and the oxirane oxygen content at that same condition was 4.6 %. Thermoset resins synthesized from sunflower oil were further modified using acrylic acid. All the resins generated were characterized using FT-IR spectroscopy. The results showed that the generated resins could be used in composite production for automobile, construction, and furniture applications

Prediction of the Phytochemical Properties of Luffa Cylindrica Seed Oil Using Artificial Neural Network

The research used an artificial neural network (ANN) to examine optimum extraction conditions and phytochemical contents of Luffa cylindrica seed oil. The oil yield was predicted using an artificial neural network. The performance of the ANN and response surface methodology models was compared. The optimum extraction yielded 7.567% oil yield, 185.676 mg/l phenol, and 45.087 mg/l terpineol at 75.57 °C extraction temperature, 5.77 h extraction time, and 10.68 g/mol n-hexane concentration, respectively. These data show that the oil output is poor but has a significant phenol and terpenoid content that may be employed in pharmaceutical sectors. The FT-IR analysis of Luffa cylindrica seed oil revealed a high level of unsaturated hydrocarbons and esters, making the oil appropriate for using in the paint industry and creating cosmetics