Mathematical and artificial neural network modeling for describing the infrared drying process of Moringa oleifera leaves and evaluation of product quality
| dc.contributor.author | Thuy, Nguyen Minh | |
| dc.contributor.author | Hao, Hong Van | |
| dc.contributor.author | Giau, Tran Ngoc | |
| dc.contributor.author | Minh, Vo Quang | |
| dc.contributor.author | Tai, Ngo Van | |
| dc.date.accessioned | 2026-08-06T10:51:59Z | |
| dc.date.available | 2026-08-06T10:51:59Z | |
| dc.date.issued | 2025-08-01 | |
| dc.description.abstract | Moringa oleifera leaves were used in the infrared drying method for powder production. The moisture ratio datasets during drying at different temperatures were fitted with eight thin-layer drying kinetics and analyzed by an artificial neural network (ANN). The goodness of fit was evaluated using the value of the coefficient of determination (R<sup>2</sup>), the chi-square (χ<sup>2</sup>), and the root mean square error (RMSE). Results indicated that drying time was between 40 and 95 min at a temperature of 55 to 70 °C. Among the mathematical drying models used, the Wang and Singh model best described the drying kinetics of Moringa leaves. But comparing with the ANN model—a machine learning-based model—it showed higher prediction capacity than the mathematical model did. For Moringa leaves dried at temperatures between 55 and 70 °C, the R<sup>2</sup>, χ<sup>2</sup>, and RMSE values for this model ranged from 97.85 to 99.59%, 0.0007 to 0.0029, and 0.0228 to 0.0503, respectively. Effective moisture diffusivity (D<inf>eff</inf>) values varied between 1.908 × 10<sup>−11</sup> and 3.875 × 10<sup>−11</sup> m<sup>2</sup>/s, with an activation energy of 43.92 kJ/mol. The drying temperature also influenced the bioactive compounds in Moringa leaves. The vibrant color of the powder was produced by drying Moringa leaves at 65 °C for 50 min. The powder had 5.85% moisture, 31.97% protein, 61.05 mg/100 g β-carotene, 62.82 mg QE/g total flavonoid content, and 1789.65 mg/100 g calcium content. | |
| dc.identifier.citation | Biomass Conversion and Biorefinery, 15(16), 23199-23209, 2025 | |
| dc.identifier.doi | 10.1007/s13399-025-06731-1 | |
| dc.identifier.issn | 21906815 | |
| dc.identifier.other | 2-s2.0-105000039011 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/17184 | |
| dc.source | Biomass Conversion and Biorefinery | |
| dc.subject | Activation energy | |
| dc.subject | Diffusivity | |
| dc.subject | Drying kinetics | |
| dc.subject | Modeling | |
| dc.subject | Moringa leaves | |
| dc.subject | Quality | |
| dc.title | Mathematical and artificial neural network modeling for describing the infrared drying process of Moringa oleifera leaves and evaluation of product quality | |
| dc.type | Article |
