Machine learning and experimental design for optimizing nitrogen-rich extract from cassava leaves via liquid hot water extraction
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Abstract
Cassava leaves are a significant source of nitrogen; however, the severity of the physicochemical extraction processes negatively affects nitrogen release. The objective of this study was to enhance nitrogen-rich extract recovery from cassava leaves through a comparative analysis of various experimental designs and machine learning (ML) techniques. Using the Plackett–Burman design, central composite design, and response surface methodology, the optimal extraction conditions were established: 20 min extraction time, 40% solid loading, and 150 mL extraction volume. The predicted amino nitrogen content reached 209 mg of N, showing a 6% deviation from the experimentally measured value. ML models—specifically, the support vector machine with a radial basis function kernel and random forest (RF)—were subsequently employed to refine the extraction conditions. The RF model showed a 6.6% deviation from the actual value, while both models identified the positive impact of increased solid loading on the total nitrogen recovery. These findings suggest that ML approaches offer promising potential for maximizing the amino nitrogen yield from cassava leaves.
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